From I to We: Group Formation and Linguistic Adaption in an Online Xenophobic Forum

From I to We: Group Formation and Linguistic Adaption in an Online Xenophobic Forum

Authors

Venue: Journal of Social and Political Psychology

Quick takeaway:

  • Linguistic study of a xenophobic online chat room using Pennebaker’s LIWC text analytic system. Users who stay in the group change from individual to group pronouns and align linguistically. Cognitive complexity also appears to reduce as users align with the group

Abstract:

  • Much of identity formation processes nowadays takes place online, indicating that intergroup differentiation may be found in online communities. This paper focuses on identity formation processes in an open online xenophobic, anti-immigrant, discussion forum. Open discussion forums provide an excellent opportunity to investigate open interactions that may reveal how identity is formed and how individual users are influenced by other users. Using computational text analysis and Linguistic Inquiry Word Count (LIWC), our results show that new users change from an individual identification to a group identification over time as indicated by a decrease in the use of “I” and increase in the use of “we”. The analyses also show increased use of “they” indicating intergroup differentiation. Moreover, the linguistic style of new users became more similar to that of the overall forum over time. Further, the emotional content decreased over time. The results indicate that new users on a forum create a collective identity with the other users and adapt to them linguistically.

Notes:

  • Social influence is broadly defined as any change – emotional, behavioral, or attitudinal – that has its roots in others’ real or imagined presence (Allport, 1954). (pg 77)
  • Regardless of why an individual displays an observable behavioral change that is in line with group norms, social identification with a group is the basis for the change. (pg 77)
  • In social psychological terms, a group is defined as more than two people that share certain goals (Cartwright & Zander, 1968). (pg 77)
  • Processes of social identification, intergroup differentiation and social influence have to date not been studied in online forums. The aim of the present research is to fill this gap and provide information on how such processes can be studied through language used on the forum. (pg 78)
  • The popularity of social networking sites has increased immensely during the last decade. At the same time, offline socializing has shown a decline (Duggan & Smith, 2013). Now, much of the socializing actually takes place online (Ganda, 2014). In order to be part of an online community, the individual must socialize with other users. Through such socializing, individuals create self-representations (Enli & Thumim, 2012). Hence, the processes of identity formation, may to a large extent take place on the Internet in various online forums. (pg 78)
  • For instance, linguistic analyses of American Nazis have shown that use of third person plural pronouns (they, them, their) is the single best predictor of extreme attitudes (Pennebaker & Chung, 2008). (pg 79)
  • Because language can be seen as behavior (Fiedler, 2008), it may be possible to study processes of social influence through linguistic analysis. Thus, our second hypothesis is that the linguistic style of new users will become increasingly similar to the linguistic style of the overall forum over time (H2). (pg 79)
  • This indicates that the content of the posts in an online forum may also change over time as arguments become more fine-tuned and input from both supporting and contradicting members are integrated into an individual’s own beliefs. This is likely to result (linguistically) in an increase in indicators of cognitive complexity. Hence, we hypothesize that the content of the posts will change over time, such that indicators of complex thinking will increase (H3a). (pg 80)
    • I’m not sure what to think about this. I expect from dimension reduction, that as the group becomes more aligned, the overall complex thinking will reduce, and the outliers will leave, at least in the extreme of a stampede condition.
  • This result indicates that after having expressed negativity in the forum, the need for such expressions should decrease. Hence, we expect that the content of the posts will change such that indicators of negative emotions will decrease, over time (H3b). (pg 80)
  • the forum is presented as a “very liberal forum”, where people are able to express their opinions, whatever they may be. This “extreme liberal” idea implies that there is very little censorship the forum is presented as a “very liberal forum”, where people are able to express their opinions, whatever they may be. This “extreme liberal” idea implies that there is very little censorship, which has resulted in that the forum is highly xenophobic. Nonetheless, due to its liberal self-presentation, the xenophobic discussions are not unchallenged. For example, also anti-racist people join this forum in order to challenge individuals with xenophobic attitudes. This means that the forum is not likely to function as a pure echo chamber, because contradicting arguments must be met with own arguments. Hence, individuals will learn from more experienced users how to counter contradicting arguments in a convincing way. Hence, they are likely to incorporate new knowledge, embrace input and contribute to evolving ideas and arguments. (pg 81)
    • Open debate can lead to the highest level of polarization (M&D)
    • There isn’t diverse opinion. The conversation is polarized, with opponents pushing towards the opposite pole. The question I’d like to see answered is has extremism increased in the forum?
  • Natural language analyses of anonymous social media forums also circumvent social desirability biases that may be present in traditional self-rating research, which is a particular important concern in relation to issues related to outgroups (Maass, Salvi, Arcuri, & Semin, 1989; von Hippel, Sekaquaptewa, & Vargas, 1997, 2008). The to-be analyzed media uses “aliases”, yielding anonymity of the users and at the same time allow us to track individuals over time and analyze changes in communication patterns. (pg 81)
    • After seeing “Ready Player One”, I also wonder if the aliases themselves could be looked at using an embedding space built from the terms used by the users? Then you get distance measurements, t-sne projections, etc.
  • Linguistic Inquiry Word Count (LIWC; Pennebaker et al., 2007; Chung & Pennebaker, 2007; Pennebaker, 2011b; Pennebaker, Francis, & Booth, 2001) is a computerized text analysis program that computes a LIWC score, i.e., the percentage of various language categories relative to the number of total words (see also www.liwc.net). (pg 81)
    • LIWC2015 ($90) is the gold standard in computerized text analysis. Learn how the words we use in everyday language reveal our thoughts, feelings, personality, and motivations. Based on years of scientific research, LIWC2015 is more accurate, easier to use, and provides a broader range of social and psychological insights compared to earlier LIWC versions
  • Figure 1c shows words overrepresented in later posts, i.e. words where the usage of the words correlates positively with how long the users has been active on the forum. The words here typically lack emotional content and are indicators of higher complexity in language. Again, this analysis provides preliminary support for the idea that time on the forum is related to more complex thinking, and less emotionality.
    • WordCloud
  • The second hypothesis was that the linguistic style of new users would become increasingly similar to other users on the forum over time. This hypothesis is evaluated by first z-transforming each LIWC score, so that each has a mean value of zero and a standard deviation of one. Then we measure how each post differs from the standardized values by summing the absolute z-values over all 62 LIWC categories from 2007. Thus, low values on these deviation scores indicate that posts are more prototypical, or highly similar, to what other users write. These deviation scores are analyzed in the same way as for Hypothesis 1 (i.e., by correlating each user score with the number of days on the forum, and then t-testing whether the correlations are significantly different from zero). In support of the hypothesis, the results show an increase in similarity, as indicated by decreasing deviation scores (Figure 2). The mean correlation coefficient between this measure and time on the forum was -.0086, which is significant, t(11749) = -3.77, p < 0.001. (pg 85)
    • ForumAlignmentI think it is reasonable to consider this a measure of alignment
  • Because individuals form identities online and because we see this in the use of pronouns, we also expected to see tendencies of social influence and adaption. This effect was also found, such that individuals’ linguistic style became increasingly similar to other users’ linguistic style over time. Past research has shown that accommodation of communication style occurs automatically when people connect to people or groups they like (Giles & Ogay 2007; Ireland et al., 2011), but also that similarity in communicative style functions as cohesive glue within a group (Reid, Giles, & Harwood, 2005). (pg 86)
  • Still, the results could not confirm an increase in cognitive complexity. It is difficult to determine why this was not observed even though a general trend to conform to the linguistic style on the forum was observed. (pg 87)
    • This is what I would expect. As alignment increases, complexity, as expressed by higher dimensional thinking should decrease.
  • This idea would also be in line with previous research that has shown that expressing oneself decreases arousal (Garcia et al., 2016). Moreover, because the forum is not explicitly racist, individuals may have simply adapted to the social norms on the forum prescribing less negative emotional displays. Finally, a possible explanation for the decrease in negative emotional words might be that users who are very angry leave the forum, because of its non-racist focus, and end up in more hostile forums. An interesting finding that was not part of the hypotheses in the present research is that the third person plural category correlated positively with all four negative emotions categories, suggesting that people using for example ‘they’ express more negative emotions (pg 87)
  • In line with social identity theory (Tajfel & Turner, 1986), we also observe linguistic adaption to the group. Hence, our results indicate that processes of identity formation may take place online. (pg 87)

