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Modeling Polarization Caused by Empathetic and Repulsive Reaction in Online Social Network
Abstract
While social media is now used by many people and plays a role in distributing information, it has recently created an unexpected problem: the actual shrinkage of information sources. This is mainly due to the ease of connecting people with similar opinions and the recommendation system. Biased information distribution promotes polarization that divides people into multiple groups with opposing views. Also, people may receive only the seemingly positive information that they prefer, or may trigger them into holding onto their opinions more strongly when they encounter opposing views. This, combined with the characteristics of social media, is accelerating the polarization of opinions and eventually social division. In this paper, we propose a model of opinion formation on social media to simulate polarization. While based on the idea that opinion neutrality is only relative, this model provides new techniques for dealing with polarization.
... We extend the interaction rules of our previously proposed one-dimensional opinion formation model [5] to multiple dimensions. In this one-dimensional model, periodic boundary conditions are imposed at both ends of the interval where opinion values can be taken, and two types of reactions, empathy and repulsion, are defined by the same form exponential functions. ...
... Another distance ∆ ′ j = 2a − ∆ j can be defined by considering periodic boundary conditions. The significant characteristic of this model is that the reaction of empathy and repulsion are described by the same framework by using the distance of vectors with periodic boundary conditions [5]. Given that smaller differences in opinion make it easier to establish empathy and that larger differences in opinion make repulsion more likely, the probabilities of empathy p e, j and repulsion p r, j in dimension j are defined as follows: ...
In this paper, we propose a multi-dimensional opinion formation model for online social networks. Previous studies have not taken into account the “curse of dimensionality” that comes from the higher-dimensional state space used in multi-dimensional opinion formation; the dimensionality is high as opinions with various types of preferences must be handled simultaneously. Conventionally, the interactions between users are limited to static models in which the topics that users discuss are predetermined and do not change. Our proposed method can avoid the above problems with previous studies by introducing low-dimensional subspaces for interacting user pairs by using word distributed representation. We propose a dynamic model that allows users to discuss various topics at each interaction. Simulations of the proposed model show that discussion focused on a particular topic encourages opinion formation.
The democratization of AI tools for content generation, combined with unrestricted access to mass media for all (e.g. through microblogging and social media), makes it increasingly hard for people to distinguish fact from fiction. This raises the question of how individual opinions evolve in such a networked environment without grounding in a known reality. The dominant approach to studying this problem uses simple models from the social sciences on how individuals change their opinions when exposed to their social neighborhood, and applies them on large social networks. We propose a novel model that incorporates two known social phenomena: (i) Biased Assimilation: the tendency of individuals to adopt other opinions if they are similar to their own; (ii) Backfire Effect: the fact that an opposite opinion may further entrench people in their stances, making their opinions more extreme instead of moderating them. To the best of our knowledge, this is the first DeGroot-type opinion formation model that captures the Backfire Effect. A thorough theoretical and empirical analysis of the proposed model reveals intuitive conditions for polarization and consensus to exist, as well as the properties of the resulting opinions.
In recent years, the ease with which social media can be accessed has led to the unexpected problem of a shrinkage in information sources. This phenomenon is caused by a system that facilitates the connection of people with similar ideas and recommendation systems. Bias in the selection of information sources promotes polarization that divides people into multiple groups with opposing views and creates conflicts between opposing groups. This paper elucidates the mechanism of polarization by proposing a model of opinion formation in social media that considers users' reactions of empathy and repulsion. Based on the idea that opinion neutrality is only relative, this model offers a novel technology for dealing with polarization.
Accurate modeling of opinion dynamics has the potential to help us understand polarization and what makes effective political discourse possible or impossible. Here, we use physics-based methods to model the evolution of political opinions within a continuously distributed population. We utilize a network-free system of determining political influence and a local-attraction, distal-repulsion dynamic for reaction to perceived content. Our approach allows for the incorporation of intergroup bias such that messages from trusted in-group sources enjoy greater leeway than out-group ones. We are able to extrapolate these nonlinear microscopic dynamics to macroscopic population distributions by using probabilistic functions representing biased environments. The framework we put forward can reproduce real-world political distributions and experimentally observed dynamics and is amenable to further refinement as more data becomes available.
