Using a Three-step Social Media Similarity (TSMS) Mapping Method to Analyze Controversial Speech Relating to COVID-19 in Twitter Collections

Yin, Zhanyuan; Fan, Lizhou; Yu, Huizi; Gilliland, Anne

doi:10.1109/bigdata50022.2020.9377930

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…It is being carried out by a transdisciplinary team of academic researchers from computer and information sciences, statistics, digital humanities and archival studies. Our research directly addresses the questions raised in #1-3 of our larger research agenda below, as well as preliminarily contemplates others raised by #4-7 [69]:…”

Section: Introductionmentioning

confidence: 64%

Using Machine Learning to Enhance Archival Processing of Social Media Archives

Fan

Yin

et al. 2022

J. Comput. Cult. Herit.

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This paper reports on a study using machine learning to identify incidences and shifting dynamics of hate speech in social media archives. To better cope with the archival processing need for such large scale and fast evolving archives, we propose the Data-driven and Circulating Archival Processing (DCAP) method. As a proof-of-concept, our study focuses on an English language Twitter archive relating to COVID-19: tweets were repeatedly scraped between February and June 2020, ingested and aggregated within the COVID-19 Hate Speech Twitter Archive (CHSTA) and analyzed for hate speech using the Generative Adversarial Network (GAN)-inspired DCAP Method. Outcomes suggest that it is possible to use machine learning and data analytics to surface and substantiate trends from CHSTA and similar social media archives that could provide immediately useful knowledge for crisis response, in controversial situations, or for public policy development, as well as for subsequent historical analysis. The approach shows potential for integrating multiple aspects of the archival workflow, and supporting automatic iterative redescription and reappraisal activities in ways that make them more accountable and more rapidly responsive to changing societal interests and unfolding developments.

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Section: Introductionmentioning

confidence: 64%

Using Machine Learning to Enhance Archival Processing of Social Media Archives

Fan

Yin

et al. 2022

J. Comput. Cult. Herit.

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“…By using NLP to effectively analyze complex semantic meanings and hidden emotions contained in large-scale online information corpora, researchers have been able to identify insights for such purposes as online marketing, customer relationship management, and monitoring public opinions [42]. For example, Liu and Toubia used semantic methods to estimate consumer content preferences from online search results [43], and Yin et al employed Machine Learning approaches to identify controversial speech related to COVID-19 on Twitter [44]. NLP has also been gaining popularity in the fields of healthcare, especially with the increasing accessibility of Electronic Health Records data.…”

Section: Nlp Methodsmentioning

confidence: 99%

Disparities and resilience: analyzing online Health information provision, behaviors and needs of LBGTQ + elders during COVID-19

Fan

Gilliland

2022

BMC Public Health

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Background Prior studies indicate that older members of LGBTQ+ communities have specific health provision and health information needs related to coping with COVID-19, its long-term effects, and the social and economic impact of the pandemic. This study addresses the issue of a lack of timely, complete, and high-quality data about this population’s healthcare and healthcare information needs and behaviors. Recognizing also that this is a diverse population made up of multiple communities and identities with different concerns and experiences, this research seeks to develop and refine a method that can provide additional nuanced data and insights that can support improved and more closely targeted health interventions and online information provision. Methods We use computational discourse analysis, which is based on NLP algorithms, to build and analyze a digital corpus of online search results containing rich, wide-ranging content such as quotes and anecdotes from older members of LGBTQ+ communities as well as practitioners, advice, and recommendations from policymakers and healthcare experts, and research outcomes. In our analysis, we develop and apply an innovative disparities and resilience (D&R) framework to identify external and internal perspectives and understand better disparities and resilience as they pertain to this population. Results Results of this initial study support previous research that LGBTQ+ elders experience aggravated health and related social-economic disparities in comparison to the general population of older people. We also find that LGBTQ+ elders leverage individual toughness and community closeness, and quickly adapt mentally and technologically, despite inadequate social infrastructure for sharing health information and elders’ often low social economic status. The methods used therefore are able to surface distinctive resilience in the face of distinctive disparities. Conclusions Our study provides evidence that methodological innovation in gathering and analyzing digital data relating to overlooked, disparately affected, and socially and economically marginalized intersectional communities such as LGBTQ+ elders can result in increased external and self-knowledge of these populations. Specifically, it demonstrates the potential of computational discourse analysis to surface hidden and emerging issues and trends relating to a multi-faceted population that has important concerns about public exposure in highly timely and automated ways. It also points to the potential benefits of triangulating data gathered through this approach with data gathered through more traditional mechanisms such as surveys and interviews. Trial registration Not Applicable.

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“…For example, during the COVID-19 pandemic, wild extremes of emotion and controversial speech have occurred on Twitter. Controversial opinions and subjective judgments are spreading heavily with Twitter retweets [6]. In such cases, controversy detection would be helpful.…”

Section: Open Access Edited Bymentioning

confidence: 99%

A novel method detecting controversial interaction in the multiplex social comment network

et al. 2023

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With the rise of online social media, users from across the world can participate in opinion formation processes, and some discussions lead to controversial debates around this phenomenon. Controversy detection in social media can help explore public discourse spaces and understand topical issues. Previous controversial detection studies focus more on identifying the opinion or emotional orientation of a comment, while we focus on whether there is a controversial relationship between a comment and its replies. Here, we collect a dataset consisting of 511 news articles, 103,787 comments, and 71,579 users on the Chinese social media platform, Toutiao, and we study the controversial interactions on the subsets of this dataset. Our approach treats news, comments, and users as different types of nodes and constructs multiplex networks connected by user–comment links (i.e., publishing relationship), comment–news links (i.e., comment relationship), and comment–comment links (i.e., replying relationship). Furthermore, we propose a model based on deep learning to detect controversial interactions from these multiplex networks. Our supervised model achieves 83.24% accuracy, with an improvement compared to competitive models. Moreover, we illustrate the applicability of our approach using different ratios of training and testing sets. Our results demonstrate the usefulness of the multiplex networks model for controversial interaction detection and provide a new perspective on controversy detection problems.

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Using a Three-step Social Media Similarity (TSMS) Mapping Method to Analyze Controversial Speech Relating to COVID-19 in Twitter Collections

Cited by 4 publications

References 6 publications

Using Machine Learning to Enhance Archival Processing of Social Media Archives

Using Machine Learning to Enhance Archival Processing of Social Media Archives

Disparities and resilience: analyzing online Health information provision, behaviors and needs of LBGTQ + elders during COVID-19

A novel method detecting controversial interaction in the multiplex social comment network

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