This page summarizes recent research on Polarization and Echo-chambers on Social Media. Table of content:
Polarized groups typically contain two densely connected groups with some cross-connections. This pattern can be easily modeled by Stochastic Block Models (SBMs). Note that such a network structure does not gurantee polarization, but could be used as starting point.
I have created a Jupyter Notebook file that demonstrates how to create such networks in a few lines of code.
Next, we will add attributes to these nodes. This is aligned with the idea that polarization of a partitioned network can be explained by nodes behaviour. For example, nodes in one group will be 'pro' some target, whereas nodes in other group will be 'anti' the same target.
We again use the same notebook to create attributed networks Notebook file.
Next, i will try some of the algorithms used in some of the papers listed below to find how well they perform on this synthetic dataset.
ToDo: I have some Twitter datasets that I will add here. Other possiblity is the blogs dataset used in earlier studies.
To create a synthetic dataset, you can use this Jupyter Notebook file
2020, Lai, Mirko, Marcella Tambuscio, Viviana Patti, Giancarlo Ruffo, and Paolo Rosso. "Stance polarity in political debates: A diachronic perspective of network homophily and conversations on Twitter." Data & Knowledge Engineering 124 (2019): 101738.In the last decade, social media gained a very significant role in public debates, and despite the many intrinsic difficulties of analyzing data streaming from on-line platforms that are poisoned by bots, trolls, and low-quality information, it is undeniable that such data can still be used to test the public opinion and overall mood and to investigate how individuals communicate with each other. With the aim of analyzing the debate in Twitter on the 2016 referendum on the reform of the Italian Constitution, we created an Italian annotated corpus for stance detection for automatically estimating the stance of a relevant number of users. We take into account a diachronic perspective to shed lights on users’ opinion dynamics. Furthermore, different types of social network communities, based on friendships, retweets, quotes, and replies were investigated, in order to analyze the communication among users with similar and divergent viewpoints. We observe particular aspects of users’ behavior. First, our analysis suggests that users tend to be less explicit in expressing their stances after the outcome of the vote; simultaneously, users who exhibit a high number of cross-stance relations tend to become less polarized or to adopt a more neutral style in the following phase of the debate. Second, despite social media networks are generally aggregated in homogeneous communities, we highlight that the structure of the network can strongly change when different types of social relations are considered. In particular, networks defined by means of reply-to messages exhibit inverse homophily by stance, and users use more often replies for expressing diverging opinions, instead of other forms of communication. Interestingly, we also observe that the political polarization increases forthcoming the election and decreases after the election day.
2020, Darwish, Kareem. "Quantifying polarization on twitter: The kavanaugh nomination." In International Conference on Social Informatics, pp. 188-201. Springer, Cham, 2019. This paper addresses polarization quantification, particularly as it pertains to the nomination of Brett Kavanaugh to the US Supreme Court and his subsequent confirmation with the narrowest margin since 1881. Republican (GOP) and Democratic (DNC) senators voted overwhelmingly along party lines. In this paper, we examine political polarization concerning the nomination among Twitter users. To do so, we accurately identify the stance of more than 128 thousand Twitter users towards Kavanaugh’s nomination using both semi-supervised and supervised classification. Next, we quantify the polarization between the different groups in terms of who they retweet and which hashtags they use. We modify existing polarization quantification measures to make them more efficient and more effective. We also characterize the polarization between users who supported and opposed the nomination.
2020, Rashed, Ammar, Mucahid Kutlu, Kareem Darwish, Tamer Elsayed, and Cansın Bayrak. "Embeddings-Based Clustering for Target Specific Stances: The Case of a Polarized Turkey." arXiv preprint arXiv:2005.09649 (2020). On June 24, 2018, Turkey conducted a highly consequential election in which the Turkish people elected their president and parliament in the first election under a new presidential system. During the election period, the Turkish people extensively shared their political opinions on Twitter. One aspect of polarization among the electorate was support for or opposition to the reelection of Recep Tayyip Erdogan. In this paper, ˘ we present an unsupervised method for target-specific stance detection in a polarized setting, specifically Turkish politics, achieving 90% precision in identifying user stances, while maintaining more than 80% recall. The method involves representing users in an embedding space using Google’s Convolutional Neural Network (CNN) based multilingual universal sentence encoder. The representations are then projected onto a lower dimensional space in a manner that reflects similarities and are consequently clustered. We show the effectiveness of our method in properly clustering users of divergent groups across multiple targets that include political figures, different groups, and parties. We perform our analysis on a large dataset of 108M Turkish election-related tweets along with the timeline tweets of 168k Turkish users, who authored 213M tweets. Given the resultant user stances, we are able to observe correlations between topics and compute topic polarization.
