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A conceptual framework for ideology beyond the left and right
Authors:
Kenneth Joseph,
Kim Williams,
David Lazer
Abstract:
NLP+CSS work has operationalized ideology almost exclusively on a left/right partisan axis. This approach obscures the fact that people hold interpretations of many different complex and more specific ideologies on issues like race, climate, and gender. We introduce a framework that understands ideology as an attributed, multi-level socio-cognitive concept network, and explains how ideology manife…
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NLP+CSS work has operationalized ideology almost exclusively on a left/right partisan axis. This approach obscures the fact that people hold interpretations of many different complex and more specific ideologies on issues like race, climate, and gender. We introduce a framework that understands ideology as an attributed, multi-level socio-cognitive concept network, and explains how ideology manifests in discourse in relation to other relevant social processes like framing. We demonstrate how this framework can clarifies overlaps between existing NLP tasks (e.g. stance detection and natural language inference) and also how it reveals new research directions. Our work provides a unique and important bridge between computational methods and ideology theory, enabling richer analysis of social discourse in a way that benefits both fields.
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Submitted 19 March, 2026;
originally announced March 2026.
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Towards global equity in political polarization research
Authors:
Max Falkenberg,
Matteo Cinelli,
Alessandro Galeazzi,
Christopher A. Bail,
Rosa M Benito,
Axel Bruns,
Anatoliy Gruzd,
David Lazer,
Jae K Lee,
Jennifer McCoy,
Kikuko Nagayoshi,
David G Rand,
Antonio Scala,
Alexandra Siegel,
Sander van der Linden,
Onur Varol,
Ingmar Weber,
Magdalena Wojcieszak,
Fabiana Zollo,
Andrea Baronchelli,
Walter Quattrociocchi
Abstract:
With a folk understanding that political polarization refers to socio-political divisions within a society, many have proclaimed that we are more divided than ever. In this account, polarization has been blamed for populism, the erosion of social cohesion, the loss of trust in the institutions of democracy, legislative dysfunction, and the collective failure to address existential risks such as Co…
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With a folk understanding that political polarization refers to socio-political divisions within a society, many have proclaimed that we are more divided than ever. In this account, polarization has been blamed for populism, the erosion of social cohesion, the loss of trust in the institutions of democracy, legislative dysfunction, and the collective failure to address existential risks such as Covid-19 or climate change. However, at a global scale there is surprisingly little academic literature which conclusively supports these claims, with half of all studies being U.S.-focused. Here, we provide an overview of the global state of research on polarization, highlighting insights that are robust across countries, those unique to specific contexts, and key gaps in the literature. We argue that addressing these gaps is urgent, but has been hindered thus far by systemic and cultural barriers, such as regionally stratified restrictions on data access and misaligned research incentives. If continued cross-disciplinary inertia means that these disparities are left unaddressed, we see a substantial risk that countries will adopt policies to tackle polarization based on inappropriate evidence, risking flawed decision-making and the weakening of democratic institutions.
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Submitted 15 April, 2025;
originally announced April 2025.
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DomainDemo: a dataset of domain-sharing activities among different demographic groups on Twitter
Authors:
Kai-Cheng Yang,
Pranav Goel,
Alexi Quintana-Mathé,
Luke Horgan,
Stefan D. McCabe,
Nir Grinberg,
Kenneth Joseph,
David Lazer
Abstract:
Social media play a pivotal role in disseminating web content, particularly during elections, yet our understanding of the association between demographic factors and information sharing online remains limited. Here, we introduce a unique dataset, DomainDemo, linking domains shared on Twitter (X) with the demographic characteristics of associated users, including age, gender, race, political affil…
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Social media play a pivotal role in disseminating web content, particularly during elections, yet our understanding of the association between demographic factors and information sharing online remains limited. Here, we introduce a unique dataset, DomainDemo, linking domains shared on Twitter (X) with the demographic characteristics of associated users, including age, gender, race, political affiliation, and geolocation, from 2011 to 2022. This new resource was derived from a panel of over 1.5 million Twitter users matched against their U.S. voter registration records, facilitating a better understanding of a decade of information flows on one of the most prominent social media platforms and trends in political and public discourse among registered U.S. voters from different sociodemographic groups. By aggregating user demographic information onto the domains, we derive five metrics that provide critical insights into over 129,000 websites. In particular, the localness and partisan audience metrics quantify the domains' geographical reach and ideological orientation, respectively. These metrics show substantial agreement with existing classifications, suggesting the effectiveness and reliability of DomainDemo's approach.
