Traits of a Leader: User Influence Level Prediction through Sociolinguistic Modeling
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910196752711680 |
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| author | Katerenchuk, Denys Levitan, Rivka |
| author_facet | Katerenchuk, Denys Levitan, Rivka |
| contents | Recognition of a user's influence level has attracted much attention as human interactions move online. Influential users have the ability to sway others' opinions to achieve some goals. As a result, predicting users' level of influence can help to understand social networks, forecast trends, prevent misinformation, etc. However, predicting user influence is a challenging problem because the concept of influence is specific to a situation or a domain, and user communications are limited to text. In this work, we define user influence level as a function of community endorsement and develop a model that significantly outperforms the baseline by leveraging demographic and personality data. This approach consistently improves RankDCG scores across eight different domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_04046 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Traits of a Leader: User Influence Level Prediction through Sociolinguistic Modeling Katerenchuk, Denys Levitan, Rivka Physics and Society Artificial Intelligence Computers and Society Recognition of a user's influence level has attracted much attention as human interactions move online. Influential users have the ability to sway others' opinions to achieve some goals. As a result, predicting users' level of influence can help to understand social networks, forecast trends, prevent misinformation, etc. However, predicting user influence is a challenging problem because the concept of influence is specific to a situation or a domain, and user communications are limited to text. In this work, we define user influence level as a function of community endorsement and develop a model that significantly outperforms the baseline by leveraging demographic and personality data. This approach consistently improves RankDCG scores across eight different domains. |
| title | Traits of a Leader: User Influence Level Prediction through Sociolinguistic Modeling |
| topic | Physics and Society Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2501.04046 |