2020, Gurukar, Saket, Deepak Ajwani, Sourav Dutta, Juho Lauri, Srinivasan Parthasarathy, and Alessandra Sala. "Towards Quantifying the Distance between Opinions." In Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 229-239. 2020. Increasingly, critical decisions in public policy, governance, and business strategy rely on a deeper understanding of the needs and opinions of constituent members (e.g. citizens, shareholders). While it has become easier to collect a large number of opinions on a topic, there is a necessity for automated tools to help navigate the space of opinions. In such contexts understanding and quantifying the similarity between opinions is key. We find that measures based solely on text similarity or on overall sentiment often fail to effectively capture the distance between opinions. Thus, we propose a new distance measure for capturing the similarity between opinions that leverages the nuanced observation – similar opinions express similar sentiment polarity on specific relevant entities-of-interest. Specifically, in an unsupervised setting, our distance measure achieves significantly better Adjusted Rand Index scores (up to 56x) and Silhouette coefficients (up to 21x) compared to existing approaches. Similarly, in a supervised setting, our opinion distance measure achieves considerably better accuracy (up to 20% increase) compared to extant approaches that rely on text similarity, stance similarity, and sentiment similarity.
2020, Baumann, Fabian, Philipp Lorenz-Spreen, Igor M. Sokolov, and Michele Starnini. "Modeling echo chambers and polarization dynamics in social networks." Physical Review Letters 124, no. 4 (2020): 048301. 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.
2019, Demszky, Dorottya, Nikhil Garg, Rob Voigt, James Zou, Matthew Gentzkow, Jesse Shapiro, and Dan Jurafsky. "Analyzing polarization in social media: Method and application to tweets on 21 mass shootings." arXiv preprint arXiv:1904.01596 (2019). We provide an NLP framework to uncover four linguistic dimensions of political polarization in social media: topic choice, framing, affect and illocutionary force. We quantify these aspects with existing lexical methods, and propose clustering of tweet embeddings as a means to identify salient topics for analysis across events; human evaluations show that our approach generates more cohesive topics than traditional LDA-based models. We apply our methods to study 4.4M tweets on 21 mass shootings. We provide evidence that the discussion of these events is highly polarized politically and that this polarization is primarily driven by partisan differences in framing rather than topic choice. We identify framing devices, such as grounding and the contrasting use of the terms “terrorist” and “crazy”, that contribute to polarization. Results pertaining to topic choice, affect and illocutionary force suggest that Republicans focus more on the shooter and event-specific facts (news) while Democrats focus more on the victims and call for policy changes. Our work contributes to a deeper understanding of the way group divisions manifest in language and to computational methods for studying them
2019, Amelkin, Victor, Petko Bogdanov, and Ambuj K. Singh. "A distance measure for the analysis of polar opinion dynamics in social networks." ACM Transactions on Knowledge Discovery from Data (TKDD) 13, no. 4 (2019): 1-34. Analysis of opinion dynamics in social networks plays an important role in today’s life. For predicting users’ political preference, it is particularly important to be able to analyze the dynamics of competing polar opinions, such as pro-Democrat vs. pro-Republican. While observing the evolution of polar opinions in a social network over time, can we tell when the network evolved abnormally? Furthermore, can we predict how the opinions of the users will change in the future? To answer such questions, it is insufficient to study individual user behavior, since opinions can spread beyond users’ ego-networks. Instead, we need to consider the opinion dynamics of all users simultaneously and capture the connection between the individuals’ behavior and the global evolution pattern of the social network. In this work, we introduce the Social Network Distance (SND)—a distance measure that quantifies the likelihood of evolution of one snapshot of a social network into another snapshot under a chosen model of polar opinion dynamics. SND has a rich semantics of a transportation problem, yet, is computable in time linear in the number of users and, as such, is applicable to large-scale online social networks. In our experiments with synthetic and Twitter data, we demonstrate the utility of our distance measure for anomalous event detection. It achieves a true positive rate of 0.83, twice as high as that of alternatives. The same predictions presented in precision-recall space show that SND retains perfect precision for recall up to 0.2. Its precision then decreases while maintaining more than 2-fold improvement over alternatives for recall up to 0.95. When used for opinion prediction in Twitter data, SND’s accuracy is 75.6%, which is 7.5% higher than that of the next best method.