Influence of augmented humans in online interactions during voting events

Influence of augmented humans in online interactions during voting events

  • Massimo Stella (Scholar)
  • Marco Cristoforetti (Scholar)
  • Marco Cristoforetti (Scholar)
  • Abstract: Overwhelming empirical evidence has shown that online social dynamics mirrors real-world events. Hence, understanding the mechanisms leading to social contagion in online ecosystems is fundamental for predicting, and even manouvering, human behavior. It has been shown that one of such mechanisms is based on fabricating armies of automated agents that are known as social bots. Using the recent Italian elections as an emblematic case study, here we provide evidence for the existence of a special class of highly influential users, that we name “augmented humans”. They exploit bots for enhancing both their visibility and influence, generating deep information cascades to the same extent of news media and other broadcasters. Augmented humans uniformly infiltrate across the full range of identified clusters of accounts, the latter reflecting political parties and their electoral ranks.
  • Bruter and Harrison [19] shift the focus on the psychological in uence that electoral arrangements exert on voters by altering their emotions and behavior. The investigation of voting from a cognitive perspective leads to the concept of electoral ergonomics: Understanding optimal ways in which voters emotionally cope with voting decisions and outcomes leads to a better prediction of the elections. (pg 1)
  • Most of the Twitter interactions are from humans to bots (46%); Humans tend to interact with bots in 56% of mentions, 41% of replies and 43% of retweets. Bots interact with humans roughly in 4% of the interactions, independently on interaction type. This indicates that bots play a passive role in the network but are rather highly mentioned/replied/retweeted by humans. (pg 2)
  • bots’ locations are distributed worldwide and they are present in areas where no human users are geo-localized such as Morocco.  (pg 2)
  • Since the number of social interactions (i.e., the degree) of a given user is an important estimator of the in uence of user itself in online social networks [1722], we consider a null model fixing users’ degree while randomizing their connections, also known as configuration model [2324].  (pg 2)
  • During the whole period, bot bot interactions are more likely than random (Δ > 0), indicating that bots tend to interact more with other bots rather than with humans (Δ < 0) during Italian elections. Since interactions often encode the spread of a given content online [16], the positive assortativity highlights that bots share contents mainly with each other and hence can resonate with the same content, be it news or spam.  (pg 2)
  • Differently from previous works, where the semantic content of bots and humans differs in its emotional polarity [12], in here we nd that bots mainly repeat the same political content of human users, thus boosting the spreading of hashtags strongly related to the electoral process, such as hashtags referring to the government or to political victory, names of political parties or names of influential politicians (see also 3). (pg 4)
  • Frequencies of individual hashtags during the whole electoral process display some interesting shifts, reported in Table III (Top). For instance, the hashtag #exitpoll, indicating the electoral outcome, becomes 10000 times more frequent on the voting day than before March 4. These shifts indicate that the frequency of hashtags reflects real-world events, thus underlining the strong link between online social dynamics and the real-world electoral process. (pg 4)
  • TABLE II. Top influencers are mostly bots. Hubs characterize influential users and broadcasters in online social systems [17], hence we use degree rankings for identifying the most in uential users in the network. (pg 5)
  • bots are mostly influential nodes which tend to interact mostly with other bots rather than humans and, when they interact with human users, they preferentially target the most influential ones. (pg 5)
  • we first filter the network by considering only pair of users with at least one retweet, with either direction, because re-sharing content it is often a good proxy of social endorsement [21]. However, Retweets alone are not sufficient to wash out the noise intrinsic to systems like Twitter, therefore we apply a more selective restriction, by requiring that at least another social action – i.e., either mention or reply – must be present in addition to a retweet [12]. This restrictive selection allows one to filter out all spurious interactions among users with the advantage of not requiring any thresholding approach with respect to the frequency of interactions themselves. (pg 5)
  • The resulting network is what we call the social bulk, i.e. a network core of endorsement and exchange among users. By construction, information ows among users who share strong social relationships and are characterized by similar ideologies: in fact, when a retweet goes from one user to another one, both of them are endorsing the same content, thus making non-directionality a viable approach for representing the endorsement related to content sharing. (pg 5)
  • Fiedler partitioning
  • The relevant literature has used the term “cyborg” for identifying indistinctly bot-assisted human or human-assisted bot accounts generating spam content over social platforms such as Twitter [5, 35]. Here, we prefer to use the term \augmented human” for indicating specifically those human accounts exploiting bots for artificially increasing, i.e. augmenting, their in uence in online social platforms, analogously to physical augmentation improving human performances in the real world [36]. (pg 8)
  • Baseline social behavior is defined by the medians of the two observables, like shown in Fig. 6c. This map allows to easily identify four categories of individuals in the social dynamics: i) hidden in uentials, generating information cascades rapidly spreading from a large small number of followers; ii) in uentials, generating information cascades rapidly spreading from a large number of followers; iii) broadcasters, generating information cascades slowly spreading from a large number of followers; iv) common users, generating information cascades slowly spreading from a small number of followers. (pg 9)
  • Hidden influentials, known to be efficient spreaders in viral phenomena [45], are mostly humans: in this category falls the augmented humans, assisted by social bots to increase their online visibility. (pg 10)
  • We define augmented humans as human users having at least 50% + 1 of bot neighbours in the social bulk. We discard users having less than 3 interactions in the social bulk. (pg 10)
  • The most central augmented human in terms of number of social interactions is Utente01, which interacts with 2700 bots and 55 humans in the social bulk. (pg 10)
  • The above cascade analysis reveals that almost 2 out 3 augmented humans resulted playing an important role in the flow of online content: 67% of augmented humans were either influentials or hidden influentials or broadcasters. These results strongly support the idea that via augmentation even common users can become social influencers without having a large number of followers/friends but rather by recurring to the aid of either armies of bots (e.g., Utente01, an hidden in uential) or the selection of a few key helping bots. (pg 11)

Consensus and Cooperation in Networked Multi-Agent Systems

Consensus and Cooperation in Networked Multi-Agent Systems

Journal: Proceedings of the IEEE

  • Proceedings of the IEEE is the leading journal to provide an in-depth review, survey, and tutorial coverage of the technical developments in electronics, electrical and computer engineering, and computer science. Consistently ranked as one of the top journals by Impact Factor, Article Influence Score and more, the journal serves as a trusted resource for engineers around the world

Authors:

Abstract:

  • This paper provides a theoretical framework for analysis of consensus algorithms for multi-agent networked systems with an emphasis on the role of directed information flow, robustness to changes in network topology due to link/node failures, time-delays, and performance guarantees. An overview of basic concepts of information consensus in networks and methods of convergence and performance analysis for the algorithms are provided. Our analysis framework is based on tools from matrix theory, algebraic graph theory, and control theory. We discuss the connections between consensus problems in networked dynamic systems and diverse applications including synchronization of coupled oscillators, flocking, formation control, fast consensus in small world networks, Markov processes and gossip-based algorithms, load balancing in networks, rendezvous in space, distributed sensor fusion in sensor networks, and belief propagation. We establish direct connections between spectral and structural properties of complex networks and the speed of information diffusion of consensus algorithms. A brief introduction is provided on networked systems with non-local information flow that are considerably faster than distributed systems with lattice-type nearest neighbor interactions. Simulation results are presented that demonstrate the role of small world effects on the speed of consensus algorithms and cooperative control of multi vehicle formations.

