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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 1-四月-2024 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. 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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 dc.contributor.advisor 鄭宇君 zh_TW dc.contributor.advisor Cheng, Yu-Chung en_US dc.contributor.author (作者) 楊聲輝 zh_TW dc.contributor.author (作者) Yang, Sheng-Hui en_US dc.creator (作者) 楊聲輝 zh_TW dc.creator (作者) Yang, Sheng-Hui en_US dc.date (日期) 2024 en_US dc.date.accessioned 1-四月-2024 14:23:30 (UTC+8) - dc.date.available 1-四月-2024 14:23:30 (UTC+8) - dc.date.issued (上傳時間) 1-四月-2024 14:23:30 (UTC+8) - dc.identifier (其他 識別碼) G0110464035 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/150659 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 傳播學院傳播碩士學位學程 zh_TW dc.description (描述) 110464035 zh_TW dc.description.abstract (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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents 第一章 緒論 8 第一節 研究動機與問題 8 第二節 研究背景與目的 13 一、 伊維菌素討論的背景 13 二、 多角度理解不實資訊和陰謀論文本 18 第三節 研究問題 19 第二章 文獻回顧 22 第一節 不實資訊研究 22 第二節 為什麼人們會分享不實資訊 29 一、 為什麼我們需要新的科學傳播框架來應對不斷湧現的不實資訊？ 29 二、 不實資訊的概念性框架 33 三、 黨派動機推理作為分享不實資訊的主流解釋 35 第三節 陰謀論與不實資訊的交纏 39 一、 陰謀論的定義及重要性 39 二、 Covid-19 的背景下的陰謀論如何增加不實資訊研究的複雜性？ 41 三、 陰謀論介入後出現了哪些交纏的研究概念？ 42 第四節 小結 46 第三章 研究設計 50 第一節 研究流程 50 第二節 主題模型與 LDA 方法 54 一、 資料來源與預處理 56 二、 LDA Trimming 56 三、 LDA Tuning（K-search） 57 第三節 質化小組討論的主題編碼 58 一、 編碼範圍與方法 59 二、 主題分組 60 三、 準確度驗證 61 第四節 LIWC 字典 61 第四章 結果與分析 64 第一節 LDA 主題模型分析結果 64 第二節 2021年三個小組之間的八大主題趨勢 65 第三節 主題網絡中的相對位置和關系 71 第四節 三個群體中的語言策略和心理狀態 72 第五章 結論 75 第一節 研究發現與貢獻 75 一、 資料分析發現 75 二、 不實資訊演化為陰謀論的風險 76 三、 建立一個深入「小議題」的方法論框架 77 四、 未來面對科學不實資訊的策略 77 第二節 研究限制與未來研究建議 79 一、 不實資訊組和陰謀論組的解釋侷限性 79 二、 資料來源限制 81 三、 小結 82 參考文獻 84 zh_TW dc.format.extent 4154122 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110464035 en_US dc.subject (關鍵詞) 陰謀論 zh_TW dc.subject (關鍵詞) 伊維菌素 zh_TW dc.subject (關鍵詞) 不實資訊 zh_TW dc.subject (關鍵詞) 文本分析 zh_TW dc.subject (關鍵詞) LDA zh_TW dc.subject (關鍵詞) 黨派動機推理 zh_TW dc.subject (關鍵詞) Conspiracy Theories en_US dc.subject (關鍵詞) Ivermectin en_US dc.subject (關鍵詞) Misinformation en_US dc.subject (關鍵詞) Texts analysis en_US dc.subject (關鍵詞) LDA en_US dc.subject (關鍵詞) Partisan Motivated Reasoning en_US dc.title (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 zh_TW dc.title (題名) Comparing Texts of Conspiracy Theories and Misinformation by Computational methods: The Case Study of Ivermectin Discussions on Social Media en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Ahmed, W., Vidal-Alaball, J., Downing, J., & Seguí, F. 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