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Submitted 16 July, 2025; v1 submitted 14 January, 2025;
originally announced January 2025.
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Diffusion of complex contagions is shaped by a trade-off between reach and reinforcement
Authors:
Allison Wan,
Christoph Riedl,
David Lazer
Abstract:
How does social network structure amplify or stifle behavior diffusion? Existing theory suggests that when social reinforcement makes the adoption of behavior more likely, it should spread more -- both farther and faster -- on clustered networks with redundant ties. Conversely, if adoption does not benefit from social reinforcement, it should spread more on random networks which avoid such redunda…
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How does social network structure amplify or stifle behavior diffusion? Existing theory suggests that when social reinforcement makes the adoption of behavior more likely, it should spread more -- both farther and faster -- on clustered networks with redundant ties. Conversely, if adoption does not benefit from social reinforcement, it should spread more on random networks which avoid such redundancies. We develop a novel model of behavior diffusion with tunable probabilistic adoption and social reinforcement parameters to systematically evaluate the conditions under which clustered networks spread behavior better than random networks. Using simulations and analytical methods, we identify precise boundaries in the parameter space where one network type outperforms the other or they perform equally. We find that, in most cases, random networks spread behavior as far or farther than clustered networks, even when social reinforcement increases adoption. Although we find that probabilistic, socially reinforced behaviors can spread farther on clustered networks in some cases, this is not the dominant pattern. Clustered networks are even less advantageous when individuals remain influential for longer after adopting, have more neighbors, or need more neighbors before social reinforcement takes effect. Under such conditions, clustering tends to help only when adoption is nearly deterministic, which is not representative of socially reinforced behaviors more generally. Clustered networks outperform random networks by a 5% margin in only 22% of the parameter space under its most favorable conditions. This pattern reflects a fundamental tradeoff: random ties enhance reach, while clustered ties enhance social reinforcement.
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Submitted 10 July, 2025; v1 submitted 12 November, 2024;
originally announced November 2024.
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Using co-sharing to identify use of mainstream news for promoting potentially misleading narratives
Authors:
Pranav Goel,
Jon Green,
David Lazer,
Philip Resnik
Abstract:
Much of the research quantifying volume and spread of online misinformation measures the construct at the source level, identifying a set of specific unreliable domains that account for a relatively small share of news consumption. This source-level dichotomy obscures the potential for users to repurpose factually true information from reliable sources to advance misleading narratives. We demonstr…
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Much of the research quantifying volume and spread of online misinformation measures the construct at the source level, identifying a set of specific unreliable domains that account for a relatively small share of news consumption. This source-level dichotomy obscures the potential for users to repurpose factually true information from reliable sources to advance misleading narratives. We demonstrate this potentially far more prevalent form of misinformation by identifying articles from reliable sources that are frequently co-shared with (shared by users who also shared) "fake" news on social media, and concurrently extracting narratives present in fake news content and claims fact-checked as false. Specifically in this study, we use Twitter/X data from May 2018 to November 2021 matched to a U.S. voter file. We find that narratives present in misinformation content are significantly more likely to occur in co-shared articles than in articles from the same reliable sources that are not co-shared, consistent with users using information from mainstream sources to enhance the credibility and reach of potentially misleading claims.
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Submitted 10 June, 2025; v1 submitted 11 August, 2023;
originally announced August 2023.
