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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.05065 |
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| _version_ | 1866909529023709184 |
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| author | Nakka, Nitheesha Yalcin, Omer F. Desmarais, Bruce A. Rajtmajer, Sarah Monroe, Burt |
| author_facet | Nakka, Nitheesha Yalcin, Omer F. Desmarais, Bruce A. Rajtmajer, Sarah Monroe, Burt |
| contents | Statistical topic modeling is widely used in political science to study text. Researchers examine documents of varying lengths, from tweets to speeches. There is ongoing debate on how document length affects the interpretability of topic models. We investigate the effects of aggregating short documents into larger ones based on natural units that partition the corpus. In our study, we analyze one million tweets by U.S. state legislators from April 2016 to September 2020. We find that for documents aggregated at the account level, topics are more associated with individual states than when using individual tweets. This finding is replicated with Wikipedia pages aggregated by birth cities, showing how document definitions can impact topic modeling results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_05065 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The study of short texts in digital politics: Document aggregation for topic modeling Nakka, Nitheesha Yalcin, Omer F. Desmarais, Bruce A. Rajtmajer, Sarah Monroe, Burt Computation and Language Machine Learning Statistical topic modeling is widely used in political science to study text. Researchers examine documents of varying lengths, from tweets to speeches. There is ongoing debate on how document length affects the interpretability of topic models. We investigate the effects of aggregating short documents into larger ones based on natural units that partition the corpus. In our study, we analyze one million tweets by U.S. state legislators from April 2016 to September 2020. We find that for documents aggregated at the account level, topics are more associated with individual states than when using individual tweets. This finding is replicated with Wikipedia pages aggregated by birth cities, showing how document definitions can impact topic modeling results. |
| title | The study of short texts in digital politics: Document aggregation for topic modeling |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2503.05065 |