In this paper, we make a bridge between two theories in social psychology, namely social judgment theory and balance theory, exploiting nonlinear models. In particular, we propose a model of opinion dynamics which takes into account the notions of latitude of acceptance, latitude of noncommitment, and latitude of rejection from social judgment theory. In the proposed model, each individual is considered as a node in a graph, and the influences are represented by edges. The proposed model considers positive (negative) edges for acceptance (rejection), while there is no connection for noncommitment. In addition, in the introduced nonlinear model, the weight of each edge, which represents the influence weight, changes depending on the opinions of the individuals in the network. Given the negative interactions, the influence graph of the network may be initially unbalanced; however, the proposed model presents that such graph seeks balance eventually. Thus, the model introduced in this paper provides a mathematical framework for balance theory introduced by Harary. Through simulations, we show that the proposed model can demonstrate consensus, bipartite consensus, and clustering of opinions.
Abstract Echo chambers in online social networks, in which users prefer to interact only with ideologically-aligned peers, are believed to facilitate misinformation spreading and contribute to radicalize political discourse. In this paper, we gauge the effects of echo chambers in information spreading phenomena over political communication networks. Mining 12 million Twitter messages, we reconstruct a network in which users interchange opinions related to the impeachment of the former Brazilian President Dilma Rousseff. We define a continuous political leaning parameter, independent of the network’s structure, that allows to quantify the presence of echo chambers in the strongly connected component of the network. These are reflected in two well-separated communities of similar sizes with opposite views of the impeachment process. By means of simple spreading models, we show that the capability of users in propagating the content they produce, measured by the associated spreading capacity, strongly depends on their attitude. Users expressing pro-impeachment leanings are capable to transmit information, on average, to a larger audience than users expressing anti-impeachment leanings. Furthermore, the users’ spreading capacity is correlated to the diversity, in terms of political position, of the audience reached. Our method can be exploited to identify the presence of echo chambers and their effects across different contexts and shed light upon the mechanisms allowing to break echo chambers.
Significance
Social media sites are often blamed for exacerbating political polarization by creating “echo chambers” that prevent people from being exposed to information that contradicts their preexisting beliefs. We conducted a field experiment that offered a large group of Democrats and Republicans financial compensation to follow bots that retweeted messages by elected officials and opinion leaders with opposing political views. Republican participants expressed substantially more conservative views after following a liberal Twitter bot, whereas Democrats’ attitudes became slightly more liberal after following a conservative Twitter bot—although this effect was not statistically significant. Despite several limitations, this study has important implications for the emerging field of computational social science and ongoing efforts to reduce political polarization online.
The aim of this paper is to modify continuous-time bounded confidence opinion dynamics models so that “changes of opinion” (intended as changes of the sign of the initial states) are never induced during the evolution. Such sign invariance can be achieved by letting opinions of different sign localized near the origin interact negatively, or neglect each other, or even repel each other. In all cases, it is possible to obtain sign-preserving bounded confidence models with state-dependent connectivity and with a clustering behavior similar to that of a standard bounded confidence model.
Recent years have witnessed a significant trend towards filling the gap between Social Network Analysis (SNA) and control theory. This trend was enabled by the introduction of new mathematical models describing dynamics of social groups, the development of algorithms and software for data analysis and the tremendous progress in understanding complex networks and multi-agent systems (MAS) dynamics. The aim of this tutorial is to highlight a novel chapter of control theory, dealing with dynamic models of social networks and processes over them, to the attention of the broad research community. In its first part [1], we have considered the most classical models of social dynamics, which have anticipated and to a great extent inspired the recent extensive studies on MAS and complex networks. This paper is the second part of the tutorial, and it is focused on more recent models of social processes that have been developed concurrently with MAS theory. Future perspectives of control in social and techno-social systems are also discussed.
