Users structure and behavior on an online social network during a political protest

Morales, Alfredo Jose; Losada, Juan Carlos; Benito, R. M.

doi:10.1016/j.physa.2012.05.015

Cited by 51 publications

(36 citation statements)

References 15 publications

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“…Furthermore, the ANTI1 network has the shortest network diameter (16 vs. 19 in PRO1 and PRO2, and 21 in ANTI2, see Table ), signifying that on average information might spread faster in the social network among ANTI1 group members than in the other three networks. This finding is similar to the previous study by Morales et al () who found that highly connected online users significantly reduced the distance between group members and increased the speed of information flow during the 2010 #SOSInternetVE Twitter protest in Venezuela.…”

Section: Pairwise Comparison Of Groups Across the Political Dividesupporting

confidence: 91%

Information Wars and Online Activism During the 2013/2014 Crisis in Ukraine: Examining the Social Structures of Pro‐ and Anti‐Maidan Groups

Gruzd¹,

Tsyganova²

2015

Policy & Internet

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This article examines how online groups are formed and sustained during crisis periods, especially when political polarization in society is at its highest level. We focus on the use of Vkontakte (VK), a popular social networking site in Ukraine, to understand how it was used by Pro-and AntiMaidan groups during the 2013/2014 crisis in Ukraine. In particular, we ask whether and to what extent the ideology (or other factors) of a particular group shapes its network structure. We find some support that online social networks are likely to represent local and potentially preexisting social networks, likely due to the dominance of reciprocal (and often close) relationships on VK and opportunities for group members to meet face-to-face during offline protests. We also identify a number of group-level indicators, such as degree centralization, modularity index and average engagement level, that could help to classify groups based on their network properties. Community researchers can start applying these group-level indicators to online communities outside VK; they can also learn from this article how to identify networks of spam and marketing accounts.

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Section: Pairwise Comparison Of Groups Across the Political Dividesupporting

confidence: 91%

Information Wars and Online Activism During the 2013/2014 Crisis in Ukraine: Examining the Social Structures of Pro‐ and Anti‐Maidan Groups

Gruzd¹,

Tsyganova²

2015

Policy & Internet

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“…We collected up to 421,602 messages, identified with the protest hashtag, which were posted by 77,706 users, between Dec. [14][15][16][17][18][19]2010 (two days before and after the protest). In our previous work [26], we found that some influential users acted as information producers, providing messages that are received by the passive large majority of information consumers. Besides, we found that users are organized in a community structure around hubs of different nature, like politicians, humorists or mass media accounts.…”

Section: Datasetsmentioning

confidence: 90%

Efficiency of human activity on information spreading on Twitter

et al. 2014

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Understanding the collective reaction to individual actions is key to effectively spread information in social media. In this work we define efficiency on Twitter, as the ratio between the emergent spreading process and the activity employed by the user. We characterize this property by means of a quantitative analysis of the structural and dynamical patterns emergent from human interactions, and show it to be universal across several Twitter conversations. We found that some influential users efficiently cause remarkable collective reactions by each message sent, while the majority of users must employ extremely larger efforts to reach similar effects. Next we propose a model that reproduces the retweet cascades occurring on Twitter to explain the emergent distribution of the user efficiency. The model shows that the dynamical patterns of the conversations are strongly conditioned by the topology of the underlying network. We conclude that the appearance of a small fraction of extremely efficient users results from the heterogeneity of the followers network and independently of the individual user behavior.

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“…Sin embargo, viendo al detalle algunos de estos acontecimientos -principalmente el movimiento #YoSoy132 en México-, y a la luz de algunos estudios relacionados (Cha et al 2010;Wu et al 2011;Morales et al 2012) podemos plantear que no hay tal horizontalidad a la hora de la "deliberación" sobre los asuntos públicos. Al respecto de la deliberación y, en general, de la acción colectiva, algunos autores importantes en el estudio tanto del capital social (Putnam 2000;Castells 1996;Van alstyne y Brynjolfsson 1997;Bimber 1998;Shapiro & Leone 1999;y Sunstein 2001) como de los movimientos sociales (Diani 2000;aldon & Staggenborg 2004;González-Bailón & Ning 2013), han estado poniendo en duda la deliberación mediante redes digitales, enfatizando sobre todo en que el impacto de las mismas está determinado por el contexto, el tipo de organización y el tipo de lazos que se establecen en la estructura de red.…”

Section: Las Redes Sociales De Internet Y La Falacia De La Horizontalunclassified

¿Quién programa las redes sociales en Internet? El caso de Twiter en el movimiento <em>#Yosoy132</em> México

Nabel¹

2015

Rev. int. sociol.

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