Social Media Data Mining With Natural Language Processing on Public Dream Contents
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929675109924864 |
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| author | Hua, Howard Yu, Joe |
| author_facet | Hua, Howard Yu, Joe |
| contents | The COVID-19 pandemic has significantly transformed global lifestyles, enforcing physical isolation and accelerating digital adoption for work, education, and social interaction. This study examines the pandemic's impact on mental health by analyzing dream content shared on the Reddit r/Dreams community. With over 374,000 subscribers, this platform offers a rich dataset for exploring subconscious responses to the pandemic. Using statistical methods, we assess shifts in dream positivity, negativity, and neutrality from the pre-pandemic to post-pandemic era. To enhance our analysis, we fine-tuned the LLaMA 3.1-8B model with labeled data, enabling precise sentiment classification of dream content. Our findings aim to uncover patterns in dream content, providing insights into the psychological effects of the pandemic and its influence on subconscious processes. This research highlights the profound changes in mental landscapes and the role of dreams as indicators of public well-being during unprecedented times. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_07839 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Social Media Data Mining With Natural Language Processing on Public Dream Contents Hua, Howard Yu, Joe Computers and Society Artificial Intelligence Computation and Language Social and Information Networks I.2.7 The COVID-19 pandemic has significantly transformed global lifestyles, enforcing physical isolation and accelerating digital adoption for work, education, and social interaction. This study examines the pandemic's impact on mental health by analyzing dream content shared on the Reddit r/Dreams community. With over 374,000 subscribers, this platform offers a rich dataset for exploring subconscious responses to the pandemic. Using statistical methods, we assess shifts in dream positivity, negativity, and neutrality from the pre-pandemic to post-pandemic era. To enhance our analysis, we fine-tuned the LLaMA 3.1-8B model with labeled data, enabling precise sentiment classification of dream content. Our findings aim to uncover patterns in dream content, providing insights into the psychological effects of the pandemic and its influence on subconscious processes. This research highlights the profound changes in mental landscapes and the role of dreams as indicators of public well-being during unprecedented times. |
| title | Social Media Data Mining With Natural Language Processing on Public Dream Contents |
| topic | Computers and Society Artificial Intelligence Computation and Language Social and Information Networks I.2.7 |
| url | https://arxiv.org/abs/2501.07839 |