2019, Kearney, Michael Wayne. "Analyzing change in network polarization." new media & society 21, no. 6 (2019): 1380-1402 The growing influence of social media in an era of media fragmentation has amplified concerns of political polarization. Yet relatively few studies have analyzed polarization in user networks over time. This study therefore examines change in network polarization on Twitter during a highly contested general election. Using Twitter’s REST API, user networks of 3000 randomly selected followers of well-known partisan and entertainmentoriented accounts were recorded 17 times in the 7 months leading up to the 2016 general election. Results suggest that partisan users form highly partisan networks on Twitter, while moderate, or less engaged, users continue to mostly avoid politics.
2018, Lahoti, Preethi, Kiran Garimella, and Aristides Gionis. "Joint non-negative matrix factorization for learning ideological leaning on Twitter." In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 351-359. ACM, 2018. https://arxiv.org/pdf/1711.10251.pdf
2018, Stewart, Leo G., Ahmer Arif, and Kate Starbird. "Examining trolls and polarization with a retweet network." In Proc. ACM WSDM, Workshop on Misinformation and Misbehavior Mining on the Web (to appear). Accessed January. 2018.
2018, Nabeel Gillani, Ann Yuan, Martin Saveski, Soroush Vosoughi, and Deb Roy. 2018. Me, My Echo Chamber, and I: Introspection on Social Media Polarization. In Proceedings of the 2018 World Wide Web Conference (WWW '18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 823-831. DOI: https://doi.org/10.1145/3178876.3186130
2018, Kiran Garimella et al. Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship
2018, Askitas, Nikolaos. "Explaining opinion polarisation with opinion copulas." PloS one 12, no. 8 (2017): e0183277. An empirically founded and widely established driving force in opinion dynamics is homophily i.e. the tendency of ªbirds of a featherº to ªflock togetherº. The closer our opinions are the more likely it is that we will interact and converge. Models using these assumptions are called bounded confidence models (BCM) as they assume a tolerance threshold after which interaction is unlikely. They are known to produce one or more clusters, depending on the size of the bound, with more than one cluster being possible only in the deterministic case. Introducing noise, as is likely to happen in a stochastic world, causes BCM to produce consensus which leaves us with the open problem of explaining the emergence and sustainance of opinion clusters and polarisation. We investigate the role of heterogeneous priors in opinion formation, introduce the concept of opinion copulas, argue that it is well supported by findings in Social Psychology and use it to show that the stochastic BCM does indeed produce opinion clustering without the need for extra assumptions.
2018, Garimella, Kiran, Gianmarco De Francisci Morales, Aristides Gionis, and Michael Mathioudakis. "Quantifying Controversy on Social Media." ACM Transactions on Social Computing 1, no. 1 (2018): 3.
2018, Sîrbu, Alina, Dino Pedreschi, Fosca Giannotti, and János Kertész. "Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model." PloS one 14, no. 3 (2019): e0213246.
The flow of information reaching us via the online media platforms is optimized not by the information content or relevance but by popularity and proximity to the target. This is typically performed in order to maximise platform usage. As a side effect, this introduces an algorithmic bias that is believed to enhance fragmentation and polarization of the societal debate. To study this phenomenon, we modify the well-known continuous opinion dynamics model of bounded confidence in order to account for the algorithmic bias and investigate its consequences. In the simplest version of the original model the pairs of discussion participants are chosen at random and their opinions get closer to each other if they are within a fixed tolerance level. We modify the selection rule of the discussion partners: there is an enhanced probability to choose individuals whose opinions are already close to each other, thus mimicking the behavior of online media which suggest interaction with similar peers. As a result we observe: a) an increased tendency towards opinion fragmentation, which emerges also in conditions where the original model would predict consensus, b) increased polarisation of opinions and c) a dramatic slowing down of the speed at which the convergence at the asymptotic state is reached, which makes the system highly unstable. Fragmentation and polarization are augmented by a fragmented initial population.