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The science of fake news
Authors:
David M. J. Lazer,
Matthew A. Baum,
Yochai Benkler,
Adam J. Berinsky,
Kelly M. Greenhill,
Filippo Menczer,
Miriam J. Metzger,
Brendan Nyhan,
Gordon Pennycook,
David Rothschild,
Michael Schudson,
Steven A. Sloman,
Cass R. Sunstein,
Emily A. Thorson,
Duncan J. Watts,
Jonathan L. Zittrain
Abstract:
Fake news emerged as an apparent global problem during the 2016 U.S. Presidential election. Addressing it requires a multidisciplinary effort to define the nature and extent of the problem, detect fake news in real time, and mitigate its potentially harmful effects. This will require a better understanding of how the Internet spreads content, how people process news, and how the two interact. We r…
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Fake news emerged as an apparent global problem during the 2016 U.S. Presidential election. Addressing it requires a multidisciplinary effort to define the nature and extent of the problem, detect fake news in real time, and mitigate its potentially harmful effects. This will require a better understanding of how the Internet spreads content, how people process news, and how the two interact. We review the state of knowledge in these areas and discuss two broad potential mitigation strategies: better enabling individuals to identify fake news, and intervention within the platforms to reduce the attention given to fake news. The cooperation of Internet platforms (especially Facebook, Google, and Twitter) with researchers will be critical to understanding the scale of the issue and the effectiveness of possible interventions.
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Submitted 15 July, 2023;
originally announced July 2023.
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Engagement Outweighs Exposure to Partisan and Unreliable News within Google Search
Authors:
Ronald E. Robertson,
Jon Green,
Damian J. Ruck,
Katherine Ognyanova,
Christo Wilson,
David Lazer
Abstract:
If popular online platforms systematically expose their users to partisan and unreliable news, they could potentially contribute to societal issues like rising political polarization. This concern is central to the echo chamber and filter bubble debates, which critique the roles that user choice and algorithmic curation play in guiding users to different online information sources. These roles can…
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If popular online platforms systematically expose their users to partisan and unreliable news, they could potentially contribute to societal issues like rising political polarization. This concern is central to the echo chamber and filter bubble debates, which critique the roles that user choice and algorithmic curation play in guiding users to different online information sources. These roles can be measured in terms of exposure, the URLs seen while using an online platform, and engagement, the URLs selected while on that platform or browsing the web more generally. However, due to the challenges of obtaining ecologically valid exposure data--what real users saw during their regular platform use--studies in this vein often only examine engagement data, or estimate exposure via simulated behavior or inference. Despite their centrality to the contemporary information ecosystem, few such studies have focused on web search, and even fewer have examined both exposure and engagement on any platform. To address these gaps, we conducted a two-wave study pairing surveys with ecologically valid measures of exposure and engagement on Google Search during the 2018 and 2020 US elections. We found that participants' partisan identification had a small and inconsistent relationship with the amount of partisan and unreliable news they were exposed to on Google Search, a more consistent relationship with the search results they chose to follow, and the most consistent relationship with their overall engagement. That is, compared to the news sources our participants were exposed to on Google Search, we found more identity-congruent and unreliable news sources in their engagement choices, both within Google Search and overall. These results suggest that exposure and engagement with partisan or unreliable news on Google Search are not primarily driven by algorithmic curation, but by users' own choices.
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Submitted 28 September, 2022; v1 submitted 31 December, 2021;
originally announced January 2022.
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(Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys
Authors:
Kenneth Joseph,
Sarah Shugars,
Ryan Gallagher,
Jon Green,
Alexi Quintana Mathé,
Zijian An,
David Lazer
Abstract:
Stance detection, which aims to determine whether an individual is for or against a target concept, promises to uncover public opinion from large streams of social media data. Yet even human annotation of social media content does not always capture "stance" as measured by public opinion polls. We demonstrate this by directly comparing an individual's self-reported stance to the stance inferred fr…
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Stance detection, which aims to determine whether an individual is for or against a target concept, promises to uncover public opinion from large streams of social media data. Yet even human annotation of social media content does not always capture "stance" as measured by public opinion polls. We demonstrate this by directly comparing an individual's self-reported stance to the stance inferred from their social media data. Leveraging a longitudinal public opinion survey with respondent Twitter handles, we conducted this comparison for 1,129 individuals across four salient targets. We find that recall is high for both "Pro" and "Anti" stance classifications but precision is variable in a number of cases. We identify three factors leading to the disconnect between text and author stance: temporal inconsistencies, differences in constructs, and measurement errors from both survey respondents and annotators. By presenting a framework for assessing the limitations of stance detection models, this work provides important insight into what stance detection truly measures.
