1,720,980 research outputs found
Trends and topics: Characterizing echo chambers’ topological stability and in-group attitudes
Full text linkNowadays, online debates focusing on a wide spectrum of topics are often characterized by clashes of polarized communities, each fiercely supporting a specific stance. Such debates are sometimes fueled by the presence of echo chambers, insulated systems whose users’ opinions are exacerbated due to the effect of repetition and by the active exclusion of opposite views. This paper offers a framework to explore how echo chambers evolve through time, considering their users’ interaction patterns and the content/attitude they convey while addressing specific controversial issues. The framework is then tested on three Reddit case studies focused on sociopolitical issues (gun control, American politics, and minority discrimination) during the first two years and a half of Donald Trump’s presidency and on an X/Twitter dataset involving BLM discussion tied to the EURO 2020 football championship. Analytical results unveil that polarized users will likely keep their affiliation to echo chambers in time. Moreover, we observed that the attitudes conveyed by Reddit users who joined risky epistemic enclaves are characterized by a slight inclination toward a more negative or neutral attitude when discussing particularly sensitive issues (e.g., fascism, school shootings, or police violence) while X/Twitter ones often tend to express more positive feelings w.r.t. those involved into less polarized communities
Towards a Social Artificial Intelligence
No full textArtificial Intelligence can both empower individuals to face complex societal challenges and exacerbate problems and vulnerabilities, such as bias, inequalities, and polarization. For scientists, an open challenge is how to shape and regulate human-centered Artificial Intelligence ecosystems that help mitigate harms and foster beneficial outcomes oriented at the social good. In this tutorial, we discuss such an issue from two sides. First, we explore the network effects of Artificial Intelligence and their impact on society by investigating its role in social media, mobility, and economic scenarios. We further provide different strategies that can be used to model, characterize and mitigate the network effects of particular Artificial Intelligence driven individual behavior. Secondly, we promote the use of behavioral models as an addition to the data-based approach to get a further grip on emerging phenomena in society that depend on physical events for which no data are readily available. An example of this is tracking extremist behavior in order to prevent violent events. In the end, we illustrate some case studies in-depth and provide the appropriate tools to get familiar with these concepts.</p
Participant behavior and community response in online mental health communities: Insights from Reddit
No full textThe growing presence of online mutual-help communities has significantly changed how people access and provide mental health (MH) support. While extensive research has explored self-disclosure and social support dynamics within these communities, less is known about users’ distinctive behavioral patterns, posting intents, and community response. This study analyzed a large-scale, five-year Reddit dataset of 67 MH-related subreddits, comprising over 3.4 million posts and 24 million comments from approximately 2.4 million users. We categorized subreddits based on the Diagnostic and Statistical Manual of Mental Disorders and compared the behavioral patterns in these communities with Reddit non-MH ones. Leveraging Reddit's post flair feature, we defined a ground truth for post intents and applied an automated classification method to infer intents across the dataset. We then used causal inference analysis to assess the effect of community responses on subsequent user behavior. Our analysis revealed that MH-related subreddits featured unique characteristics in content length, throwaway account usage, user actions, persistence, and community response. These online behaviors mirrored those in other mutual-help Reddit communities and resonated with offline patterns while diverging from non-support-oriented subreddits. We also found that seeking support and venting are the predominant posting intents, with users tending to maintain consistent intents over time. Furthermore, we observed that receiving comments and reactions significantly influenced users’ follow-up engagement, fostering increased participation. These findings highlight the supportive role of online MH communities and emphasize the need for tailored design to optimize user experience and support for individuals facing MH challenges
Change my mind : data driven estimate of open-mindedness from political discussions
Full text linkOne of the main dimensions characterizing the unfolding of opinion formation processes in social debates is the degree of open-mindedness of the involved population. Opinion dynamic modeling studies have tried to capture such a peculiar expression of individuals' personalities and relate it to emerging phenomena like polarization, radicalization, and ideology fragmentation. However, one of their major limitations lies in the strong assumptions they make on the initial distribution of such characteristics, often fixed so as to satisfy a normality hypothesis. Here we propose a data-driven methodology to estimate users' openmindedness from online discussion data. Our analysis-focused on the political discussion taking place on Reddit during the first two years of the Trump presidency-unveils the existence of statistically diverse distributions of open-mindedness in annotated sub-populations (i.e., Republicans, Democrats, and Moderates/Neutrals). Moreover, such distributions appear to be stable across time and generated by individual users' behaviors that remain consistent and underdispersed.One of the main dimensions characterizing the unfolding of opinion formation processes in social debates is the degree of open-mindedness of the involved population. Opinion dynamic modeling studies have tried to capture such a peculiar expression of individuals’ personalities and relate it to emerging phenomena like polarization, radicalization, and ideology fragmentation. However, one of their major limitations lies in the strong assumptions they make on the initial distribution of such characteristics, often fixed so as to satisfy a normality hypothesis. Here we propose a data-driven methodology to estimate users’ open-mindedness from online discussion data. Our analysis—focused on the political discussion taking place on Reddit during the first two years of the Trump presidency—unveils the existence of statistically diverse distributions of open-mindedness in annotated sub-populations (i.e., Republicans, Democrats, and Moderates/Neutrals). Moreover, such distributions appear to be ..
Whose Voice Matters? Authority and Influence in the Italian Twitter Debates on Covid-19
No full textThe Covid-19 pandemic intensified public discourse on social media, with Twitter becoming a key platform for information exchange. In such environments, authorities—influential figures from various domains—play a crucial role in shaping public opinion, having the power to influence offline behaviors both individually and collectively. In this work, we study the role of pro-vaccine and anti-vaccine authorities within the Italian Twitter debate on Covid-19 in five contextually relevant temporal windows corresponding to different pandemic phases. Analyzing a dataset of over ∼50M tweets, we identify central actors and quantify both their impact and their influence on users’ opinions. Our results suggest that while anti-vax authorities were able to gain more consensus during the vaccination phases, pro-vax authorities became more influential in the latter stage of the vaccination campaign
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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