Quantifying Controversy in Social Media (1507.05224v5)

Abstract: Which topics spark the most heated debates on social media? Identifying those topics is not only interesting from a societal point of view, but also allows the filtering and aggregation of social media content for disseminating news stories. In this paper, we perform a systematic methodological study of controversy detection by using the content and the network structure of social media. Unlike previous work, rather than study controversy in a single hand-picked topic and use domain specific knowledge, we take a general approach to study topics in any domain. Our approach to quantifying controversy is based on a graph-based three-stage pipeline, which involves (i) building a conversation graph about a topic; (ii) partitioning the conversation graph to identify potential sides of the controversy; and (iii) measuring the amount of controversy from characteristics of the graph. We perform an extensive comparison of controversy measures, different graph-building approaches, and data sources. We use both controversial and non-controversial topics on Twitter, as well as other external datasets. We find that our new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy, and show that content features are vastly less helpful in this task.

• The paper presents a framework that quantifies controversy on social media by analyzing user interaction graphs with novel metrics including RWC, BCC, and EC.
• It employs random walk and centrality measures to effectively distinguish polarized topics across diverse datasets.
• The study highlights practical insights for tracking controversy dynamics over time, with implications for mitigating echo chambers and guiding balanced content recommendations.

Analyzing Controversy on Social Media: Methodologies and Implications

The paper, "Quantifying Controversy on Social Media," by Garimella et al., embarks on a comprehensive exploration of controversy detection across various social media platforms, with a concentrated focus on Twitter. The paper presents a framework engineered to identify and quantify controversy devoid of reliance on specific domain knowledge, thus proposing a generalist approach applicable across multiple domains including political, cultural, and economic areas.

The research pivots around systematically analyzing the conversation graph of a given topic. These graphs are constructed with vertices representing users and edges denoting interaction types such as retweets, mentions, or endorsements. Garimella et al. propose that controversy is encapsulated within the structural characteristics of these graphs. Controversial topics exhibit distinctively clustered structures corresponding to the differing sides of an argument.

Several novel measures are introduced to quantify controversy:

1. Random Walk Controversy (RWC): This measure utilizes random walk probabilities across high-degree vertices within topic-related conversation graphs, evidencing pronounced discriminatory power for controversial topics.
2. Betweenness Centrality Controversy (BCC): This metric calculates the divergence in edge betweenness centrality between boundary-crossing edges and internal edges to detect controversies.
3. Embedding Controversy (EC): This measure leverages low-dimensional graph embeddings to estimate topic separation, drawing inspiration from the Davies-Bouldin index.

The empirical evaluation spans multiple datasets sourced from Twitter and other platforms, indicating the robustness of the proposed framework and the effectiveness of random-walk-based RWC in distinguishing controversial topics. The paper's pipeline, encapsulating graph construction, partitioning, and controversy quantification, provides a rigorous yet efficient methodology capable of integrating various data and interaction patterns beyond Twitter, as demonstrated in their experiments.

Furthermore, the paper pursues the evolution of controversy over time, exemplifying its practical utility in real-world deployments for controversy tracking and recommendation systems aimed at balancing user viewpoints. Such research underscores the potential of these methodologies in mitigating echo chambers and polarization in social discourse, highlighting feasible pathways to reduce societal polarization.

Nonetheless, the present work's focus is predominantly on two-sided controversies and heavily reliant on Twitter as a use-case scenario. Addressing controversies with multiple factions or adapting to platforms with differing interaction dynamics might form the basis of further inquiry.

In future advancements, exploring the temporal dynamics of controversy more deeply and harnessing such insights for real-time interventions in media curation, or broadening the applicability to other digital communication platforms, could widely extend the utility of this research. Additionally, refining these methodologies for improved detection accuracy and computational efficiency remains promising for its potential deployment in large-scale social media systems.

Overall, this paper makes substantive contributions to the computational understanding of social media dynamics, with important implications for the analysis of digital interactions and the development of systems fostering informed and balanced discourse.