Same wavelength group identification from online social networks: A general framework

Reacting to social issues or events through Online Social Networks has become a social habit. Social scientists have identified several network relationships and dimensions that induce homophily. Sentiments or opinions towards different issues have been observed as a key dimension which characterizes human behaviour. People usually express their sentiments towards various issues. Different persons from different walks of social life may share same opinion towards various issues. When these persons constitute a group, such groups can be conveniently termed same wavelength groups. We propose a novel framework based on sentiments and an algorithm to identify such same wavelength groups from online social networks like twitter. The proposed algorithm generates same wavelength groups in polynomial time for relatively small set of events. The analysis of such groups would be of help in unravelling their response patterns and behavioural features.

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How social learning amplifies moral outrage expression in online social networks

10.31234/osf.io/gf7t5 ◽

2021 ◽

Author(s):

William J. Brady ◽

Killian Lorcan McLoughlin ◽

Tuan Nguyen Doan ◽

Molly Crockett

Keyword(s):

Social Networks ◽

Social Learning ◽

Online Social Networks ◽

Social Life ◽

Learning Processes ◽

Moral Outrage ◽

Social Feedback ◽

Learning Mechanisms ◽

Behavioral Experiments ◽

Social Learning Processes

Moral outrage shapes fundamental aspects of human social life and is now widespread in online social networks. Here, we show how social learning processes amplify online moral outrage expressions over time. In two pre-registered observational studies of Twitter (7,331 users and 12.7 million total tweets) and two pre-registered behavioral experiments (N = 240), we find that positive social feedback for outrage expressions increases the likelihood of future outrage expressions, consistent with principles of reinforcement learning. We also find that outrage expressions are sensitive to expressive norms in users’ social networks, over and above users’ own preferences, suggesting that norm learning processes guide online outrage expressions. Moreover, expressive norms moderate social reinforcement of outrage: in ideologically extreme networks, where outrage expression is more common, users are less sensitive to social feedback when deciding whether to express outrage. Our findings highlight how platform design interacts with human learning mechanisms to impact moral discourse in digital public spaces.

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Análisis de la dinámica, la estructura y el contenido de los mensajes de Twitter: violencia sexual en #Cuéntalo

Empiria Revista de metodología de ciencias sociales ◽

10.5944/empiria.53.2022.32614 ◽

2022 ◽

Author(s):

Modesto Escobar ◽

Elena Gil Moreno ◽

Cristina Calvo López

Keyword(s):

Social Networks ◽

Content Analysis ◽

Sexual Violence ◽

Online Social Networks ◽

Public Information ◽

Redes Sociales ◽

Social Scientists ◽

New Type ◽

Shared Narrative

Las redes sociales online se han ido convirtiendo en uno de los principales vehículos de comunicación y una de las mayores fuentes de información de actualidad. Esta creciente popularidad deja en evidencia la importancia de que los científicos sociales seamos capaces de analizar, interpretar y comprender en profundidad este nuevo tipo de herramientas. Este artículo tiene como objetivo mostrar los diversos métodos de análisis de la información pública obtenida a partir de una de estas redes, Twitter. Para ello tomamos como ejemplificación explicativa el caso #Cuéntalo, un episodio de narrativa compartida iniciado en esta red entre los días 26 y 28 de abril de 2018 tras la conocida sentencia de “La Manada”. A través de este caso se presentan aquí distintas metodologías para el estudio de los contenidos transmitidos, que van desde los análisis descriptivos más elementales hasta los análisis de contenido, pasando por la clasificación de actores relevantes y el descubrimiento de la estructura de las relaciones entre los protagonistas y sus mensajes. Los resultados muestran cómo esta polémica sentencia derivó en una conversación digital viral donde distintas usuarias (en especial periodistas, escritoras y activistas feministas) comenzaron a compartir sus relatos de situaciones de violencia sexual vividas por las participantes o sus conocidas usando esta etiqueta, siendo capaces de identificar a las principales protagonistas, las distintas relaciones que establecieron entre ellas y sus mensajes y los principales temas que se conformaron en torno a ellos. Online social networks have become one of the main communication vehicles and one of the greatest sources of current information. This growing popularity shows the importance of social scientists being able to analyze, interpret and understand in depth this new type of tools. This article aims to show the diverse methods of analysis of public information obtained from one of these networks, Twitter. To do this, we take as an explanatory example the case of #Cuéntalo, an episode of shared narrative that began on this network between April 26 and 28, 2018 after the well-known sentence of “La Manada”. Through this case, we present different methodologies for the study of broadcasted content, ranging from the most elementary descriptive tools to content analysis, passing through the classification of relevant actors and the discovery of the structure of the relationships amongst their protagonists and their messages. The results show how this controversial sentence led to a viral digital conversation where different users (especially journalists, writers, feminists and influencers) began to share their stories of situations of sexual violence experienced by the participants or their acquaintances using this label. Through this analysis, it was possible to identify the main protagonists, the different relationships that they established between them and their messages and the main themes that were formed around them.

