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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: "What are People Talking about in #BlackLivesMatter and #StopAsianHate? Exploring and Categorizing Twitter Topics Emerged in Online Social Movements through the Latent Dirichlet Allocation Model" |
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Identification Number: |
doi:10.7910/DVN/IYOIDD |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2022-01-16 |
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Version: |
1 |
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Bibliographic Citation: |
Tong, Xin; Li, Jiayi; Li, Yixuan; Zhang, Luyao, 2022, "Replication Data for: "What are People Talking about in #BlackLivesMatter and #StopAsianHate? Exploring and Categorizing Twitter Topics Emerged in Online Social Movements through the Latent Dirichlet Allocation Model"", https://doi.org/10.7910/DVN/IYOIDD, Harvard Dataverse, V1, UNF:6:l9ChvzVuQRE/Qf4SkqPkwg== [fileUNF] |
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Citation |
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Title: |
Replication Data for: "What are People Talking about in #BlackLivesMatter and #StopAsianHate? Exploring and Categorizing Twitter Topics Emerged in Online Social Movements through the Latent Dirichlet Allocation Model" |
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Identification Number: |
doi:10.7910/DVN/IYOIDD |
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Authoring Entity: |
Tong, Xin (Duke Kunshan University) |
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Li, Jiayi (Duke Kunshan University) |
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Li, Yixuan (Duke Kunshan University) |
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Zhang, Luyao (Duke Kunshan University) |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Zhang, Luyao |
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Depositor: |
Zhang, Luyao |
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Date of Deposit: |
2022-01-16 |
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Holdings Information: |
https://doi.org/10.7910/DVN/IYOIDD |
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Study Scope |
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Keywords: |
Arts and Humanities, Computer and Information Science, Social Sciences, Arts and Humanities, Computer and Information Science, Social Sciences |
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Abstract: |
Minority groups have been using social media platforms to organize their social movements that create profound social impacts. Black Lives Matter (BLM) is one of the many successful social movements that started and expanded on social media. However, quantitative analysis with rigorous machine learning and natural language processing is absent. We apply Latent Dirichlet Allocation (LDA) model to analyze more than one million tweets with #blacklivesmatter following a major BLM movement and compared the results to those with #stopasianhate after a Stop Asian Hate (SAH) event. Our findings revealed that the tweets presented a thorough examination of the most influential topics, including the dominant topic of politics and social justice. By comparing the analysis results from the two most recent and critical online social movements, our study contributes to the topic analysis of social movements on microblogging platforms in particular and social media in general. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
CC0 Waiver |
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Other Study Description Materials |
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File Description--f5744683 |
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File: stop_asian_hate.tab |
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Notes: |
UNF:6:l9ChvzVuQRE/Qf4SkqPkwg== |
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List of Variables: | |
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Variables |
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f5744683 Location: |
Variable Format: character Notes: UNF:6:xSoyB4u+V6jdFfCvUGBBMg== |
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f5744683 Location: |
Variable Format: character Notes: UNF:6:gskUu8Ghh62O6bzWB9j7kQ== |
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f5744683 Location: |
Variable Format: character Notes: UNF:6:XjaQ9GFmaOZ52wFvc4Z7Cg== |
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Label: |
black_lives_matter.csv |
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Notes: |
text/csv |