Notes:

  • In networks of agents (or dynamic systems), “consensus” means to reach an agreement regarding a certain quantity of interest that depends on the state of all agents. A “consensus algorithm” (or protocol) is an interaction rule that specifies the information exchange between an agent and all of its (nearest) neighbors on the network (pp 215)
    • In my work, this is agreement on heading and velocity
  • Graph Laplacians are an important point of focus of this paper. It is worth mentioning that the second smallest eigenvalue of graph Laplacians called algebraic connectivity quantifies the speed of convergence of consensus algorithms. (pp 216)
  • More recently, there has been a tremendous surge of interest among researchers from various disciplines of engineering and science in problems related to multi-agent networked systems with close ties to consensus problems. This includes subjects such as consensus [26]–[32], collective behavior of flocks and swarms [19], [33]–[37], sensor fusion [38]–[40], random networks [41], [42], synchronization of coupled oscillators [42]–[46], algebraic connectivity of complex networks [47]–[49], asynchronous distributed algorithms [30], [50], formation control for multi-robot systems [51]–[59], optimization-based cooperative control [60]–[63], dynamic graphs [64]–[67], complexity of coordinated tasks [68]–[71], and consensus-based belief propagation in Bayesian networks [72], [73]. (pp 216)
    • That is a dense lit review. How did they order it thematically?
  • A byproduct of this framework is to demonstrate that seemingly different consensus algorithms in the literature [10], [12]–[15] are closely related. (pp 216)
  • To understand the role of cooperation in performing coordinated tasks, we need to distinguish between unconstrained and constrained consensus problems. An unconstrained consensus problem is simply the alignment problem in which it suffices that the state of all agents asymptotically be the same. In contrast, in distributed computation of a function f(z), the state of all agents has to asymptotically become equal to f(z), meaning that the consensus problem is constrained. We refer to this constrained consensus problem as the f-consensus problem. (pp 217)
    • Normal exploring/flocking/stampeding is unconstrained. Herding adds constraint, though it’s dynamic. The variables that have to be manipulated in the case of constraint to result in the same amount of consensus are probably what’s interesting here. Examples could be how ‘loud’ does the herder have to be? Also, how ‘primed’ does the population have to be to accept herding?
  • …cooperation can be informally interpreted as “giving consent to providing one’s state and following a common protocol that serves the group objective.” (pp 217)
  • Formal analysis of the behavior of systems that involve more than one type of agent is more complicated, particularly, in presence of adversarial agents in noncooperative games [79], [80]. (pp 217)
  • The reason matrix theory [81] is so widely used in analysis of consensus algorithms [10], [12], [13], [14], [15], [64] is primarily due to the structure of P in (4) and its connection to graphs. (pp 218)
  • The role of consensus algorithms in particle based flocking is for an agent to achieve velocity matching with respect to its neighbors. In [19], it is demonstrated that flocks are networks of dynamic systems with a dynamic topology. This topology is a proximity graph that depends on the state of all agents and is determined locally for each agent, i.e., the topology of flocks is a state dependent graph. The notion of state-dependent graphs was introduced by Mesbahi [64] in a context that is independent of flocking. (pp 218)
    • They leave out heading alignment here. Deliberate? Or is heading alignment just another variant on velocity
  • Consider a network of decision-making agents with dynamics ẋi = ui interested in reaching a consensus via local communication with their neighbors on a graph G = (V, E). By reaching a consensus, we mean asymptotically converging to a one-dimensional agreement space characterized by the following equation: x1 = x2 = … = x (pp 219)
  • A dynamic graph G(t) = (V, E(t)) is a graph in which the set of edges E(t) and the adjacency matrix A(t) are time-varying. Clearly, the set of neighbors Ni(t) of every agent in a dynamic graph is a time-varying set as well. Dynamic graphs are useful for describing the network topology of mobile sensor networks and flocks [19]. (pp 219)
  • GraphLaplacianGradientDescent(pp 220)
  • algebraic connectivity of a graph: The algebraic connectivity (also known as Fiedler value or Fiedler eigenvalue) of a graph G is the second-smallest eigenvalue of the Laplacian matrix of G.[1] This eigenvalue is greater than 0 if and only if G is a connected graph. This is a corollary to the fact that the number of times 0 appears as an eigenvalue in the Laplacian is the number of connected components in the graph. The magnitude of this value reflects how well connected the overall graph is. It has been used in analysing the robustness and synchronizability of networks. (wikipedia) (pp 220)
  • According to Gershgorin theorem [81], all eigenvalues of L in the complex plane are located in a closed disk centered at delta + 0j with a radius of delta, the maximum degree of a graph (pp 220)
    • This is another measure that I can do of the nomad/flock/stampede structures combined with DBSCAN. Each agent knows what agents it is connected with, and we know how many agents there are. Each agent row should just have the number of agents it is connected to.
  • In many scenarios, networked systems can possess a dynamic topology that is time-varying due to node and link failures/creations, packet-loss [40], [98], asynchronous consensus [41], state-dependence [64], formation reconfiguration [53], evolution [96], and flocking [19], [99]. Networked systems with a dynamic topology are commonly known as switching networks. (pp 226)
  • Conclusion: A theoretical framework was provided for analysis of consensus algorithms for networked multi-agent systems with fixed or dynamic topology and directed information flow. The connections between consensus problems and several applications were discussed that include synchronization of coupled oscillators, flocking, formation control, fast consensus in small-world networks, Markov processes and gossip-based algorithms, load balancing in networks, rendezvous in space, distributed sensor fusion in sensor networks, and belief propagation. The role of “cooperation” in distributed coordination of networked autonomous systems was clarified and the effects of lack of cooperation was demonstrated by an example. It was demonstrated that notions such as graph Laplacians, nonnegative stochasticmatrices, and algebraic connectivity of graphs and digraphs play an instrumental role in analysis of consensus algorithms. We proved that algorithms introduced by Jadbabaie et al. and Fax and Murray are identical for graphs with n self-loops and are both special cases of the consensus algorithm of Olfati-Saber and Murray. The notion of Perron matrices was introduced as the discrete-time counterpart of graph Laplacians in consensus protocols. A number of fundamental spectral properties of Perron matrices were proved. This led to a unified framework for expression and analysis of consensus algorithms in both continuous-time and discrete-time. Simulation results for reaching a consensus in small-worlds versus lattice-type nearest-neighbor graphs and cooperative control of multivehicle formations were presented. (pp 231)

Schooling as a strategy for taxis in a noisy environment

Schooling as a strategy for taxis in a noisy environment

Journal: Evolutionary Ecology: Evolutionary Ecology is a conceptually oriented journal of basic biology at the interface of ecology and evolution. The journal publishes original research, reviews and discussion papers dealing with evolutionary ecology, including evolutionary aspects of behavioral and population ecology. The objective is to promote the conceptual, theoretical and empirical development of ecology and evolutionary biology; the scope extends to all organisms and systems. Research papers present the results of empirical and theoretical investigations, testing current theories in evolutionary ecology.

Author: Daniel Grunbaum: My research program seeks to establish quantitative relationships between short-term, small-scale processes, such as individual movement behaviors, and their long-term, large-scale population level effects, such as population fluxes and distributions.