2017, Matakos, Antonis, Evimaria Terzi, and Panayiotis Tsaparas. "Measuring and moderating opinion polarization in social networks." Data Mining and Knowledge Discovery 31, no. 5 (2017): 1480-1505. The polarization of society over controversial social issues has been the subject of study in social sciences for decades (Isenberg in J Personal Soc Psychol 50(6):1141–1151, 1986, Sunstein in J Polit Philos 10(2):175–195, 2002). The widespread usage of online social networks and social media, and the tendency of people to connect and interact with like-minded individuals has only intensified the phenomenon of polarization (Bakshy et al. in Science 348(6239):1130–1132, 2015). In this paper, we consider the problem of measuring and reducing polarization of opinions in a social network. Using a standard opinion formation model (Friedkin and Johnsen in J Math Soc 15(3–4):193–206, 1990), we define the polarization index, which, given a network and the opinions of the individuals in the network, it quantifies the polarization observed in the network. Our measure captures the tendency of opinions to concentrate in network communities, creating echo-chambers. Given this numeric measure of polarization, we then consider the problem of reducing polarization in the network by convincing individuals (e.g., through education, exposure to diverse viewpoints, or incentives) to adopt a more neutral stand towards controversial issues. We formally define the ModerateInternal and ModerateExpressed problems, and we prove that both our problems are NP-hard. By exploiting the linearalgebraic characteristics of the opinion formation model we design polynomial-time algorithms for both problems. Our experiments with real-world datasets demonstrate the validity of our metric, and the efficiency and the effectiveness of our algorithms in practice.
2017, Garimella, Venkata Rama Kiran, and Ingmar Weber. "A long-term analysis of polarization on Twitter." In Eleventh International AAAI Conference on Web and Social Media. 2017.
2017, M. T. A. Amin, C. Aggarwal, S. Yao, T. Abdelzaher and L. Kaplan, "Unveiling polarization in social networks: A matrix factorization approach," IEEE INFOCOM 2017 - IEEE Conference on Computer Communications, Atlanta, GA, 2017, pp. 1-9.
doi: 10.1109/INFOCOM.2017.8056959 Social media has played an important role in shaping political discourse over the last decade. It is often perceived to have increased political polarization, thanks to the scale of discussions and their public nature. Here, we try to answer whether political polarization in the US on Twitter has increased over the last eight years. We analyze a large longitudinal Twitter dataset of 679,000 users and look at signs of polarization in their (i) network - how people follow political and media accounts, (ii) tweeting behavior - whether they retweet content from both sides, and (iii) content - how partisan the hashtags they use are. Our analysis shows that online polarization has indeed increased over the past eight years and that, depending on the measure, the relative change is 10%-20%. Our study is one of very few with such a long-term perspective, encompassing two US presidential elections and two mid-term elections, providing a rare longitudinal analysis.
2017, Peterson, Erik, Sharad Goel, and Shanto Iyengar. "Echo Chambers and Partisan Polarization: Evidence from the 2016 Presidential Campaign." (2017). Online users tend to select claims that adhere to their system of beliefs and to ignore dissenting information. Confirmation bias, indeed, plays a pivotal role in viral phenomena. Furthermore, the wide availability of content on the web fosters the aggregation of likeminded people where debates tend to enforce group polarization. Such a configuration might alter the public debate and thus the formation of the public opinion. In this paper we provide a mathematical model to study online social debates and the related polarization dynamics. We assume the basic updating rule of the Bounded Confidence Model (BCM) and we develop two variations a) the Rewire with Bounded Confidence Model (RBCM), in which discordant links are broken until convergence is reached; and b) the Unbounded Confidence Model, under which the interaction among discordant pairs of users is allowed even with a negative feedback, either with the rewiring step (RUCM) or without it (UCM). From numerical simulations we find that the new models (UCM and RUCM), unlike the BCM, are able to explain the coexistence of two stable final opinions, often observed in reality. Lastly, we present a mean field approximation of the newly introduced models.
2017, Takikawa, Hiroki, and Kikuko Nagayoshi. "Political Polarization in Social Media: Analysis of the" Twitter Political Field" in Japan." arXiv preprint arXiv:1711.06752 (2017).
