Identifying the Popularity and Persuasiveness of Right- and Left-Leaning Group Videos on Social Media

Lin, Aiming; Kathuria, Anika; Panda, Subhadarshi; Sahai, Arushi; Yu, Yuwen; Levitan, Sarah Ita; Hirschberg, Julia

doi:10.1109/bigdata52589.2021.9671635

Cited by 2 publications

(5 citation statements)

References 12 publications

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“…These actions directly impact social justice and governmental initiatives in the contemporary United States. Oath Keepers have been identified as a group that encouraged defiance of COVID-19 pandemic orders (MacNab, 2020) in addition to government standoffs and commentary related to the murder of George Floyd (Ai et al, 2021). Thus, “[i]t is highly problematic from a criminal justice perspective when law enforcement … are involved with these forms of far right activity, [because v]igilantism and the concept of ‘taking justice into your own hands’ run counter to the concept of a rule of law” (Dickinson, 2021).…”

Section: Discussionmentioning

confidence: 99%

COVID-19, Diffuse Anxiety, and Public (Mis)Trust in Government: Empirical Insights and Implications for Crime and Justice

Reid

Brown

Dmello

2023

Criminal Justice Review

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Given that trust in government is a critical feature of a well-functioning democracy, research into its determinants has long been a priority among public opinion scholars. The consensus in the literature is that short-term factors drive the ebbs and flows of public trust, and a climate of mistrust has significant consequences on the government's ability to deliver on policies and enforce the law. Despite decades of extensive research on public trust, changing circumstances related to the COVID-19 pandemic support the need to investigate the factors shaping trust in this distinct period. This article, using data from the American National Election Study, explores how economic, social, and political anxieties pervasive throughout the pandemic influence trust in the United States government. Findings from ordered logistic regression analyses indicate that public trust in government is associated with views of the government's COVID-19 response, beliefs about the state of the country and government corruption, economic anxieties, and concerns about election fraud and the status of American democracy. Findings also reveal that sentiments toward institutions—including the police and the Center for Disease Control—contribute to variability in public trust. The implications of these findings for criminal justice research and policy are also considered.

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Section: Discussionmentioning

confidence: 99%

COVID-19, Diffuse Anxiety, and Public (Mis)Trust in Government: Empirical Insights and Implications for Crime and Justice

Reid

Brown

Dmello

2023

Criminal Justice Review

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“…In the same context, Kathuria et al [70] analyzed the metadata of YouTube videos statistically and performed classification using ML algorithms to detect the stance of the videos. The author made use of a dataset that included the metadata for more than 31k videos from left-and RW (rightwing) groups on several video-streaming websites, including YouTube, Vimeo, and 4chan.…”

Section: B Detection and Prevention Methodsmentioning

confidence: 99%

“…Faddoul et al [27] collected 1146 seed data channels related to conspiracy theories and generated their up-next video recommendations. Kathuria et al [70] used YouTube, Vimeo, and 4chan platforms to collect comments, as well as the number of likes and views for videos from left-wing channels like Antifa and right-wing groups like Proud Boys. Some studies utilized public datasets in their work.…”

Section: A Dataset and Its Sourcesmentioning

confidence: 99%

“…Nouh et al in [77] used Dabiq to collect English articles related to propaganda. [88] and [70]] used both magazines to perform classifications of articles. Some websites publish publicly accessible databases and datasets.…”

Section: Blog and Forumsmentioning

confidence: 99%

“…Study Approach Dataset Type Mitigation Techniques 2023 [79] ML Imbalanced Na 2023 [101] Graph Imbalanced Na 2022 [21] ML,DL Imbalanced SMOTE 2022 [23] ML Balanced Na 2022 [22] ML,DL Balanced Na 2022 [61] ML Balanced Na 2022 [26] ML Imbalanced SMOTE 2022 [106] DL Imbalanced Na 2022 [28] DL Imbalanced weighted loss function 2022 [107] DL Balanced Na 2022 [108] DL Imbalanced oversampling, undersampling 2022 [110] Clustering Imbalanced SMOTE 2022 [114] Analysis Balanced Na 2022 [65] Analysis Balanced Na 2022 [98] Graph Balanced Na 2022 [19] Graph Imbalanced Na 2021 [60] ML Imbalanced undersampling 2021 [70] ML Imbalanced Na 2021 [85] DL Imbalanced oversampling, undersampling 2021 [79] DL Imbalanced oversampling, undersampling 2021 [115] Analysis Imbalanced weighted sampling 2021 [67] Analysis Imbalanced Na 2021 [118] Analysis Na Na 2021 [96] Graph Imbalanced Na 2021 [20] Graph Balanced Na 2021 [99] Graph Imbalanced oversampling 2020 [54] ML Imbalanced oversampling, undersampling 2020 [55] ML Imbalanced oversampling, undersampling 2020 [24] ML Imbalanced Na 2020 [91] ML Imbalanced SMOTE 2020 [58] ML Imbalanced SMOTE 2020 [81] ML Imbalanced SMOTE 2020 [27] ML Balanced Na 2020 [116] Analysis Imbalanced SMOTE 2020 [57] ML Imbalanced oversampling , undersampling 2019 [59] ML Imbalanced oversampling , undersampling 2019 [63] ML Imbal...…”

Section: Yearmentioning

confidence: 99%

See 2 more Smart Citations

A Comprehensive Survey of Detection and Prevention Approaches for Online Radicalization: Identifying Gaps and Future Directions

Berjawi,

Fenza,

Loia

2023

IEEE Access

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In the digital era, online radicalization has emerged as a significant concern for governments, social media platforms, and researchers. Detecting and preventing online radicalization have become key priorities, leading to extensive research efforts. This study presents a comprehensive survey of existing works in this field, covering various techniques and methodologies. An extensive assessment of 68 publications from databases such as IEEE, SCOPUS, and Web of Science (WoS) was conducted to analyze recent literature on detecting and preventing online radicalization. This research provides an overview of the definition of online radicalization and its relationship with social media. It explores different types and sources of datasets used in studying online radicalization. Additionally, it categorizes approaches and techniques, including Machine Learning (ML), Deep Learning (DL), and Graph algorithms, for detecting and preventing online radicalization. The survey identifies limitations and challenges in the field, highlighting existing gaps and suggesting potential directions for further study. To the best of the authors' knowledge, this work is the first of its kind to undertake such a holistic investigation that consolidates these methodologies presenting them in an accessible manner. The findings contribute as a valuable resource for academics, decision-makers, and professionals working in the field of counter-radicalization and provide insights into existing countermeasures against this expanding threat.

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Identifying the Popularity and Persuasiveness of Right- and Left-Leaning Group Videos on Social Media

Cited by 2 publications

References 12 publications

COVID-19, Diffuse Anxiety, and Public (Mis)Trust in Government: Empirical Insights and Implications for Crime and Justice

COVID-19, Diffuse Anxiety, and Public (Mis)Trust in Government: Empirical Insights and Implications for Crime and Justice

A Comprehensive Survey of Detection and Prevention Approaches for Online Radicalization: Identifying Gaps and Future Directions

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