Abstract

  • A common strategy to overcome this problem is taxis, a behaviour in which an animal performs a biased random walk by changing direction more rapidly when local conditions are getting worse.
    • Consider voters switching from Bush->Obama->Trump
  • Such an animal spends more time moving in right directions than wrong ones, and eventually gets to a favourable area. Taxis is ineffcient, however, when environmental gradients are weak or overlain by `noisy’ small-scale fluctuations. In this paper, I show that schooling behaviour can improve the ability of animals performing taxis to climb gradients, even under conditions when asocial taxis would be ineffective. Schooling is a social behaviour incorporating tendencies to remain close to and align with fellow members of a group. It enhances taxis because the alignment tendency produces tight angular distributions within groups, and dampens the stochastic effects of individual sampling errors. As a result, more school members orient up-gradient than in the comparable asocial case. However, overly strong schooling behaviour makes the school slow in responding to changing gradient directions. This trade-off suggests an optimal level of schooling behaviour for given spatio-temporal scales of environmental variations.
    • This has implications for everything from human social interaction to ANN design.

Notes

  • Because limiting resources typically have `patchy’ distributions in which concentrations may vary by orders of magnitude, success or failure in finding favourable areas often has an enormous impact on growth rates and reproductive success. To locate resource concentrations, many aquatic organisms display tactic behaviours, in which they orient with respect to local variations in chemical stimuli or other environmental properties. (pp 503)
  • Here, I propose that schooling behaviours improve the tactic capabilities of school members, and enable them to climb faint and noisy gradients which they would otherwise be unable to follow. (pp 504)
  • Schooling is thought to result from two principal behavioural components: (1) tendencies to move towards neighbours when isolated, and away from them when too close, so that the group retains a characteristic level of compactness; and (2) tendencies to align orientation with those of neighbours, so that nearby animals have similar directions of travel and the group as a whole exhibits a directional polarity. (pp 504)
    • My models indicate that attraction isn’t required, as long as there is a distance-graded awareness. In other words, you align most strongly with those agents that are closest.
  • I focus in this paper on schooling in aquatic animals, and particularly on phytoplankton as a distributed resource. However, although I do not examine them specifically, the modelling approaches and the basic results apply more generally to other environmental properties (such as temperature), to other causes of population movement (such as migration) and to other socially aggregating species which form polarized groups (such as flocks, herds and swarms). (pp 504)
  • Under these circumstances, the search of a nektonic filter-feeder for large-scale concentrations of phytoplankton is analogous to the behaviour of a bacterium performing chemotaxis. The essence of the analogy is that, while higher animals have much more sophisticated sensory and cognitive capacities, the scale at which they sample their environment is too small to identify accurately the true gradient. (pp 505)
    • And, I would contend for determining optimal social interactions in large groups.
  • Bacteria using chemotaxis usually do not directly sense the direction of the gradient. Instead, they perform random walks in which they change direction more often or by a greater amount if conditions are deteriorating than if they are improving (Keller and Segel, 1971; Alt, 1980; Tranquillo, 1990). Thus, on average, individuals spend more time moving in favourable directions than in unfavourable ones. (pp 505)
  • A bacterial analogy has been applied to a variety of behaviours in more complex organisms, such as spatially varying di€usion rates due to foraging behaviours or food-handling in copepods and larval ®sh (Davis et al., 1991), migration patterns in tuna (Mullen, 1989) and restricted area searching in ladybugs (Kareiva and Odell, 1987) and seabirds (Veit et al., 1993, 1995). The analogy provides for these higher animals a quantitative prediction of distribution patterns and abilities to locate resources at large space and time scales, based on measurable characteristics of small-scale movements. (pp 505)
  • I do not consider more sophisticated (and possibly more effective) social tactic algorithms, in which explicit information about the environment at remote points is actively or passively transmitted between individuals, or in which individual algorithms (such as slowing down when in relatively high concentrations) cause the group to function as a single sensing unit (Kils, 1986, described in Pitcher and Parrish, 1993). (pp 506)
    • This is something that could be easily added to the model. There could be a multiplier for each data cell that acts as a velocity scalar of the flock. That should have significant effects! This could also be applied to gradient descent. The flock of Gradient Descent Agents (GDAs) could have a higher speed across the fitness landscape, but slow and change direction when a better value is found by one of the GDAs. It occurs to me that this would work with a step function, as long as the baseline of the flock is sufficiently broad.
  • When the noise predominates (d <= 1), the angular distribution of individuals is nearly uniform, and the up-gradient velocity is near zero. In a range of intermediate values of d(0.3 <= d <= 3), there is measurable but slow movement up-gradient. The question I will address in the next two sections is: Can individuals in this intermediate signal-to-noise range with slow gradient-climbing rates improve their tactic ability by adopting a social behaviour (i.e. schooling)? (pp 508)
  • The key attributes of these models are: (1) a decreasing probability of detection or responsiveness to neighbours at large separation distances; (2) a social response that includes some sort of switch from attractive to repulsive interactions with neighbours, mediated by either separation distance or local density of animals*; and (3) a tendency to align with neighbours (Inagaki et al., 1976; Matuda and Sannomiya, 1980, 1985; Aoki, 1982; Huth and Wissel, 1990, 1992; Warburton and Lazarus, 1991; Grunbaum, 1994). (pp 508)
    • * Though not true of belief behavior (multiple individuals can share the same belief), for a Gradient Descent Agent (GDA), the idea of attraction/repulsion may be important.
  • If the number of neighbours is within an acceptable range, then the individual does not respond to them. On the other hand, if the number is outside that range, the individual turns by a small amount, Δθ3, to the left or right according to whether it has too many or too few of them and which side has more neighbours. In addition, at each time step, each individual randomly chooses one of its visible neighbours and turns by a small amount, Δθ4, towards that neighbour’s heading. (pp 508)
  • The results of simulations based on these rules show that schooling individuals, on average, move more directly in an up-gradient direction than asocial searchers with the same tactic parameters. Figure 4 shows the distribution of individuals in simulations of asocial and social taxis in a periodic domain (i.e. animals crossing the right boundary re-enter the left boundary, etc.). (pp 509)
  • Gradient Schooling
  • As predicted by Equation (5), asocial taxis results in a broad distribution of orientations, with a peak in the up-gradient (positive x-axis) direction but with a large fraction of individuals moving the wrong way at any given time (Fig. 5a,b). By comparison, schooling individuals tend to align with one another, forming a group with a tightened angular distribution. There is stochasticity in the average velocity of both asocial and social searchers (Fig. 5c). On average, however, schooling individuals move up-gradient faster and more directly than asocial ones. These simulation results demonstrate that it is theoretically possible to devise tactic search strategies utilizing social behaviours that are superior to asocial algorithms. That is, one of the advantages of schooling is that, potentially, it allows more successful search strategies under `noisy’ environmental conditions, where variations on the micro-scales at which animals sense their environment obscure the macro-scale gradients between ecologically favourable and unfavourable regions. (pp 510)
  • School-size effects must depend to some extent on the tactic and schooling algorithms, and the choices of parameters. However, underlying social taxis are the statistics of pooling outcomes of independent decisions, so the numerical dependence on school size may operate in a similar manner for many comparable behavioural schemes. For example, it seems reasonable to expect that, in many alternative schooling and tactic algorithms, decisions made collectively by less than 10 individuals would show some improvement over the asocial case but also retain much of the variability. Similarly, in most scenarios, group statistics probably vary only slowly with group size once it reaches sizes of 50-100. (pp 514)
  • when group size becomes large, the behaviour of model schools changes in character. With numerous individuals, stochasticity in the behaviour of each member has a relatively weaker effect on group motion. The behaviour of the group as a whole becomes more consistent and predictable, for longer time periods. (pp 514)
    • I think that this should be true in belief spaces as well. It may be difficult to track one person’s trajectory, but a group in aggregate, particularly a polarized group may be very detectable.
  • An example of group response to changing gradient direction shows that there can be a cost to strong alignment tendency. In this example, the gradient is initially pointed in the negative y-direction (Fig. 9). After an initial period of 5 time units, during which the gradient orients perpendicularly to the x-axis, the gradient reverts to the usual x-direction orientation. The school must then adjust to its new surroundings by shifting to climb the new gradient. This example shows that alignment works against course adjustment: the stronger the tendency to align, the slower is the group’s reorientation to the new gradient direction. This is apparently due to a non-linear interaction between alignment and taxis: asymmetries in the angular distribution during the transition create a net alignment flux away from the gradient direction. Thus, individuals that pay too much attention to neighbours, and allow alignment to overwhelm their tactic tendencies, may travel rapidly and persistently in the wrong direction. (pp 516)
    • So, if alignment (and velocity matching) are strong enough, the conditions for a stampede (group behavior with negative outcomes – in this case, less food) emerge
  • The models also suggest that there is a trade-off in strengthening tendencies to align with neighbours: strong alignment produces tight angular distributions, but increases the time needed to adjust course when the direction of the gradient changes. A reasonable balance seems to be achieved when individuals take roughly the same time to coalesce into a polarized group as they do to orient to the gradient in asocial taxis. (pp 518)
    • There is something about the relationship between explore and exploit in this statement that I really need to think about.
  • Social taxis is potentially effective in animals whose resources vary substantially over large length scales and for whom movements over these scales are possible. (pp 518)
    • Surviving as a social animal requires staying in the group. Since belief can cover wide ranges (e.g. religion), does there need to be a mechanism where individuals can harmonize their beliefs? From Social Norms and Other Minds The Evolutionary Roots of Higher Cognition :  Field research on primate societies in the wild and in captivity clearly shows that the capacity for (at least) implicit appreciation of permission, prohibition, and obligation social norms is directly related to survival rates and reproductive success. Without at least a rudimentary capacity to recognize and respond appropriately to these structures, remaining within a social group characterized by a dominance hierarchy would be all but impossible.
  • Interestingly, krill have been reported to school until a food patch has been discovered, whereupon they disperse to feed, consistent with a searching function for schooling. The apparent effectiveness of schooling as a strategy for taxis suggests that these schooling animals may be better able to climb obscure large-scale gradients than they would were they asocial. Interactive effects of taxis and sociality may affect the evolutionary value of larger groups both directly, by improving foraging ability with group size, and indirectly, by constraining alignment rates. (pp 518)
  • An example where sociality directly affects foraging strategy is forage area copying, in which unsuccessful fish move to the vicinity of neighbours that are observed to be foraging successfully (Pitcher et al., 1982; Ranta and Kaitala, 1991; Pitcher and Parrish, 1993). Pitcher and House (1987) interpreted area copying in goldfish as the result of a two-stage decision process: (1) a decision to stay put or move depending on whether feeding rate is high or low; and (2) a decision to join neighbours or not based upon whether or not further solitary searching is successful. Similar group dynamics have been observed in foraging seabirds (Porter and Seally, 1982; Haney et al., 1992).
  • Synchrokinesis depends upon the school having a relatively large spatial extent: part of a migrating school encounters an especially favourable or unfavourable area. The response of that section of the school is propagated throughout the school by alignment and grouping behaviours, with the result that the school as a whole is more effective at route-finding than isolated individuals. Forage area copying and synchrokinesis are distinct from social taxis in that an individual discovers and reacts to an environmental feature or resource, and fellow group members exploit that discovery. In social taxis, no individual need ever have greater knowledge about the environment than any other — social taxis is essentially bound up in the statistics of pooling the outcomes of many unreliable decisions. Synchrokinesis and social taxis are complementary mechanisms and may be expected to co-occur in migrating and gradient-climbing schools. (pp 519)
  • For example, in the comparisons of taxis among groups of various sizes, the most successful individuals were in the asocial simulation, even though as a fraction of the entire population they were vanishingly small. (pp 519)
    • Explorers have the highest payoff for the highest risks