This chapter provides an accessible introduction for point processes, and especially Hawkes processes, for modeling discrete, inter-dependent events over continuous time. We start by reviewing the definitions and the key concepts in point processes. We then introduce the Hawkes process, its event intensity function, as well as schemes for event simulation and parameter estimation. We also describe a practical example drawn from social media data - we show how to model retweet cascades using a Hawkes self-exciting process. We presents a design of the memory kernel, and results on estimating parameters and predicting popularity. The code and sample event data are available as an online appendix
Significance
Twitter and other social media platforms are believed to have altered the course of numerous historical events, from the Arab Spring to the US presidential election. Online social networks have become a ubiquitous medium for discussing moral and political ideas. Nevertheless, the field of moral psychology has yet to investigate why some moral and political ideas spread more widely than others. Using a large sample of social media communications concerning polarizing issues in public policy debates (gun control, same-sex marriage, climate change), we found that the presence of moral-emotional language in political messages substantially increases their diffusion within (and less so between) ideological group boundaries. These findings offer insights into how moral ideas spread within networks during real political discussion.
Online social networks (OSNs) produce a huge volume of content and clickstream data over time as a result of continuous social interactions between users. Because these social interactions are not fully observable, the mining of such social streams is more complex than traditional data streams. Stochastic point processes, as a promising approach, have recently received significant research attention in social network analysis, in attempts to discover latent network structure of online social networks and particularly understand human interactions and behavior within the social networks. The objective of this paper is to provide a tutorial to the point process framework and its implementation in social media analytics. It begins by providing a quick overview of the history of Hawkes point processes as the most widely used classes of point process models. We identify various capabilities and attributes of the Hawkes point processes and build a bridge between the theory and practice of point processes in social network analytics. Then the paper includes a brief description of some current research projects that demonstrate the potential of the proposed framework. We also conclude with a discussion of some research opportunities in online social network and clickstream point process data.
Confirmation bias, as the term is typically used in the psychological literature, connotes the seeking or interpreting of evidence in ways that are partial to existing beliefs, expectations, or a hypothesis in hand. The author reviews evidence of such a bias in a variety of guises and gives examples of its operation in several practical contexts. Possible explanations are considered, and the question of its utility or disutility is discussed.
We use public data from Twitter, both in English and Arabic, to study the phenomenon of secular vs. Islamist polarization in Twitter. Starting with a set of prominent seed Twitter users from both camps, we follow retweeting edges to obtain an extended network of users with inferred political orientation. We present an in-depth description of the members of the two camps, both in terms of behavior on Twitter and in terms of offline characteristics such as gender. Through the identification of partisan users, we compute a valence on the secular vs. Islamist axis for hashtags and use this information both to analyze topical interests and to quantify how polarized society as a whole is at a given point in time. For the last 12 months, large values on this "polarization barometer" coincided with periods of violence. Tweets are furthermore annotated using hand-crafted dictionaries to quantify the usage of (i) religious terms, (ii) derogatory terms referring to other religions, and (ii) references to charitable acts. The combination of all the information allows us to test and quantify a number of stereo-typical hypotheses such as (i) that religiosity and political Islamism are correlated, (ii) that political Islamism and negative views on other religions are linked, (iii) that religiosity goes hand in hand with charitable giving, and (iv) that the followers of the Egyptian Muslim Brotherhood are more tightly connected and expressing themselves "in unison" than the secular opposition. Whereas a lot of existing literature on the Arab Spring and the Egyptian Revolution is largely of qualitative and descriptive nature, our contribution lies in providing a quantitative and data-driven analysis of online communication in this dynamic and politically charged part of the world.
In this paper we describe an approach to the relationship between a network of interpersonal influences and the content of individuals’ opinions. Our work starts with the specification of social process rather than social equilibrium. Several models of social influence that have appeared in the literature are derived as special cases of the approach. Some implications for theories on social conflict and conformity also are developed in this paper.
