COVID19α: Interactive Spatio-Temporal Visualization of COVID-19 Symptoms through Tweet Analysis

A New Social Media Analytics Method for Identifying Factors Contributing to COVID-19 Discussion Topics

“…Since the emergence of the COVID-19 crisis, scholars and policymakers have adeptly harnessed Twitter as a principal reservoir for the meticulous scrutiny of public sentiments [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. The perspicacious analysis of public sentiment engenders empirically grounded policymaking across a spectrum of COVID-19-related strategic imperatives, including, but not limited to, the imposition of lockdown measures, travel restrictions, vaccination campaigns, and the amelioration of misinformation dissemination.…”

“…These noteworthy observations discern an array of contributory variables encompassing tweet language, negative sentiment, positive sentiment, neutral sentiment, tweet timestamp, retweet count, friend count, and follower count, which exert discernible influences upon distinct discussion themes. Importantly, it merits emphasis that none of the extant studies on Twitter-based COVID-19 discourse, as indexed in [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], have proffered a methodology as innovative as the one advanced herein, integrating AI-powered regression and clustering techniques for the purpose of discerning the determinants of COVID-19-related discussion topics.…”

“…The survey also found that age, race, political party, and news source are some of the factors that influence the level of belief in COVID-19 misinformation. To eliminate these misconceptions and also to make strategic policy decisions on controlling COVID-19 crises, researchers and policymakers have been using Twitter analytics with artificial intelligence (AI) and natural language processing (NLP) (as shown in [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]). However, none of these tweet-mining technics in the area of COVID-19 (i.e., [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]) or other topics (e.g., [26][27][28][29][30][31][32][33]), have demonstrated a methodology to identify the factors correlated to Twitter discourse topics.…”

Zero-Shot Multi-Label Topic Inference with Sentence Encoders and LLMs

“…This work is built upon prior research from multiple areas, including Topic Modeling and Categorization (Blei et al, 2003;Wang et al, 2011;Iwata et al, 2009), Text Annotation (Ogren, 2006;Zlabinger, 2019;Bijoy et al, 2021), Zero-Shot Learning (Veeranna et al, 2016;Yin et al, 2019), Sentence embeddings (Casanueva et al, 2020;Cer et al, 2018a), Large Language Models (Scao et al, 2022;Brown et al, 2020) etc. A brief discussion on each area and how this work is positioned concerning the state-of-the-art is as follows.…”