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Submitted 7 September, 2021; v1 submitted 3 September, 2021;
originally announced September 2021.
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Sustained Online Amplification of COVID-19 Elites in the United States
Authors:
Ryan J. Gallagher,
Larissa Doroshenko,
Sarah Shugars,
David Lazer,
Brooke Foucault Welles
Abstract:
The ongoing, fluid nature of the COVID-19 pandemic requires individuals to regularly seek information about best health practices, local community spreading, and public health guidelines. In the absence of a unified response to the pandemic in the United States and clear, consistent directives from federal and local officials, people have used social media to collectively crowdsource COVID-19 elit…
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The ongoing, fluid nature of the COVID-19 pandemic requires individuals to regularly seek information about best health practices, local community spreading, and public health guidelines. In the absence of a unified response to the pandemic in the United States and clear, consistent directives from federal and local officials, people have used social media to collectively crowdsource COVID-19 elites, a small set of trusted COVID-19 information sources. We take a census of COVID-19 crowdsourced elites in the United States who have received sustained attention on Twitter during the pandemic. Using a mixed methods approach with a panel of Twitter users linked to public U.S. voter registration records, we find that journalists, media outlets, and political accounts have been consistently amplified around COVID-19, while epidemiologists, public health officials, and medical professionals make up only a small portion of all COVID-19 elites on Twitter. We show that COVID-19 elites vary considerably across demographic groups, and that there are notable racial, geographic, and political similarities and disparities between various groups and the demographics of their elites. With this variation in mind, we discuss the potential for using the disproportionate online voice of crowdsourced COVID-19 elites to equitably promote timely public health information and mitigate rampant misinformation.
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Submitted 15 September, 2020;
originally announced September 2020.
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Exploring the Ideological Nature of Journalists' Social Networks on Twitter and Associations with News Story Content
Authors:
John Wihbey,
Thalita Dias Coleman,
Kenneth Joseph,
David Lazer
Abstract:
The present work proposes the use of social media as a tool for better understanding the relationship between a journalists' social network and the content they produce. Specifically, we ask: what is the relationship between the ideological leaning of a journalist's social network on Twitter and the news content he or she produces? Using a novel dataset linking over 500,000 news articles produced…
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The present work proposes the use of social media as a tool for better understanding the relationship between a journalists' social network and the content they produce. Specifically, we ask: what is the relationship between the ideological leaning of a journalist's social network on Twitter and the news content he or she produces? Using a novel dataset linking over 500,000 news articles produced by 1,000 journalists at 25 different news outlets, we show a modest correlation between the ideologies of who a journalist follows on Twitter and the content he or she produces. This research can provide the basis for greater self-reflection among media members about how they source their stories and how their own practice may be colored by their online networks. For researchers, the findings furnish a novel and important step in better understanding the construction of media stories and the mechanics of how ideology can play a role in shaping public information.
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Submitted 30 August, 2017; v1 submitted 22 August, 2017;
originally announced August 2017.
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ConStance: Modeling Annotation Contexts to Improve Stance Classification
Authors:
Kenneth Joseph,
Lisa Friedland,
William Hobbs,
Oren Tsur,
David Lazer
Abstract:
Manual annotations are a prerequisite for many applications of machine learning. However, weaknesses in the annotation process itself are easy to overlook. In particular, scholars often choose what information to give to annotators without examining these decisions empirically. For subjective tasks such as sentiment analysis, sarcasm, and stance detection, such choices can impact results. Here, fo…
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Manual annotations are a prerequisite for many applications of machine learning. However, weaknesses in the annotation process itself are easy to overlook. In particular, scholars often choose what information to give to annotators without examining these decisions empirically. For subjective tasks such as sentiment analysis, sarcasm, and stance detection, such choices can impact results. Here, for the task of political stance detection on Twitter, we show that providing too little context can result in noisy and uncertain annotations, whereas providing too strong a context may cause it to outweigh other signals. To characterize and reduce these biases, we develop ConStance, a general model for reasoning about annotations across information conditions. Given conflicting labels produced by multiple annotators seeing the same instances with different contexts, ConStance simultaneously estimates gold standard labels and also learns a classifier for new instances. We show that the classifier learned by ConStance outperforms a variety of baselines at predicting political stance, while the model's interpretable parameters shed light on the effects of each context.