We study the issue of polarization in society through a model of opinion formation. We say an opinion formation process is polarizing if it results in increased divergence of opinions. Empirical studies have shown that homophily, i.e., greater interaction between like-minded individuals, results in polarization. However, we show that DeGroot's well-known model of opinion formation based on repeated averaging can never be polarizing, even if individuals are arbitrarily homophilous. We generalize DeGroot's model to account for a phenomenon well known in social psychology as biased assimilation: When presented with mixed or inconclusive evidence on a complex issue, individuals draw undue support for their initial position, thereby arriving at a more extreme opinion. We show that in a simple model of homophilous networks, our biased opinion formation process results in polarization if individuals are sufficiently biased. In other words, homophily alone, without biased assimilation, is not sufficient to polarize society. Quite interestingly, biased assimilation also provides a framework to analyze the polarizing effect of Internet-based recommender systems that show us personalized content.
We present a model of opinion dynamics where agents adjust continuous opinions on the occasion of random binary encounters whenever their difference in opinion is below a given threshold. High thresholds yield convergence of opinions towards an average opinion, but low thresholds result in several opinion clusters: members of the same cluster share the same opinion but do not adjust any more with members of other clusters.
An extensive literature addresses citizen ignorance, but very little research focuses on misperceptions. Can these false or
unsubstantiated beliefs about politics be corrected? Previous studies have not tested the efficacy of corrections in a realistic
format. We conducted four experiments in which subjects read mock news articles that included either a misleading claim from
a politician, or a misleading claim and a correction. Results indicate that corrections frequently fail to reduce misperceptions
among the targeted ideological group. We also document several instances of a “backfire effect” in which corrections actually
increase misperceptions among the group in question.
KeywordsMisperceptions-Misinformation-Ignorance-Knowledge-Correction-Backfire
We examine partisan differences in the behavior, communication patterns and
social interactions of more than 18,000 politically-active Twitter users to
produce evidence that points to changing levels of partisan engagement with the
American online political landscape. Analysis of a network defined by the
communication activity of these users in proximity to the 2010 midterm
congressional elections reveals a highly segregated, well clustered partisan
community structure. Using cluster membership as a high-fidelity (87% accuracy)
proxy for political affiliation, we characterize a wide range of differences in
the behavior, communication and social connectivity of left- and right-leaning
Twitter users. We find that in contrast to the online political dynamics of the
2008 campaign, right-leaning Twitter users exhibit greater levels of political
activity, a more tightly interconnected social structure, and a communication
network topology that facilitates the rapid and broad dissemination of
political information.
Systems as diverse as genetic networks or the world wide web are best
described as networks with complex topology. A common property of many large
networks is that the vertex connectivities follow a scale-free power-law
distribution. This feature is found to be a consequence of the two generic
mechanisms that networks expand continuously by the addition of new vertices,
and new vertices attach preferentially to already well connected sites. A model
based on these two ingredients reproduces the observed stationary scale-free
distributions, indicating that the development of large networks is governed by
robust self-organizing phenomena that go beyond the particulars of the
individual systems.
When does opinion formation within an interacting group lead to consensus, polarization or fragmentation? The article investigates various models for the dynamics of continuous opinions by analytical methods as well as by computer simulations. Section 2 develops within a unified framework the classical model of consensus formation, the variant of this model due to Friedkin and Johnsen, a time-dependent version and a nonlinear version with bounded confidence of the agents. Section 3 presents for all these models major analytical results. Section 4 gives an extensive exploration of the nonlinear model with bounded confidence by a series of computer simulations. An appendix supplies needed mathematical definitions, tools, and theorems.
Suppose that the authors are interested in the distribution of a set of characteristics over a population. They study a precise sense in which this distribution can be said to be polarized and provide a theory of measurement. Polarization, as conceptualized here, is closely related to the generation of social tensions, to the possibilities of revolution and revolt, and to the existence of social unrest in general. The authors take special care to distinguish their theory from the theory of inequality measurement. They derive measures of polarization that are easily applicable to distributions of characteristics such as income and wealth. Copyright 1994 by The Econometric Society.