Alignment in social interactions

Alignment in social interactions (2016)

Journal: Consciousness and Cognition, an International Journal, provides a forum for a natural science approach to the issues of consciousnessvoluntary control, and self. The journal features empirical research (in the form of articles) and theoretical reviews. The journal aims to be both scientifically rigorous and open to novel contributions.

Mattia Gallotti (Scholar):  Manager of The Human Mind Project at the School of Advanced Study of the University of London. I have a keen interest in academic management and governance, and I now consult on aspects of social innovation in the public sector.

Merle Theresa Fairhurst-MenuhinMerle is equally driven by a passion for art and science. Her days are split between work in cognitive neuroscience and exploring the rich repertoire of art song.

Chris Frith (Scholar):  I have been trying to delineate the mechanisms underlying the human ability to share representations of the world, for it is this ability that makes communication possible and allows us to achieve more than we could as individuals. We think that there are two major processes involved. The first is an automatic form of priming (sometimes referred to as contagion or empathy), whereby our representations of the world become aligned with those of the person with whom we are interacting. The second is a form of forward modelling, analogous to that used in the control of our own actions.

Abstract:

  • According to the prevailing paradigm in social-cognitive neuroscience, the mental states of individuals become shared when they adapt to each other in the pursuit of a shared goal. We challenge this view by proposing an alternative approach to the cognitive foundations of social interactions. The central claim of this paper is that social cognition concerns the graded and dynamic process of alignment of individual minds, even in the absence of a shared goal. When individuals reciprocally exchange information about each other’s minds processes of alignment unfold over time and across space, creating a social interaction. Not all cases of joint action involve such reciprocal exchange of information. To understand the nature of social interactions, then, we propose that attention should be focused on the manner in which people align words and thoughts, bodily postures and movements, in order to take one another into account and to make full use of socially relevant information.

Notes:

  • The concept of alignment has since evolved and is used to describe the multi-level, dynamic, and interactive mechanisms that underpin the sharing of people’s mental attitudes and representations in all kinds of social interactions (Dale, Fusaroli, & Duran, 2013). (pp 253)
  • The underlying justification for subsuming all these cases under the same mechanism is that cognition and action cannot be separated. The sharing of minds and bodies can then be conceptualized in terms of an integrated system of alignment, defined as the dynamic coupling of behavioural and/or cognitive states of two people (Dumas, Laroche, & Lehmann, 2014). (pp 253)
  • we are interested in the explanatory significance of alignment for a more general theory of social interaction, not in instrumental behaviour and/or alignment per se. (pp 254)
  • The central claim of this paper is that the alignment of minds, which emerges in social interactions, involves the reciprocal exchange of information whereby individuals adjust minds and bodies in a graded and dynamic manner. As these processes of alignment unfold, interacting partners will exchange information about each other’s minds and therefore act socially, whether or not a shared goal is in place. (pp 254)
  • In particular, in recent theoretical and empirical work on social cognition, reciprocity is increasingly recognized as a useful resource to capture the ‘‘jointness” of a joint action.Interpersonal understanding can be achieved by reading into one another’s mind reciprocally (Butterfill, 2013), and an explanation of the processes whereby the alignment of minds and bodies unfolds in space and time should involve an account of reciprocity (Zahavi & Rochat, 2015). In the process of a reciprocal exchange of information, individuals may adapt to varying degrees to one another. This is certainly the case in instances of temporal synchronisation and coordination in which physical alignment in time and space has been theorized to depend on cognitive models of adaptation (Elliott, Chua, & Wing, 2016Hayashi & Kondo, 2013Repp & Su, 2013) and thus on reciprocal interactions (D’Ausilio, Novembre, Fadiga, & Keller, 2015Keller, Novembre, & Hove, 2014Tognoli & Kelso, 2015). The behaviour of one player results in a change in behaviour of the other in a reciprocal way so as to achieve temporal synchrony. Interestingly, though not surprisingly, this reciprocal exchange of information results in physical alignment, which in turn has also been shown to result in greater degrees of affiliation and greater mental alignment (Hove & Risen, 2009Rabinowitch & Knafo-Noam, 2015Wiltermuth & Heath, 2009). Specifically, we suggest that, rather than a focus on the sharedness of the intended goal, we should attend to the graded exchange of information that creates alignment. The most social of interactions, in our formulation, are those in which ‘‘live” (‘‘online”, see Schilbach, 2014) information is exchanged dynamically (i.e. over time, across multiple points) bidirectionally and used to adapt behaviour and align with another (Jasmin et al., 2016). (pp 255)
  • Indeed, it is possible to have reciprocity and thus social interaction without cooperation. This would be the case, for example, in a competitive scenario in which the minds of the subjects are aligned at the appropriate level of description, and the sharing is essential to solve social dilemmas involving antagonistic behaviour (Bratman, 2014). In these exchanges, what is needed for the minds of the agents to attune to one another is that they adapt thoughts, bodily postures and movements, to take one another into account and reason as a team, even though the team might consist of competitive actors where none is aware that they are acting from the perspective of the same group and in the pursuit of some common goal (Bacharach, 2006). (pp 255)
  • fundamentally social nature has to do with the process whereby systems reciprocate thoughts and experiences, rather than with the endpoint i.e. the goal. It turns out that two features are often taken to be central to the process whereby interacting agents align minds and bodies. First, the interacting agents must be aware that they are doing something together with others. Second, the success of their joint performance is taken as a measure of how shared the participants’ goals are. (pp 255)
  • our suggestion is that what matters for the relevant alignment of minds and bodies to occur is the reciprocal exchange of information, not awareness of the reciprocal exchange of information. (pp 255)
    • This is all that is needed for flocking to happen. It is the range of that exchange that determines the phase change from independent to flock to stampede. Trust is involved in the reciprocity too, I think
  • Becoming mutually aware that we are sharing attitudes, dispositions, bodily postures, perhaps goals, does not mean that the ‘jointness’ of our actions has become available to each of us for conscious report. Reciprocity of awareness is emphatically not the same as awareness of reciprocity. The process of reciprocally exchanging information and mutually adapting to one another need not necessarily result in any degree of shared awareness. (pp 256)
  • In animals, a signal, for example about the source of food, that is too weak for an individual fish to follow can be followed by a group through the simple rules of bodily alignment that create shoaling behaviour (Grunbaum, 1998). Shoaling behaviour can also be observed in humans (Belz, Pyritz, & Boos, 2013), who can achieve group advantage through more complex forms of adjustment than just bodily alignment. Pairs of participants trying to detect a weak visual signal can achieve a greater group advantage when they align the terms they use to report their confidence in what they saw (Fusaroli et al., 2012). Indeed, linguistic alignment at many levels can be observed in dialogue (Pickering & Garrod, 2004) and can improve comprehension (Adank, Hagoort, & Bekkering, 2010; Fusaroli et al., 2012). (pp 256)
  • Much research has been driven, so far, by the implicit goal of identifying optimal group performance as a proxy for mental alignment (Fusaroli et al., 2012), however, there is conceptual room and empirical evidence for arguing that optimal task performance is not a good index of mental alignment or ‘optimal sociality’. In other words, taking achievement of a shared goal as the paradigm of a social interaction leads to the binary conception of sociality according to which an interaction is either (optimally) social, or it is not. (pp 256)
    • This is a problem that I have with opinion dynamics models. Convergence on a particular opinion isn’t the only issue. There is a dynamic process where opinions fall in and out of favor. This is the difference between the contagion model, which is one way (uninfected->infected) and motion through belief space. The goal really doesn’t matter, except in a subset of cases (Though these may be very important)
  • Two systems can interact when they have access to information relating to each other (Bilek et al., 2015). There are different ways of exchanging information between systems and hence different types of interaction (Liu & Pelowski, 2014), but in everycase some kind of alignment occurs (Coey, Varlet, & Richardson, 2012Huygens, 1673). (pp 257)
  • Such offline interaction can be contrasted with the case of online social interactions, where both participants act. The distinction between offline and online social interaction tasks is now acknowledged as crucial for advancing our understanding of the cognition processes underlying social interaction (Schilbach, 2014). (pp 257)
  • In contrast to salsa, consider the case of tango in which movements are improvised and as such require constant, mutual adaptation (Koehne et al., 2015; Tateo, 2014). Tango dancers have access to information relating to each other and, by virtue of the task, they exchange information with one another across time in a reciprocal and bidirectional fashion. The juxtaposition of tango with salsa highlights a spectrum of degrees of mutual reciprocity, with a richer form of interaction and greater need for alignment in tango compared with salsa.
  • we will take reciprocity to be the primary requirement for social interactions. We suggest that reciprocity can be identified with a special kind of alignment, mutual alignment, involving adjustment in both parties to the interaction. However, not all cases of joint action lead to mutual alignment. It is important to distinguish this mutual alignment from other types of alignment, which do not involve a reciprocal exchange of information between the agents. (pp 257)
  • In contrast to salsa, consider the case of tango in which movements are improvised and as such require constant, mutual adaptation (Koehne et al., 2015Tateo, 2014). Tango dancers have access to information relating to each other and, by virtue of the task, they exchange information with one another across time in a reciprocal and bidirectional fashion. The juxtaposition of tango with salsa highlights a spectrum of degrees of mutual reciprocity, with a richer form of interaction and greater need for alignment in tango compared with salsa. (pp 257)
  • AlignmentInSocialInteractions(pp 258)
  • The biggest challenge currently facing philosophers and scientists of social cognition is to understand social interactions. We suggest that this problem is best approached at the level of processes of mental alignment rather than through joint action tasks based on shared goals, and we propose that the key process is one of reciprocal, dynamic and graded adaptation between the participants in the interaction. Defining social interactions in terms of reciprocal patterns of alignment shows that not all joint actions involve reciprocity and also that social interactions can occur in the absence of shared goals. This approach has two particular advantages. First, it emphasises the key point that interactions can only be fully understood at the level of the group, rather than the individual. The pooling together of individual mental resources generates results that exceed the sum of the individual contributions. But, second, our approach points towards the mechanisms of adaptation that must be occurring within each individual in order to create the interaction (Friston & Frith, 2015). (pp 259)
  • This picture of social interaction in terms of mental alignment suggests two important theoretical developments. One is about a possible way to characterize the idea that types of social interaction lie on a continuum of possible solutions. If we focus on the task or the shared goal being pursued by agents jointly, as the current literature suggests, then only limited subdivisions of types of interaction will emerge. If, however, our focus extends so as to integrate the nature of the interaction, conceived of in terms of information exchange, then we can arrive at a higher degree of resolution of the space in which social interaction lie. This will define a spectrum of types of interaction (not just offline versus online social cognition), suggesting a dimensional rather than a discrete picture. After all, alignment comes in degrees and a spectrum-like definition of sociality implies that there is a variety of forms of alignment and hence of interactions. (pp 269)
    • My work would indicate that meaningful transitions occur for Unaligned (pure explore), Complex (flocking), and Total (stampede).

Speaker–listener neural coupling underlies successful communication

Speaker–listener neural coupling underlies successful communication (2010)

Greg J. Stephens

Lauren J. Silbert

Uri Hasson (HassonLab at Princeton)

Abstract:

  • Verbal communication is a joint activity; however, speech production and comprehension have primarily been analyzed as independent processes within the boundaries of individual brains. Here, we applied fMRI to record brain activity from both speakers and listeners during natural verbal communication. We used the speaker’s spatiotemporal brain activity to model listeners’ brain activity and found that the speaker’s activity is spatially and temporally coupled with the listener’s activity. This coupling vanishes when participants fail to communicate. Moreover, though on average the listener’s brain activity mirrors the speaker’s activity with a delay, we also find areas that exhibit predictive anticipatory responses. We connected the extent of neural coupling to a quantitative measure of story comprehension and find that the greater the anticipatory speaker–listener coupling, the greater the understanding. We argue that the observed alignment of production- and comprehension-based processes serves as a mechanism by which brains convey information.
    • This seems to be the root article for neural coupling. It seems to be an area of vigorous study, with lots of work coming out from the three authors.
    • The study design is also really good.