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A review of Facebook research in the social sciences

10.31219/osf.io/2r92d ◽

2016 ◽

Author(s):

Robert Edmund Wilson ◽

Samuel D. Gosling

Keyword(s):

Online Social Networks ◽

Social Life ◽

Information Disclosure ◽

Future Research ◽

Social Scientists ◽

The Social ◽

Naturalistic Setting ◽

The Impact ◽

Share Information

With over 800 million active users, Facebook is changing the way hundreds of millions of people relate to one another and share information. A rapidly growing body of research has accompanied the meteoric rise of Facebook as social scientists assess the impact of Facebook on social life. In addition, researchers have recognized the utility of Facebook as a novel tool to observe behavior in a naturalistic setting, test hypotheses, and recruit participants. However, research on Facebook emanates from a wide variety of disciplines, with results being published in a broad range of journals and conference proceedings, making it difficult to keep track of various findings. And because Facebook is a relatively recent phenomenon, uncertainty still exists about the most effective ways to do Facebook research. To address these issues, the authors conducted a comprehensive literature search, identifying 412 relevant articles, which were sorted into 5 categories: descriptive analysisof users, motivations for using Facebook, identity presentation, the role of Facebook in social interactions, and privacy and information disclosure. The literature review serves as the foundation from which to assess current findings and offer recommendations to the field for future research on Facebook and online social networks more broadly.

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A Comparison of Online Social Networks and Real-Life Social Networks: A Study of Sina Microblogging

Mathematical Problems in Engineering ◽

10.1155/2014/578713 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 4

Author(s):

Dayong Zhang ◽

Guang Guo

Keyword(s):

Social Networks ◽

Structural Properties ◽

Interpersonal Relationships ◽

Online Social Networks ◽

Social Life ◽

Low Cost ◽

Online Social Network ◽

Real Life ◽

Human Communication ◽

Cognitive Constraints

Online social networks appear to enrich our social life, which raises the question whether they remove cognitive constraints on human communication and improve human social capabilities. In this paper, we analyze the users' following and followed relationships based on the data of Sina Microblogging and reveal several structural properties of Sina Microblogging. Compared with real-life social networks, our results confirm some similar features. However, Sina Microblogging also shows its own specialties, such as hierarchical structure and degree disassortativity, which all mark a deviation from real-life social networks. The low cost of the online network forms a broader perspective, and the one-way link relationships make it easy to spread information, but the online social network does not make too much difference in the creation of strong interpersonal relationships. Finally, we describe the mechanisms for the formation of these characteristics and discuss the implications of these structural properties for the real-life social networks.

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How social learning amplifies moral outrage expression in online social networks

Science Advances ◽

10.1126/sciadv.abe5641 ◽

2021 ◽

Vol 7 (33) ◽

pp. eabe5641

Author(s):

William J. Brady ◽

Killian McLoughlin ◽

Tuan N. Doan ◽

Molly J. Crockett

Keyword(s):

Social Networks ◽

Reinforcement Learning ◽

Social Learning ◽

Online Social Networks ◽

Social Life ◽

Moral Outrage ◽

Social Feedback ◽

Learning Mechanisms ◽

Behavioral Experiments ◽

Social Learning Processes

Moral outrage shapes fundamental aspects of social life and is now widespread in online social networks. Here, we show how social learning processes amplify online moral outrage expressions over time. In two preregistered observational studies on Twitter (7331 users and 12.7 million total tweets) and two preregistered behavioral experiments (N = 240), we find that positive social feedback for outrage expressions increases the likelihood of future outrage expressions, consistent with principles of reinforcement learning. In addition, users conform their outrage expressions to the expressive norms of their social networks, suggesting norm learning also guides online outrage expressions. Norm learning overshadows reinforcement learning when normative information is readily observable: in ideologically extreme networks, where outrage expression is more common, users are less sensitive to social feedback when deciding whether to express outrage. Our findings highlight how platform design interacts with human learning mechanisms to affect moral discourse in digital public spaces.

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