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Submitted 21 August, 2017;
originally announced August 2017.
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Measuring Personalization of Web Search
Authors:
Anikó Hannák,
Piotr Sapieżyński,
Arash Molavi Khaki,
David Lazer,
Alan Mislove,
Christo Wilson
Abstract:
Web search is an integral part of our daily lives. Recently, there has been a trend of personalization in Web search, where different users receive different results for the same search query. The increasing level of personalization is leading to concerns about Filter Bubble effects, where certain users are simply unable to access information that the search engines' algorithm decides is irrelevan…
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Web search is an integral part of our daily lives. Recently, there has been a trend of personalization in Web search, where different users receive different results for the same search query. The increasing level of personalization is leading to concerns about Filter Bubble effects, where certain users are simply unable to access information that the search engines' algorithm decides is irrelevant. Despite these concerns, there has been little quantification of the extent of personalization in Web search today, or the user attributes that cause it.
In light of this situation, we make three contributions. First, we develop a methodology for measuring personalization in Web search results. While conceptually simple, there are numerous details that our methodology must handle in order to accurately attribute differences in search results to personalization. Second, we apply our methodology to 200 users on Google Web Search and 100 users on Bing. We find that, on average, 11.7% of results show differences due to personalization on Google, while 15.8% of results are personalized on Bing, but that this varies widely by search query and by result ranking. Third, we investigate the user features used to personalize on Google Web Search and Bing. Surprisingly, we only find measurable personalization as a result of searching with a logged in account and the IP address of the searching user. Our results are a first step towards understanding the extent and effects of personalization on Web search engines today.
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Submitted 15 June, 2017;
originally announced June 2017.
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Tracking Employment Shocks Using Mobile Phone Data
Authors:
Jameson L. Toole,
Yu-Ru Lin,
Erich Muehlegger,
Daniel Shoag,
Marta C. Gonzalez,
David Lazer
Abstract:
Can data from mobile phones be used to observe economic shocks and their consequences at multiple scales? Here we present novel methods to detect mass layoffs, identify individuals affected by them, and predict changes in aggregate unemployment rates using call detail records (CDRs) from mobile phones. Using the closure of a large manufacturing plant as a case study, we first describe a structural…
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Can data from mobile phones be used to observe economic shocks and their consequences at multiple scales? Here we present novel methods to detect mass layoffs, identify individuals affected by them, and predict changes in aggregate unemployment rates using call detail records (CDRs) from mobile phones. Using the closure of a large manufacturing plant as a case study, we first describe a structural break model to correctly detect the date of a mass layoff and estimate its size. We then use a Bayesian classification model to identify affected individuals by observing changes in calling behavior following the plant's closure. For these affected individuals, we observe significant declines in social behavior and mobility following job loss. Using the features identified at the micro level, we show that the same changes in these calling behaviors, aggregated at the regional level, can improve forecasts of macro unemployment rates. These methods and results highlight promise of new data resources to measure micro economic behavior and improve estimates of critical economic indicators.
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Submitted 25 May, 2015;
originally announced May 2015.
