259 research outputs found
Networked partisanship and framing: A socio-semantic network analysis of the Italian debate on migration
The huge amount of data made available by the massive usage of social media has opened up the unprecedented possibility to carry out a data-driven study of political processes. While particular attention has been paid to phenomena like elite and mass polarization during online debates and echo-chambers formation, the interplay between online partisanship and framing practices, jointly sustaining adversarial dynamics, still remains overlooked. With the present paper, we carry out a socio-semantic analysis of the debate about migration policies observed on the Italian Twittersphere, across the period May-November 2019. As regards the social analysis, our methodology allows us to extract relevant information about the political orientation of the communities of users—hereby called partisan communities—without resorting upon any external information. Remarkably, our community detection technique is sensitive enough to clearly highlight the dynamics characterizing the relationship among different political forces. As regards the semantic analysis, our networks of hashtags display a mesoscale structure organized in a core-periphery fashion, across the entire observation period. Taken altogether, our results point at different, yet overlapping, trajectories of conflict played out using migration issues as a backdrop. A first line opposes communities discussing substantively of migration to communities approaching this issue just to fuel hostility against political opponents; within the second line, a mechanism of distancing between partisan communities reflects shifting political alliances within the governmental coalition. Ultimately, our results contribute to shed light on the complexity of the Italian political context characterized by multiple poles of partisan alignment
All the ties that bind. A socio-semantic network analysis of Twitter political discussions
Social media play a crucial role in what contemporary sociological reflections define as a ‘hybrid
media system’. Online spaces created by social media platforms resemble global public
squares hosting large-scale social networks populated by citizens, political leaders, parties
and organizations, journalists, activists and institutions that establish direct interactions and
exchange contents in a disintermediated fashion. In the last decade, an increasing number
of studies from researchers coming from different disciplines has approached the study of the
manifold facets of citizen participation in online political spaces. In most cases, these studies
have focused on the investigation of direct relationships amongst political actors. Conversely,
relatively less attention has been paid to the study of contents that circulate during online
discussions and how their diffusion contributes to building political identities. Even more
rarely, the study of social media contents has been investigated in connection with those concerning
social interactions amongst online users. To fill in this gap, my thesis work proposes
a methodological procedure consisting in a network-based, data-driven approach to both
infer communities of users with a similar communication behavior and to extract the most
prominent contents discussed within those communities. More specifically, my work focuses
on Twitter, a social media platform that is widely used during political debates. Groups
of users with a similar retweeting behavior - hereby referred to as discursive communities -
are identified starting with the bipartite network of Twitter verified users retweeted by nonverified
users. Once the discursive communities are obtained, the corresponding semantic
networks are identified by considering the co-occurrences of the hashtags that are present in
the tweets sent by their members.
The identification of discursive communities and the study of the related semantic networks
represent the starting point for exploring more in detail two specific conversations that took
place in the Italian Twittersphere: the former occured during the electoral campaign before
the 2018 Italian general elections and in the two weeks after Election day; the latter
centered on the issue of migration during the period May-November 2019. Regarding the
social analysis, the main result of my work is the identification of a behavior-driven picture
of discursive communities induced by the retweeting activity of Twitter users, rather than
determined by prior information on their political affiliation. Although these communities
do not necessarily match the political orientation of their users, they are closely related to
the evolution of the Italian political arena. As for the semantic analysis, this work sheds light
on the symbolic dimension of partisan dynamics. Different discursive communities are, in
fact, characterized by a peculiar conversational dynamics at both the daily and the monthly
time-scale. From a purely methodological aspect, semantic networks have been analyzed by
employing three (increasingly restrictive) benchmarks. The k-shell decomposition of both
filtered and non-filtered semantic networks reveals the presence of a core-periphery structure
providing information on the most debated topics within each discursive community and
characterizing the communication strategy of the corresponding political coalition
Rising tides or rising stars?: Dynamics of shared attention on twitter during media events