Notes

  • In this study we directly examine the spatial and temporal coupling between production and comprehension across brains during natural verbal communication. (pp 14425)
  • Using fMRI, we recorded the brain activity of a speaker telling an unrehearsed real-life story and the brain activity … (n = 11) of a listener listening to the recorded audio of the spoken story, thereby capturing the time-locked neural dynamics from both sides of the communication. Finally, we used a detailed questionnaire to assess the level of comprehension of each listener. (pp 14425)
  • …because communication unfolds over time, this coupling will exhibit important temporal structure. In particular, because the speaker’s production-based processes mostly precede the listener’s comprehension-based processes, the listener’s neural dynamics will mirror the speaker’s neural dynamics with some delay. Conversely, when listeners use their production system to emulate and predict the speaker’s utterances, we expect the opposite: the listener’s dynamics will precede the speaker’s dynamics. (pp 14425)
  • To analyze the direct interaction of production and comprehension mechanisms, we considered only spatially local models that measure the degree of speaker–listener coupling within the same Talairach location. (pp 14426)
  • we also observed significant speaker–listener coupling in a collection of extralinguistic areas known to be involved in the processing of semantic and social aspects of the story (19), including the precuneus, dorsolateral prefrontal cortex, orbitofrontal cortex, striatum, and medial prefrontal cortex. (pp 14426)
  • In agreement with previous work, the story evoked highly reliable activity inmany brain areas across all listeners (8, 11, 12) (Fig. 2B, yellow). We note that the agreement with previous work is far from assured: the story here was both personal and spontaneous, and was recorded in the noisy environment of the scanner. The similarity in the response patterns across all listeners underscores a strong tendency to process incoming verbal information in similar ways. A comparison between the speaker–listener and the listener–listenermaps reveals an extensive overlap (Fig. 2B, orange). These areas include many of the sensory related, classic linguistic-related and extralinguistic-related brain areas, demonstrating that many of the areas involved in speech comprehension (listener–listener coupling) are also aligned during communication (speaker–listener coupling). (pp 14426)
  • To test whether the extensive speaker–listener coupling emerges only when information is transferred across interlocutors, we blocked the communication between speaker and listener. We repeated the experiment while recording a Russian speaker telling a story in the scanner, and then played the story to non–Russian speaking listeners (n = 11). In this experimental setup, although the Russian speaker is trying to communicate information, the listeners are unable to extract the information from the incoming acoustic sounds. Using identical analysis methods and statistical thresholds, we found no significant coupling between the speaker and the listeners or among the listeners. At significantly lower thresholds we found that the non–Russian-speaking listener–listener coupling was confined to early auditory cortices. This indicates that the reliable activity in most areas, besides early auditory cortex, depends on a successful processing of the incoming information, and is not driven by the low-level acoustic aspects of the stimuli. (pp 14426)
  • Neural Coupling
    • In my model, the anticipation is modeled by the alignment and velocity, but others come to similar conclusions. It may be a way of dealing with noisy environments. Which would be another way of saying group dynamics with incomplete information.
  • Our analysis also identifies a subset of brain regions in which the activity in the listener’s brain precedes the activity in the speaker’s brain. The listener’s anticipatory responses were localized to areas known to be involved in predictions and value representation (pp 14428)
  • Such findings are in agreement with the theory of interactive linguistic alignment (1). According to this theory, production and comprehension become tightly aligned on many different levels during verbal communication, including the phonetic, phonological, lexical, syntactic, and semantic representationsAccordingly, we observed neural coupling during communication at many different processing levels, including low-level auditory areas (induced by the shared input), production-based areas (e.g., Broca’s area), comprehension based areas (e.g., Wernicke’s area and TPJ), and high-order extralinguistic areas (e.g., precuneus and mPFC) that can induce shared contextual model of the situation(34). Interestingly, some of these extralinguistic areas are known to be involved in processing social information crucial for successful communication, including, among others, the capacity to discern the beliefs, desires, and goals of others.(pp 14429)

 

Suppressing the Search Engine Manipulation Effect (SEME)

Suppressing the Search Engine Manipulation Effect (SEME)

  • Authors
    • Robert Epstein, (American Institute for Behavioral Research and Technology) Epstein and Robertson have found in multiple studies that search rankings that favor a political candidate drive the votes of undecided voters toward that candidate, an effect they call SEME (“seem”), the Search Engine Manipulation Effect.
    • Ronald Robertson (Northeastern University) I design experiments and technologies to explore the ways in which online platforms can influence the attitudes, beliefs, and behavior of individuals and groups. Currently, I am a PhD student in the world’s first Network Science PhD program at Northeastern University and am advised by Christo Wilson and David Lazer.
    • David Lazer (Northeastern University) professor of political science and computer and information science and the co-director of the NULab for Texts, Maps, and Networks
    • Christo Wilson (Northeastern University) Assistant Professor in the College of Computer and Information Science atNortheastern University. I am a member of the Cybersecurity and Privacy Institute and the Director of the BS in Cybersecurity Program in the College.

 