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Facts and Figuring: An Experimental Investigation of Network Structure and Performance in Information and Solution Spaces
Authors:
Jesse Shore,
Ethan Bernstein,
David Lazer
Abstract:
Using data from a large laboratory experiment on problem solving in which we varied the structure of 16-person networks we investigate how an organization's network structure may be constructed to optimize performance in complex problem-solving tasks. Problem solving involves both search for information and search for theories to make sense of that information. We show that the effect of network s…
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Using data from a large laboratory experiment on problem solving in which we varied the structure of 16-person networks we investigate how an organization's network structure may be constructed to optimize performance in complex problem-solving tasks. Problem solving involves both search for information and search for theories to make sense of that information. We show that the effect of network structure is opposite for these two equally important forms of search. Dense clustering encourages members of a network to generate more diverse information, but it also has the power to discourage the generation of diverse theories: clustering promotes exploration in information space, but decreases exploration in solution space. Previous research, tending to focus on only one of those two spaces, had produced inconsistent conclusions about the value of network clustering. By adopting an experimental platform on which information was measured separately from solutions, we were able to reconcile past contradictions and clarify the effects of network clustering on performance. The finding both provides a sharper tool for structuring organizations for knowledge work and reveals the challenges inherent in manipulating network structure to enhance performance, as the communication structure that helps one aspect of problem solving may harm the other.
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Submitted 29 June, 2014;
originally announced June 2014.
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Using sociometers to quantify social interaction patterns
Authors:
Jukka-Pekka Onnela,
Benjamin N. Waber,
Alex,
Pentland,
Sebastian Schnorf,
David Lazer
Abstract:
Research on human social interactions has traditionally relied on self-reports. Despite their widespread use, self-reported accounts of behaviour are prone to biases and necessarily reduce the range of behaviours, and the number of subjects, that may be studied simultaneously. The development of ever smaller sensors makes it possible to study group-level human behaviour in naturalistic settings ou…
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Research on human social interactions has traditionally relied on self-reports. Despite their widespread use, self-reported accounts of behaviour are prone to biases and necessarily reduce the range of behaviours, and the number of subjects, that may be studied simultaneously. The development of ever smaller sensors makes it possible to study group-level human behaviour in naturalistic settings outside research laboratories. We used such sensors, sociometers, to examine gender, talkativeness and interaction style in two different contexts. Here, we find that in the collaborative context, women were much more likely to be physically proximate to other women and were also significantly more talkative than men, especially in small groups. In contrast, there were no gender-based differences in the non-collaborative setting. Our results highlight the importance of objective measurement in the study of human behaviour, here enabling us to discern context specific, gender-based differences in interaction style.
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Submitted 15 October, 2014; v1 submitted 23 May, 2014;
originally announced May 2014.
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Privacy in Sensor-Driven Human Data Collection: A Guide for Practitioners
Authors:
Arkadiusz Stopczynski,
Riccardo Pietri,
Alex Pentland,
David Lazer,
Sune Lehmann
Abstract:
In recent years, the amount of information collected about human beings has increased dramatically. This development has been partially driven by individuals posting and storing data about themselves and friends using online social networks or collecting their data for self-tracking purposes (quantified-self movement). Across the sciences, researchers conduct studies collecting data with an unprec…
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In recent years, the amount of information collected about human beings has increased dramatically. This development has been partially driven by individuals posting and storing data about themselves and friends using online social networks or collecting their data for self-tracking purposes (quantified-self movement). Across the sciences, researchers conduct studies collecting data with an unprecedented resolution and scale. Using computational power combined with mathematical models, such rich datasets can be mined to infer underlying patterns, thereby providing insights into human nature. Much of the data collected is sensitive. It is private in the sense that most individuals would feel uncomfortable sharing their collected personal data publicly. For this reason, the need for solutions to ensure the privacy of the individuals generating data has grown alongside the data collection efforts. Out of all the massive data collection efforts, this paper focuses on efforts directly instrumenting human behavior, and notes that -- in many cases -- the privacy of participants is not sufficiently addressed. For example, study purposes are often not explicit, informed consent is ill-defined, and security and sharing protocols are only partially disclosed. This paper provides a survey of the work related to addressing privacy issues in research studies that collect detailed sensor data on human behavior. Reflections on the key problems and recommendations for future work are included. We hope the overview of the privacy-related practices in massive data collection studies can be used as a frame of reference for practitioners in the field. Although focused on data collection in an academic context, we believe that many of the challenges and solutions we identify are also relevant and useful for other domains where massive data collection takes place, including businesses and governments.
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Submitted 20 March, 2014;
originally announced March 2014.