Echo chambers and opinion polarization recently quantified in several sociopolitical contexts and across different social media raise concerns on their potential impact on the spread of misinformation and on the openness of debates. Despite increasing efforts, the dynamics leading to the emergence of these phenomena remain unclear. We propose a model that introduces the dynamics of radicalization as a reinforcing mechanism driving the evolution to extreme opinions from moderate initial conditions. Inspired by empirical findings on social interaction dynamics, we consider agents characterized by heterogeneous activities and homophily. We show that the transition between a global consensus and emerging radicalized states is mostly governed by social influence and by the controversialness of the topic discussed. Compared with empirical data of polarized debates on Twitter, the model qualitatively reproduces the observed relation between users’ engagement and opinions, as well as opinion segregation in the interaction network. Our findings shed light on the mechanisms that may lie at the core of the emergence of echo chambers and polarization in social media.
Information cascades are important dynamical processes in complex networks. An information cascade can describe the spreading dynamics of rumour, disease, memes, or marketing campaigns, which initially start from a node or a set of nodes in the network. If conditions are right, information cascades rapidly encompass large parts of the network, thus leading to epidemics or epidemic spreading. Certain network topologies are particularly conducive to epidemics, while others decelerate and even prohibit rapid information spreading. Here we review models that describe information cascades in complex networks, with an emphasis on the role and consequences of node centrality. In particular, we present simulation results on sample networks that reveal just how relevant the centrality of initiator nodes is on the latter development of an information cascade, and we define the spreading influence of a node as the fraction of nodes that is activated as a result of the initial activation of that node. A systemic review of existing results shows that some centrality measures, such as the degree and betweenness, are positively correlated with the spreading influence, while other centrality measures, such as eccentricity and the information index, have negative correlation. A positive correlation implies that choosing a node with the highest centrality value will activate the largest number of nodes, while a negative correlation implies that the node with the lowest centrality value will have the same effect.We discuss possible applications of these results, and we emphasize how information cascades can help us identify nodes with the highest spreading capability in complex networks.
In this paper, we study the linking patterns and discussion topics of political bloggers. Our aim is to measure the degree of interaction between liberal and conservative blogs, and to uncover any differences in the structure of the two communities. Specifically, we analyze the posts of 40 "A-list" blogs over the period of two months preceding the U.S. Presidential Election of 2004, to study how often they referred to one another and to quantify the overlap in the topics they discussed, both within the liberal and conservative communities, and also across communities. We also study a single day snapshot of over 1,000 political blogs. This snapshot captures blogrolls (the list of links to other blogs frequently found in sidebars), and presents a more static picture of a broader blogosphere. Most significantly, we find differences in the behavior of liberal and conservative blogs, with conservative blogs linking to each other more frequently and in a denser pattern.
A simple and efficient method of simulation is discussed for point processes that are specified by their conditional intensities. The method is based on the thinning algorithm which was introduced recently by Lewis and Shedler for the simulation of nonhomogeneous Poisson processes. Algorithms are given for past dependent point processes containing multivariate processes. The simulations are performed for some parametric conditional intensity functions, and the accuracy of the simulated data is demonstrated by the likelihood ratio test and the minimum Akaike information criterion (AIC) procedure.
We model opinion dynamics in populations of agents with continuous opinion and uncertainty. The opinions and uncertainties are modified by random pair interactions. We propose a new model of interactions, called relative agreement model, which is a variant of the previously discussed bounded confidence. In this model, uncertainty as well as opinion can be modified by interactions. We introduce extremist agents by attributing a much lower uncertainty (and thus higher persuasion) to a small proportion of agents at the extremes of the opinion distribution. We study the evolution of the opinion distribution submitted to the relative agreement model. Depending upon the choice of parameters, the extremists can have a very local influence or attract the whole population. We propose a qualitative analysis of the convergence process based on a local field notion. The genericity of the observed results is tested on several variants of the bounded confidence model.
Reaching a consensus
- M H Degroot