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Interdisciplinary Approach to Identify and Characterize COVID-19 Misinformation on Twitter: Mixed Methods Study

Isip Tan IT, Cleofas J, Solano G, Pillejera JG, Catapang JK

JMIR Form Res 2023;7:e41134

Problem Statement

Studying COVID-19 misinformation on Twitter presents methodological challenges. A computational approach can analyze large data sets, but it is limited when interpreting context. A qualitative approach allows for a deeper analysis of content, but it is labor-intensive and feasible only for smaller data sets.

Solution

    • An interdisciplinary team composed of experts in health, health informatics, social science, and computer science combined computational and qualitative methods to gain a better understanding of COVID-19 misinformation on Twitter.
    • Natural language processing mislabeled tweets, likely due to tweets written in Filipino or a combination of the Filipino and English languages.
    • Identifying the formats and discursive strategies of tweets with misinformation required iterative, manual, and emergent coding by human coders with experiential and cultural knowledge of Twitter.

KLIP: Kullback-Leibler divergence for scoring informativeness and phraseness

Clamor TD, Solano GA, Oco N, Catapang JK, Cleofas J, Isip-Tan IT. Identification and analysis of COVID-19-related misinformation Tweets via kullback-leibler divergence for informativeness and phraseness and biterm topic modeling. Proceedings of the 2022 International Conference on Artificial Intelligence in Information and Communication; ICAIIC '22; February 21-24, 2022; Jeju Island, Korea. 2020. pp. 451–6.

icisiptan@up.edu.ph