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Rising tides or rising stars?: Dynamics of shared attention on Twitter during media events
Authors:
Yu-Ru Lin,
Brian Keegan,
Drew Margolin,
David Lazer
Abstract:
"Media events" such as political debates generate conditions of shared attention as many users simultaneously tune in with the dual screens of broadcast and social media to view and participate. Are collective patterns of user behavior under conditions of shared attention distinct from other "bursts" of activity like breaking news events? Using data from a population of approximately 200,000 polit…
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"Media events" such as political debates generate conditions of shared attention as many users simultaneously tune in with the dual screens of broadcast and social media to view and participate. Are collective patterns of user behavior under conditions of shared attention distinct from other "bursts" of activity like breaking news events? Using data from a population of approximately 200,000 politically-active Twitter users, we compare features of their behavior during eight major events during the 2012 U.S. presidential election to examine (1) the impact of "media events" have on patterns of social media use compared to "typical" time and (2) whether changes during media events are attributable to changes in behavior across the entire population or an artifact of changes in elite users' behavior. Our findings suggest that while this population became more active during media events, this additional activity reflects concentrated attention to a handful of users, hashtags, and tweets. Our work is the first study on distinguishing patterns of large-scale social behavior under condition of uncertainty and shared attention, suggesting new ways of mining information from social media to support collective sensemaking following major events.
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Submitted 10 July, 2013;
originally announced July 2013.
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#Bigbirds Never Die: Understanding Social Dynamics of Emergent Hashtag
Authors:
Yu-Ru Lin,
Drew Margolin,
Brian Keegan,
Andrea Baronchelli,
David Lazer
Abstract:
We examine the growth, survival, and context of 256 novel hashtags during the 2012 U.S. presidential debates. Our analysis reveals the trajectories of hashtag use fall into two distinct classes: "winners" that emerge more quickly and are sustained for longer periods of time than other "also-rans" hashtags. We propose a "conversational vibrancy" framework to capture dynamics of hashtags based on th…
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We examine the growth, survival, and context of 256 novel hashtags during the 2012 U.S. presidential debates. Our analysis reveals the trajectories of hashtag use fall into two distinct classes: "winners" that emerge more quickly and are sustained for longer periods of time than other "also-rans" hashtags. We propose a "conversational vibrancy" framework to capture dynamics of hashtags based on their topicality, interactivity, diversity, and prominence. Statistical analyses of the growth and persistence of hashtags reveal novel relationships between features of this framework and the relative success of hashtags. Specifically, retweets always contribute to faster hashtag adoption, replies extend the life of "winners" while having no effect on "also-rans." This is the first study on the lifecycle of hashtag adoption and use in response to purely exogenous shocks. We draw on theories of uses and gratification, organizational ecology, and language evolution to discuss these findings and their implications for understanding social influence and collective action in social media more generally.
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Submitted 28 March, 2013;
originally announced March 2013.
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More Voices Than Ever? Quantifying Media Bias in Networks
Authors:
Yu-Ru Lin,
James P. Bagrow,
David Lazer
Abstract:
Social media, such as blogs, are often seen as democratic entities that allow more voices to be heard than the conventional mass or elite media. Some also feel that social media exhibits a balancing force against the arguably slanted elite media. A systematic comparison between social and mainstream media is necessary but challenging due to the scale and dynamic nature of modern communication. Her…
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Social media, such as blogs, are often seen as democratic entities that allow more voices to be heard than the conventional mass or elite media. Some also feel that social media exhibits a balancing force against the arguably slanted elite media. A systematic comparison between social and mainstream media is necessary but challenging due to the scale and dynamic nature of modern communication. Here we propose empirical measures to quantify the extent and dynamics of social (blog) and mainstream (news) media bias. We focus on a particular form of bias---coverage quantity---as applied to stories about the 111th US Congress. We compare observed coverage of Members of Congress against a null model of unbiased coverage, testing for biases with respect to political party, popular front runners, regions of the country, and more. Our measures suggest distinct characteristics in news and blog media. A simple generative model, in agreement with data, reveals differences in the process of coverage selection between the two media.
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Submitted 4 November, 2011;
originally announced November 2011.