2017, Tang, Shiliang, Qingyun Liu, Megan McQueen, Scott Counts, Apurv Jain, Heather Zheng, and Ben Zhao. "Echo chambers in investment discussion boards." (2017). https://www.microsoft.com/en-us/research/wp-content/uploads/2017/03/investment-echo-chambers.pdf
2017, Del Vicario, Michela, Antonio Scala, Guido Caldarelli, H. Eugene Stanley, and Walter Quattrociocchi. "Modeling confirmation bias and polarization." Scientific reports 7 (2017): 40391.
2016, Bessi, Alessandro, Fabiana Zollo, Michela Del Vicario, Michelangelo Puliga, Antonio Scala, Guido Caldarelli, Brian Uzzi, and Walter Quattrociocchi. "Users polarization on Facebook and Youtube." PloS one 11, no. 8 (2016): e0159641.
Users online tend to select information that support and adhere their beliefs, and to form polarized groups sharing the same view—e.g. echo chambers. Algorithms for content promotion may favour this phenomenon, by accounting for users preferences and thus limiting the exposure to unsolicited contents. To shade light on this question, we perform a comparative study on how same contents (videos) are consumed on different online social media— i.e. Facebook and YouTube—over a sample of 12M of users. Our findings show that content drives the emergence of echo chambers on both platforms. Moreover, we show that the users’ commenting patterns are accurate predictors for the formation of echo-chambers
2015, Barberá, Pablo, John T. Jost, Jonathan Nagler, Joshua A. Tucker, and Richard Bonneau. "Tweeting from left to right: Is online political communication more than an echo chamber?." Psychological science 26, no. 10 (2015): 1531-1542.
2015, Morales, A. J., Javier Borondo, Juan Carlos Losada, and Rosa M. Benito. "Measuring political polarization: Twitter shows the two sides of Venezuela." Chaos: An Interdisciplinary Journal of Nonlinear Science 25, no. 3 (2015): 033114
2015, Williams, Hywel TP, James R. McMurray, Tim Kurz, and F. Hugo Lambert. "Network analysis reveals open forums and echo chambers in social media discussions of climate change." Global Environmental Change 32 (2015): 126-138.
2014, Akoglu, Leman. "Quantifying Political Polarity Based on Bipartite Opinion Networks." In ICWSM. 2014. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.432.4647&rep=rep1&type=pdf
2013, Guerra, Pedro Henrique Calais, Wagner Meira Jr, Claire Cardie, and Robert Kleinberg. "A Measure of Polarization on Social Media Networks Based on Community Boundaries." In ICWSM. 2013. Polarization in social media networks is a fact in several scenarios such as political debates and other contexts such as same-sex marriage, abortion and gun control. Understanding and quantifying polarization is a longterm challenge to researchers from several areas, also being a key information for tasks such as opinion analysis. In this paper, we perform a systematic comparison between social networks that arise from both polarized and non-polarized contexts. This comparison shows that the traditional polarization metric – modularity – is not a direct measure of antagonism between groups, since non-polarized networks may be also divided into fairly modular communities. To bridge this conceptual gap, we propose a novel polarization metric based on the analysis of the boundary of a pair of (potentially polarized) communities, which better captures the notions of antagonism and polarization. We then characterize polarized and non-polarized social networks according to the concentration of high-degree nodes in the boundary of communities, and found that polarized networks tend to exhibit low concentration of popular nodes along the boundary. To demonstrate the usefulness of our polarization measures, we analyze opinions expressed on Twitter on the gun control issue in the United States, and conclude that our novel metrics help making sense of opinions expressed on online media.
2011, Conover, Michael, Jacob Ratkiewicz, Matthew R. Francisco, Bruno Gonçalves, Filippo Menczer, and Alessandro Flammini. "Political polarization on twitter." ICWSM 133 (2011): 89-96
2009, Gilbert, Eric, Tony Bergstrom, and Karrie Karahalios. "Blogs are echo chambers: Blogs are echo chambers." In System Sciences, 2009. HICSS'09. 42nd Hawaii International Conference on, pp. 1-10. IEEE, 2009.
Awesome tutorial by Kiran Garimella Orignially shared on Dropbox
Coletto, Mauro, Kiran Garimella, Aristides Gionis, and Claudio Lucchese. "A motif-based approach for identifying controversy." arXiv preprint arXiv:1703.05053 (2017).
BuzzFeedNews Partisan News Analysis BBC The-myth-of-the-online-echo-chamber