Tweeting From Left to Right

By Pablo Barberá, John T. Jost, Jonathan Nagler, Joshua A. Tucker, Richard Bonneau. 2015

1,025 citations recorded by the source corpus.

Subfields: Communication, Sociology and Political Science, Statistical and Nonlinear Physics, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Communication

In Sociology and Political Science

In Statistical and Nonlinear Physics

In Natural Language Processing

Citations in the corpus

Degenerate Feedback Loops in Recommender Systems (2019, background): “Barber´a et al. (2015) also presented evidence of echo chamber related to political issues on Twitter.”
Stance Detection on Social Media: State of the Art and Trends (2020, background): “Previous studies have concluded that social media are polarized in nature which boost homophily behavior [Barberá et al. 2015; Bessi et al. 2016; Quattrociocchi et al. 2016; Darwish et al. 2017a; Garimella et al. 2018].”
Partisan Polarization Is the Primary Psychological Motivation behind Political Fake News Sharing on Twitter (2020, background): “In general, partisans seem to share information from ideologically similar sources (Barberá et al. 2015) and to treat articles shared by political opponents with greater suspicion (Lee, Kim, and Coe 2018), suggesting that considerations about the political usefulness of information matter.”
Educative Interventions to Combat Misinformation: Evidence from a Field Experiment in India (2020, result): “context show a monotonically increasing age function with respect to vulnerability to misinformation (Barberá et al. 2015), this is contrary to what I find.”
Political Attitudes and the Processing of Misinformation Corrections (2018, background): “…conservative traits such as perceived threat, ambiguity intolerance, and closed-mindedness may foster resistance to attitude-dissonant retractions (Barberá et al., 2015; Jost et al., 2003; Jost, 2017; Lewandowsky et al., 2005; Nam et al., 2013; Nyhan & Reifler, 2010; Prasad et al., 2009; Vraga,…”
The MAD Model of Moral Contagion: The Role of Motivation, Attention, and Design in the Spread of Moralized Content Online (2019, background): “Whether it is political discussions that resemble echo chambers and often highlight political group differences (Barberá et al., 2015; Brady et al., 2017), political leaders who disseminate partisan content (Brady, Wills, et al., 2019), or the viral spread of fake political news (Lazer et al.,…”
Uncertain Self in a Changing World: A Foundation for Radicalisation, Populism, and Autocratic Leadership (2020, background): “Means not sharing a superscript differ significantly as a simple main effect. can largely choose their own on-line “echo chamber” as a source of information to validate their own worldview and social identity (cf. Barberá et al., 2015; Colleoni et al., 2014; Peters et al., 2010).”
Emotion shapes the diffusion of moralized content in social networks (2017, methodology): “” Given past research suggesting that political conservatives may possess more homophilous online social networks than liberals (32, 34), we also explored whether the in-group advantage for moralemotional language would be greater in conservative (vs.”