Interoperable pipelines for social cyber-security: assessing Twitter information operations during NATO Trident Juncture 2018

Tyagi

13th ACM Web Science Conference 2021

2021

Self Cite

In this paper, we introduce a social network perspective for characterizing polarized information ecosystems. We apply our framework to a large-scale dataset of climate change conversations on Twitter. Leveraging a stance detection algorithm, we quantify the linksharing behaviors of Believers and Disbelievers of anthropogenic climate change. We generate networks of web domains based on co-sharing by Believers and Disbelievers, and we characterize these networks in terms of both structure and content. While Believers outnumber Disbelievers in our dataset, our results showed that Disbelievers are responsible for sharing over three times as many unique domains. However, for every 10% increment in a domain's proportion of Disbeliever sharers, the same domain is shared 58% less. Structurally, we observed that domain clusters associated with Believers were significantly smaller, denser, and less connected to other domains. Content-wise, we additionally found that Disbelievers were the near-exclusive sharers of majority right-wing and known fake news sources, whereas both Believers and Disbelievers shared left-wing and centrist domains. Collectively, our findings indicate that climate change Believers rely on a popular, well-consolidated, and exclusive set of mainstream web domains. In contrast, Disbelievers draw on a diverse and fragmented collection of fringe information sources. Although these results suggest the marginal status of climate change skepticism, they also troublingly point to its expansive ecosystem of online information featuring multiple entry points. We conclude with directions for future research and potential implications for science communication and climate change policymakers.

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mainstream Consensus and the Expansive Fringe: Characterizing the Polarized Information Ecosystems of Online Climate Change Discourse

Tyagi

13th ACM Web Science Conference 2021

2021

Self Cite

“…Common between the two countries, however, are a combination of hyperpartisanship among polarized publics, the rise in populist-authoritarian leadership, and sustained challenges in curbing the pandemic [1,68,81]. At the time of writing in mid-September, the US faces the largest number of COVID-19 cases in the world, while the Philippines has some of the highest in Southeast Asia [88].…”

Section: Global Hate During the Pandemic: The United States And The Pmentioning

confidence: 99%

“…For instance, observing higher levels of racial hate in the US, but not in the Philippines, made sense in relation to known societal conditions delineating the two nations [20,37,58]. While political polarization does indeed represent a serious problem for both nations [1,8,81], the multicultural setting of the US points to a fertile ground for racialized hate in a way that may not be comparable in the Philippines [5,28,44,89]. These findings also usefully show the localized boundaries of universally framed claims about the rise of racist discourse in relation to COVID-19 [73].…”

Section: Toward Global Computational Social Sciencementioning

confidence: 99%

Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines

2020

J Comput Soc Sc

Self Cite

Online hate speech represents a serious problem exacerbated by the ongoing COVID-19 pandemic. Although often anchored in real-world social divisions, hate speech in cyberspace may also be fueled inorganically by inauthentic actors like social bots. This work presents and employs a methodological pipeline for assessing the links between hate speech and bot-driven activity through the lens of social cybersecurity. Using a combination of machine learning and network science tools, we empirically characterize Twitter conversations about the pandemic in the United States and the Philippines. Our integrated analysis reveals idiosyncratic relationships between bots and hate speech across datasets, highlighting different network dynamics of racially charged toxicity in the US and political conflicts in the Philippines. Most crucially, we discover that bot activity is linked to higher hate in both countries, especially in communities which are denser and more isolated from others. We discuss several insights for probing issues of online hate speech and coordinated disinformation, especially through a global approach to computational social science.

“…Examining Twitter conversations about the pandemic in the Philippines and the United States, we formulate methods for characterizing online hate speech and the communities which propagate it over time (Uyheng et al. 2019 ). More specifically, we probe how network clusters with higher levels of hate speech systematically differ from others both in terms of structural (e.g., density, echo chambers) and functional (e.g., targeting of specific identities) features (Crenshaw 1990 ; Joseph et al.…”

Section: Introductionmentioning

confidence: 99%

Characterizing network dynamics of online hate communities around the COVID-19 pandemic

2021

Appl Netw Sci

Self Cite

Hate speech has long posed a serious problem for the integrity of digital platforms. Although significant progress has been made in identifying hate speech in its various forms, prevailing computational approaches have tended to consider it in isolation from the community-based contexts in which it spreads. In this paper, we propose a dynamic network framework to characterize hate communities, focusing on Twitter conversations related to COVID-19 in the United States and the Philippines. While average hate scores remain fairly consistent over time, hate communities grow increasingly organized in March, then slowly disperse in the succeeding months. This pattern is robust to fluctuations in the number of network clusters and average cluster size. Infodemiological analysis demonstrates that in both countries, the spread of hate speech around COVID-19 features similar reproduction rates as other COVID-19 information on Twitter, with spikes in hate speech generation at time points with highest community-level organization of hate speech. Identity analysis further reveals that hate in the US initially targets political figures, then grows predominantly racially charged; in the Philippines, targets of hate consistently remain political over time. Finally, we demonstrate that higher levels of community hate are consistently associated with smaller, more isolated, and highly hierarchical network clusters across both contexts. This suggests potentially shared structural conditions for the effective spread of hate speech in online communities even when functionally targeting distinct identity groups. Our findings bear theoretical and methodological implications for the scientific study of hate speech and understanding the pandemic’s broader societal impacts both online and offline.