  • Abstract: A recent series of experiments demonstrated that introducing ranking bias to election-related search engine results can have a strong and undetectable influence on the preferences of undecided voters. This phenomenon, called the Search Engine Manipulation Effect (SEME), exerts influence largely through order effects that are enhanced in a digital context. We present data from three new experiments involving 3,600 subjects in 39 countries in which we replicate SEME and test design interventions for suppressing the effect. In the replication, voting preferences shifted by 39.0%, a number almost identical to the shift found in a previously published experiment (37.1%). Alerting users to the ranking bias reduced the shift to 22.1%, and more detailed alerts reduced it to 13.8%. Users’ browsing behaviors were also significantly altered by the alerts, with more clicks and time going to lower-ranked search results. Although bias alerts were effective in suppressing SEME, we found that SEME could be completely eliminated only by alternating search results – in effect, with an equal-time rule. We propose a browser extension capable of deploying bias alerts in real-time and speculate that SEME might be impacting a wide range of decision-making, not just voting, in which case search engines might need to be strictly regulated.
  • Introduction
    • Recent research has shown that society’s growing dependence on ranking algorithms leaves our psychological heuristics and vulnerabilities susceptible to their influence on an unprecedented scale and in unexpected ways
    • Experiments conducted on Facebook’s Newsfeed have demonstrated that subtle ranking manipulations can influence the emotional language people use
    • Similarly, experiments on web search have shown that manipulating election-related search engine rankings can shift the voting preferences of undecided voters by 20% or more after a single search
    • While “bias” can be ambiguous, our focus is on the ranking bias recently quantified by Kulshrestha et al. with Twitter rankings
    • Our results provide support for the robustness of SEME and create a foundation for future efforts to mitigate ranking bias. More broadly, our work adds to the growing literature that provides an empirical basis to calls for algorithm accountability and transparency [24, 25, 90, 91] and contributes a quantitative approach that complements the qualitative literature on designing interventions for ranking algorithms
    • Our results also suggest that proactive strategies that prevent ranking bias (e.g., alternating rankings) are more effective than reactive strategies that suppress the effect through design interventions like bias alerts. Given the accumulating evidence, we speculate that SEME may be impacting a wide range of decision-making, not just voting
  • Related Work
    • Order effects are among the strongest and most reliable effects ever discovered in the psychological sciences [29, 88]. These effects favorably affect the recall and evaluation of items at the beginning of a list (primacy) and at the end of a list (recency).
      • There does not seem to be an equivalent primacy effect in maps that I can find
    • online systems can: (1) provide a platform for constant, large-scale, rapid experimentation, (2) tailor their persuasive strategies by mining detailed demographic and behavioral profiles of users [1, 6, 9, 18, 121], and (3) provide users with a sense of control over the system that enhances their susceptibility to influence
      • Is this flocking from the flock’s perspective? Sort of an Ur-flock?
      • This is that Trust/Awareness equation again
    • A recent report involving 33,000 people found that search engines were the most trusted source of news, with 64% of people reporting that they trust search engines, compared to 57% for traditional media, 51% for online media, and 41% for social media [10]. Similarly, a 2012 survey by Pew found that 73% of search engine users report that “all or most of the information they find is accurate and trustworthy,” and 66% report that “search engines are a fair and unbiased source of information” [105].
    • Suggestions for fostering resistance can be broken down into two primary strategies: (1) providing forewarnings [43, 49] and (2) training and motivating people to resist [79, 120].
      • Interesting that alternate, non-ordered design approaches aren’t even mentioned
    • Part of the reason that forewarnings work is explained by psychological reactance theory [12], which posits that when people believe their intellectual freedom is threatened – by exposing an attempt to persuade, for example – they react in the direction opposite that of the intended one
    • In the context of online media bias, researchers have primarily explored methods for curbing the effects of algorithmic filtering and selective exposure [87, 96] rather than ranking bias [71]. In this vein, researchers have developed services that encourage users to explore multiple perspectives [97, 98] and browser extensions that gamify and encourage balanced political news consumption [19, 20, 86]. However, these solutions are somewhat impractical because they require users to adopt new services or exert additional effort.
  • Methods – Experiment Design
    • To construct biased search rankings we asked four independent raters to provide bias ratings of the webpages we collected on an 11-point Likert scale ranging from -5 “favors Cameron” to +5 “favors Miliband”. We then selected the 15 webpages that most strongly favored Cameron and the 15 that most strongly favored Miliband to create three bias groups
    • The query in the search engine was fixed as “UK Politics ‘David Cameron’ OR ‘Ed Miliband’”, and subjects could not reformulate it.
    • On top of assignment to a bias group, subjects were randomly assigned to one of three alert experiments.We drew from the literature on decision-making and design intervention to implement so-called debiasing strategies for improving decision-making in the presence of biased information [39, 78, 82]. Specifically, we constructed and placed alerts in the search results produced by our mock search engine that provided forewarnings with salient graphics, autonomony-supportive language, and details on the persuasive threat
  • Methods – Procedure
    • After providing informed consent and answering basic demographic questions
      • Do this and use this phrase!
    • Subjects then rated the two candidates on 10-point Likert scales with respect to their overall impression of each candidate, how much they trusted each candidate, and how much they liked each candidate. Subjects also indicated their likelihood of voting for one candidate or the other on an 11-point Likert scale where the candidates’ names appeared at opposite ends of the scale and 0 indicated no preference, as well as on a binary choice question where subjects indicated who they would vote for if the election were held today.
      • This is a good way to set up the game. People read the dilemma, formulate an initial solution and their level of commitment to it. They can choose to make it “public” as their first statement or to keep it private and display a “no opinion” initial statement
    • We asked: “While you were doing your online research on the candidates, did you notice anything about the search results that bothered you in any way?” and prompted subjects to explain what had bothered them in a free response format: “If you answered “yes,” please tell us what bothered you.” We did not directly ask subjects whether they had “noticed bias” to avoid the inflation of false positive rates that leading questions can cause
  • Methods – Participants
    • We recruited 3,883 subjects between April 28, 2015 and May 6, 2015 on Amazon’s Mechanical Turk (AMT; https://mturk.com), a subject pool frequently used by behavioral, economic, and social science researchers [8, 13, 102]. We excluded from our analysis subjects who reported an English fluency level of 5 or less (on a scale of 1 to 10) (n=26)
      • MTurk would be a good source of participants as well
  • Analysys – Search metrics
    • Utilizing Kolmogorov-Smirnov (K-S) tests of differences in distributions, we found significant differences in the patterns of time spent on the 30 webpages between subjects in the no alert experiment (correlation with ranking: Spearman’s ρ = -0.836, P <0.001) and the high alert experiment (ρ = -0.654, P <0.001) (K-S D = 0.467, P <0.01), and between subjects in the low alert experiment (ρ = -0.719, P <0.001) and the high alert experiment (K-S D = 0.400, P <0.01)
      • A way of looking for explore/exploit populations? And how fast can it be determined? Google uses a mechanism to stop an experiment once a confidence level is reached. Also, bootstrap would be good here
    • Similarly, we also found significant differences in the patterns of clicks that subjects made on the 30 webpages between subjects in the no alert experiment (ρ = -0.865, P <0.001) and the high alert experiment (ρ = -0.795, P <0.001) (K-S D = 0.500, P <0.001), and between subjects in the low alert experiment (ρ = -0.876, P <0.001) and the high alert experiment (K-S D = 0.367, P <0.05)
    • Among all conditions,we found that differences in the patterns of time and clicks on the individual rankings primarily emerged on the first SERP, but less so on the second, fourth, and fifth SERPs
  • Analysys – Attitude Shifts
    • we found that the mean shifts in candidate ratings for the bias groups significantly converged on the mean shift found in the neutral group as the level of detail in the alerts increased, with high alerts creating higher convergence than low alerts
      • As more diverse information is injected, populations compromise
  • Analysys – Vote Shifts
    • Vote Manipulation Power (VMP)is the percent change in the number of subjects, in the two bias groups combined, who indicated that they would vote for the candidate who was favored by their search rankings. That is, if x and x ′ subjects in the bias groups said they would vote for the favored candidate before and after conducting the search, respectively, then VMP = (x ′ − x)/x.
      • This could also be applied to the game to watch how votes for an outcome change over time. In the case of the game, new candidates can come into existence, so we need to watch for that.
  • Analysys – Bias Awareness
    • We found 8.1% of subjects that showed awareness of the bias in the no alert experiment, a figure identical to the 8.1% awareness rate found by Eslami et al. in their audit of Booking.com [37], and similar to the 8.6% of subjects who showed awareness in the original study [30]. The percentage of subjects showing bias awareness increased to 21.5% in the low alert experiment, and 23.4% in the high alert experiment.
  • Discussion
    • However, despite the additional suppression of the high alerts, the lowest VMP was found among the neutral group subjects: rankings alternating between favoring the two candidates prevented SEME.
      • This configuration forces users to “explore” more, within the context of a list affordance.
    • As with previous research on SEME [30], and with research on attitude change and influence more generally [3, 72, 120], we found that subjects vary in their susceptibility to SEME, as well as in their responsiveness to the alerts, based on their personal characteristics (Figure 6 and Figure 7 in the Appendix).
      • Explorer and exploiter populations?
    • As more people turn to the internet for political news [85, 115], designing systems that can monitor and suppress the effects of algorithm biases, like ranking bias, will play an increasingly important role in protecting the public’s psychological vulnerabilities.
      • And one of the big issues is finding bias at scale with domain independence
    • Real-time automated bias detection could potentially be achieved by utilizing a Natural Language Processing (NLP) approach. One could utilize opinions [75], sentiment [99], linguistic patterns [109], word associations [14], or recursive neural networks [59] with human-coded data to classify biased language.
      • Scale and domain problems.
  • Discussion – Awareness of bias
    • Awareness of ranking bias appears to suppress SEME only when it occurs in conjunction with a bias alert, perhaps because an alert is a kind of warning–inherently negative in nature.
      • According to Moscovici, an inherently negative construct should reduce polarization movement.
    • Awareness of ranking bias in the absence of bias alerts might increase VMP because people perceive the bias as a kind of social proof [111, 112], made all the more powerful because of the disproportionate trust people have in search rankings [10, 95, 105]. The user’s interpretation might be, “This candidate MUST be good, because even the search results say so.”