"Media events" generate conditions of shared attention as many users simultaneously tune in with the dual screens of broadcast and social media to view and participate. We examine how collective patterns of user behavior under conditions of shared attention are distinct from other "bursts" of activity like breaking news events. Using 290 million tweets from a panel of 193,532 politically active Twitter users, we compare features of their behavior during eight major events during the 2012 U.S. presidential election to examine how patterns of social media use change during these media events compared to "typical" time and whether these changes are attributable to shifts in the behavior of the population as a whole or shifts from particular segments such as elites. Compared to baseline time periods, our findings reveal that media events not only generate large volumes of tweets, but they are also associated with (1) substantial declines in interpersonal communication, (2) more highly concentrated attention by replying to and retweeting particular users, and (3) elite users predominantly benefiting from this attention. These findings empirically demonstrate how bursts of activity on Twitter during media events significantly alter underlying social processes of interpersonal communication and social interaction. Because the behavior of large populations within socio-technical systems can change so dramatically, our findings suggest the need for further research about how social media responses to media events can be used to support collective sensemaking, to promote informed deliberation, and to remain resilient in the face of misinformation. © 2014 Lin et al
News and information leadership in the digital age
This paper examines information networks on social media to draw conclusions about influence relationships among members of the mass media. The project considers social networks and information patterns using Twitter data, first at the newspaper level and second at the journalist level. Using a computational approach, we look for evidence of elite-directed information flows, as well as exploring whether we find evidence of an increase in the democratization of newsmaking. This study finds that elite voices continue to dominate information networks in the digital age; however, it also finds evidence that information can move expeditiously from journalists in local and regional outlets to elite ones, and vice versa. We move further to explore the content of tweets among the journalist network, finding that there are substantial, direct interactions among elite and regional and local journalists. Our results taken together uncover new network patterns and provide a novel insight on the role of information technologies in newsmaking in the digital age
Twits, Toxic Tweets, and Tribal Tendencies: Trends in Politically Polarized Posts on Twitter
Social media platforms are often blamed for exacerbating political
polarization and worsening public dialogue. Many claim hyperpartisan users post
pernicious content, slanted to their political views, inciting contentious and
toxic conversations. However, what factors, actually contribute to increased
online toxicity and negative interactions? In this work, we explore the role
that political ideology plays in contributing to toxicity both on an individual
user level and a topic level on Twitter. To do this, we train and open-source a
DeBERTa-based toxicity detector with a contrastive objective that outperforms
the Google Jigsaw Persective Toxicity detector on the Civil Comments test
dataset. Then, after collecting 187 million tweets from 55,415 Twitter users,
we determine how several account-level characteristics, including political
ideology and account age, predict how often each user posts toxic content.
Running a linear regression, we find that the diversity of views and the
toxicity of the other accounts with which that user engages has a more marked
effect on their own toxicity. Namely, toxic comments are correlated with users
who engage with a wider array of political views. Performing topic analysis on
the toxic content posted by these accounts using the large language model MPNet
and a version of the DP-Means clustering algorithm, we find similar behavior
across 6,592 individual topics, with conversations on each topic becoming more
toxic as a wider diversity of users become involved
Just Another Day on Twitter: A Complete 24 Hours of Twitter Data
At the end of October 2022, Elon Musk concluded his acquisition of Twitter.
In the weeks and months before that, several questions were publicly discussed
that were not only of interest to the platform's future buyers, but also of
high relevance to the Computational Social Science research community. For
example, how many active users does the platform have? What percentage of
accounts on the site are bots? And, what are the dominating topics and
sub-topical spheres on the platform? In a globally coordinated effort of 80
scholars to shed light on these questions, and to offer a dataset that will
equip other researchers to do the same, we have collected all 375 million
tweets published within a 24-hour time period starting on September 21, 2022.
To the best of our knowledge, this is the first complete 24-hour Twitter
dataset that is available for the research community. With it, the present work
aims to accomplish two goals. First, we seek to answer the aforementioned
questions and provide descriptive metrics about Twitter that can serve as
references for other researchers. Second, we create a baseline dataset for
future research that can be used to study the potential impact of the
platform's ownership change
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