A Statistical Analysis of the Relationship Between Meme Stocks and Social Media
Meme stocks, driven by viral social media trends, have added new complexities to financial markets. Prior studies have explored meme stock price dynamics and investor sentiment, but the interplay between social media activity and market movements, along with the structural and linguistic features of...
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| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10948426/ |
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| author | Seungju Lee Yunyoung Lee Jaewook Lee Hoki Kim |
| author_facet | Seungju Lee Yunyoung Lee Jaewook Lee Hoki Kim |
| author_sort | Seungju Lee |
| collection | DOAJ |
| description | Meme stocks, driven by viral social media trends, have added new complexities to financial markets. Prior studies have explored meme stock price dynamics and investor sentiment, but the interplay between social media activity and market movements, along with the structural and linguistic features of online discussions, remains understudied. To address this, we integrate econometric and NLP-based techniques, combining correlation analysis, Granger causality testing, BSADF-based bubble detection, and textual analysis. Our results reveal a strong correlation between trading volume and social media engagement, with Granger causality confirming a feedback loop between market fluctuations and online discussions. BSADF analysis demonstrates that social media-based detection complements price-based methods by identifying explosive periods they may miss. Additionally, network analysis indicates that meme stock discussions exhibit distinct structural patterns, while linguistic analysis highlights unique word choices and emoji usage. Sentiment analysis shows that bullish sentiment dominates during speculative surges, reinforcing the emotionally driven nature of meme stock trading. These findings provide investors with a complementary tool for risk assessment by integrating sentiment with traditional market indicators, while helping regulators monitor online sentiment to identify early signs of speculative excess and market instability. |
| format | Article |
| id | doaj-art-1270e104024d45f0adfc230a1003fff8 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1270e104024d45f0adfc230a1003fff82025-08-20T02:26:24ZengIEEEIEEE Access2169-35362025-01-0113631436315610.1109/ACCESS.2025.355746010948426A Statistical Analysis of the Relationship Between Meme Stocks and Social MediaSeungju Lee0https://orcid.org/0000-0002-2553-0177Yunyoung Lee1https://orcid.org/0000-0002-1129-0467Jaewook Lee2https://orcid.org/0000-0001-5720-8337Hoki Kim3https://orcid.org/0000-0001-5361-459XDepartment of Industrial Engineering, Seoul National University, Seoul, South KoreaDepartment of Artificial Intelligence and Data Science, Sejong University, Seoul, South KoreaDepartment of Industrial Engineering, Seoul National University, Seoul, South KoreaDepartment of Industrial Security, Chung-Ang University, Seoul, South KoreaMeme stocks, driven by viral social media trends, have added new complexities to financial markets. Prior studies have explored meme stock price dynamics and investor sentiment, but the interplay between social media activity and market movements, along with the structural and linguistic features of online discussions, remains understudied. To address this, we integrate econometric and NLP-based techniques, combining correlation analysis, Granger causality testing, BSADF-based bubble detection, and textual analysis. Our results reveal a strong correlation between trading volume and social media engagement, with Granger causality confirming a feedback loop between market fluctuations and online discussions. BSADF analysis demonstrates that social media-based detection complements price-based methods by identifying explosive periods they may miss. Additionally, network analysis indicates that meme stock discussions exhibit distinct structural patterns, while linguistic analysis highlights unique word choices and emoji usage. Sentiment analysis shows that bullish sentiment dominates during speculative surges, reinforcing the emotionally driven nature of meme stock trading. These findings provide investors with a complementary tool for risk assessment by integrating sentiment with traditional market indicators, while helping regulators monitor online sentiment to identify early signs of speculative excess and market instability.https://ieeexplore.ieee.org/document/10948426/Behavioral financeinvestor sentimentmeme stocksnatural language processingsocial media |
| spellingShingle | Seungju Lee Yunyoung Lee Jaewook Lee Hoki Kim A Statistical Analysis of the Relationship Between Meme Stocks and Social Media IEEE Access Behavioral finance investor sentiment meme stocks natural language processing social media |
| title | A Statistical Analysis of the Relationship Between Meme Stocks and Social Media |
| title_full | A Statistical Analysis of the Relationship Between Meme Stocks and Social Media |
| title_fullStr | A Statistical Analysis of the Relationship Between Meme Stocks and Social Media |
| title_full_unstemmed | A Statistical Analysis of the Relationship Between Meme Stocks and Social Media |
| title_short | A Statistical Analysis of the Relationship Between Meme Stocks and Social Media |
| title_sort | statistical analysis of the relationship between meme stocks and social media |
| topic | Behavioral finance investor sentiment meme stocks natural language processing social media |
| url | https://ieeexplore.ieee.org/document/10948426/ |
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