Quantifying Controversy in Social Media

@article{Garimella2016QuantifyingCI,
  title={Quantifying Controversy in Social Media},
  author={Venkata Rama Kiran Garimella and Gianmarco De Francisci Morales and A. Gionis and Michael Mathioudakis},
  journal={Proceedings of the Ninth ACM International Conference on Web Search and Data Mining},
  year={2016}
}
Which topics spark the most heated debates in social media? Identifying these topics is a first step towards creating systems which pierce echo chambers. In this paper, we perform a systematic methodological study of controversy detection using social media network structure and content. Unlike previous work, rather than identifying controversy in a single hand-picked topic and use domain-specific knowledge, we focus on comparing topics in any domain. Our approach to quantifying controversy is… 

Figures and Tables from this paper

Quantifying Controversy on Social Media
TLDR
A systematic methodological study of controversy detection by using the content and the network structure of social media and a new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy and shows that content features are vastly less helpful in this task.
antifying Controversy on Social Media
TLDR
A systematic methodological study of controversy detection by using the content and the network structure of social media, and finds that a new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy.
Explaining Controversy on Social Media via Stance Summarization
TLDR
This paper focuses on Twitter and treats the stance summarization as a ranking problem of finding the top k tweets that best summarize the two conflicting stances of a controversial topic, and formalizes the characteristics of a good stance summary and proposes a ranking model accordingly.
Measuring the controversy level of Arabic trending topics on Twitter
TLDR
This work collects a large dataset of tweets on different trending topics from different domains and applies several approaches for controversy detection and compares their outcomes to determine which one is the most consistent measure.
Exposing Twitter Users to Contrarian News
TLDR
The demo provides one of the first steps in developing automated tools that help users explore, and possibly escape, their echo chambers and expose users to information which presents a contrarian point of view.
The Effect of Collective Attention on Controversial Debates on Social Media
TLDR
This work is the first to study the dynamic evolution of polarized online debates at such scale and finds consistent evidence that increased collective attention is associated with increased network polarization and network concentration within each side of the debate.
Mary, Mary, Quite Contrary: Exposing Twitter Users to Contrarian News
TLDR
The demo provides one of the first steps in developing automated tools that help users explore, and possibly escape, their echo chambers by exposing users to information which presents a contrarian point of view.
Do the Communities We Choose Shape our Political Beliefs? A Study of the Politicization of Topics in Online Social Groups
TLDR
Comment data from approximately 3,300 message boards on Reddit is analyzed to provide novel empirical knowledge about how group topic informs political bias in Reddit sub-communities, suggesting fairly strong correlations between group topic and politically biased language in communities.
Modeling Controversy within Populations
TLDR
A multi-dimensional model of controversy is proposed, viewing controversy as a trait rooted in population and suggesting that controversy should be separately observed in a given population, rather than held as a fixed universal quantity.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 46 REFERENCES
Quantifying Controversy on Social Media
TLDR
A systematic methodological study of controversy detection by using the content and the network structure of social media and a new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy and shows that content features are vastly less helpful in this task.
BiasWatch: A Lightweight System for Discovering and Tracking Topic-Sensitive Opinion Bias in Social Media
TLDR
An efficient optimization-based opinion bias propagation method over the social/information network is developed, which leads to a 20% accuracy improvement versus a next-best alternative for bias estimation, as well as uncovering the opinion leaders and evolving themes associated with these topics.
Political Polarization on Twitter
TLDR
It is demonstrated that the network of political retweets exhibits a highly segregated partisan structure, with extremely limited connectivity between left- and right-leaning users, and surprisingly this is not the case for the user-to-user mention network, which is dominated by a single politically heterogeneous cluster of users.
A Measure of Polarization on Social Media Networks Based on Community Boundaries
TLDR
A systematic comparison between social networks that arise from both polarized and non-polarized contexts shows that the traditional polarization metric -modularity - is not a direct measure of antagonism between groups, and proposes a novel polarization metric based on the analysis of the boundary of a pair of (potentially polarized) communities, which better captures the notions of antagonist and polarization.
Controversy and Sentiment in Online News
TLDR
This work takes a data-driven approach to understand how controversy interplays with emotional expression and biased language in the news, and shows that one can indicate to what extent an issue is controversial, by comparing it with other issues in terms of how they are portrayed across different media.
Exposure to ideologically diverse news and opinion on Facebook
TLDR
Examination of the news that millions of Facebook users' peers shared, what information these users were presented with, and what they ultimately consumed found that friends shared substantially less cross-cutting news from sources aligned with an opposing ideology.
Exploring Controversy in Twitter
TLDR
Among the topics discussed on social media, some spark more heated debate than others, and understanding which ones are controllable is extremely useful for a variety of purposes, such as for journalists to understand what issues divide the public, or for social scientists to understand how controversy is manifested in social interactions.
Quantifying Political Polarity Based on Bipartite Opinion Networks
TLDR
This paper proposes a linear algorithm that exploits network effects to learn both the polarity labels as well as the rankings of people and issues in a completely unsupervised manner and provides an effective, fast, and easy-to-implement solution.
Measuring Political Polarization: Twitter shows the two sides of Venezuela
TLDR
It is shown that the proposed methodology can detect different degrees of polarization, depending on the structure of the network, and an index is proposed to quantify the extent to which the resulting distribution is polarized.
Data Portraits: Connecting People of Opposing Views
TLDR
The results suggest that organic visualization design may revert the negative effects of providing potentially sensitive content, and introduce a paradigm to present a data portrait of users, in which their characterizing topics are visualized and their corresponding tweets are displayed using an or-ganic design.
...
1
2
3
4
5
...