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    題名: 以計算分析方法比較陰謀論與不實資訊的文本： 以社交媒體中的伊維菌素討論為例
    Comparing Texts of Conspiracy Theories and Misinformation by Computational methods: The Case Study of Ivermectin Discussions on Social Media
    作者: 楊聲輝
    Yang, Sheng-Hui
    貢獻者: 鄭宇君
    Cheng, Yu-Chung
    楊聲輝
    Yang, Sheng-Hui
    關鍵詞: 陰謀論
    伊維菌素
    不實資訊
    文本分析
    LDA
    黨派動機推理
    Conspiracy Theories
    Ivermectin
    Misinformation
    Texts analysis
    LDA
    Partisan Motivated Reasoning
    日期: 2024
    上傳時間: 2024-04-01 14:23:30 (UTC+8)
    摘要: 本研究以 2021 年 Facebook 的伊維菌素（Ivermectin）討論為個案研究，當時全球面臨嚴重的新冠疫情，卻沒有治療新冠的特效藥，因此社交媒體上的官方敘事，與另類藥物伊維菌素產生了競合關係。例如，有人聲稱伊維菌素可以替代疫苗、城市封鎖或 Covid-19 篩檢等新冠防疫措施。這些不實資訊（Misinformation）對於公共衛生和政府政策推動產生了危害，甚至演變成了陰謀論（Conspiracy Theory），認為政府和商業組織正暗中共謀，並從打壓伊維菌素中獲利。
    本研究以大數據文本和計算方法為基礎，深入研究不實資訊和陰謀論的特徵，採用黨派動機推理的心理機制為分析的理論框架，通過對不實資訊和陰謀論的文本進行計算分析和語言心理分析，藉此瞭解民主社會的政治極化現象。研究者通過Facebook (Meta) 官方許可用來收集公開社團與專頁貼文工具 CrowdTangle API ，爬取2021 年整年約 40 萬筆Ivermectin的貼文，根據語言篩選出 40621 筆英文資料進行分析。 研究設計上，採用LDA主題模型分析以及質化的小組討論編碼，為陰謀論、不實資訊和事實訊息分類，通過分析文本訊息的語言心理特徵，比較了不實資訊傳播者和陰謀論傳播者在黨派動機推理程度上的差異。
    本研究發現，在伊維菌素的討論中，陰謀論與不實資訊經常交纏在一起，代理人、文本訊息與傳播動機在組間有著微妙的差異，本研究資料集還顯示不實資訊和陰謀論有可能同時並存，過往研究少有學者在同一個議題下，對兩者進行組間比較。
    This study is a case study of the 2021 discussions on Ivermectin on Facebook. During this time, the world was grappling with the COVID-19 pandemic, and there was no specific treatment for the virus. This led to a competition between official narratives on social media and alternative remedies like Ivermectin. Some claimed that Ivermectin could replace vaccines, city lockdowns, or COVID testing as preventive measures. Such misinformation had adverse effects on public health and government policy and, in some cases, even evolved into conspiracy theories, suggesting that governments and businesses were secretly conspiring to profit from suppressing Ivermectin.
    This study utilized big data text and computational methods to delve into the characteristics of misinformation and conspiracy theories. It employed partisan motivation reasoning as the theoretical framework for analysis. By conducting computational and psycholinguistic analyses of misinformation and conspiracy theory texts, the study aimed to understand political polarization in democratic societies.
    The researcher collected approximately 400,000 Ivermectin-related posts from Facebook throughout 2021 using the CrowdTangle API. From these, 40,621 English-language posts were selected for analysis through language filtering. The research design included the use of LDA topic modeling and qualitative group discussions for coding, categorizing conspiracy theories, misinformation, and factual messages. By analyzing the language and psychological characteristics of the text messages, the study compared the differences in partisan motivation reasoning between spreaders of misinformation and conspiracy theories.
    The study found that conspiracy theories and misinformation frequently intertwined in discussions about Ivermectin. There were subtle differences between agents, text messages, and dissemination motivations. The dataset also revealed that misinformation and conspiracy theories could coexist, a comparison that has been less explored in previous research on the same topic.
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    描述: 碩士
    國立政治大學
    傳播學院傳播碩士學位學程
    110464035
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0110464035
    資料類型: thesis
    顯示於類別:[傳播學院傳播碩士學位學程] 學位論文

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