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This study examines Italian-language content on the social media platform Mastodon, employing some typical tools for textual analysis. A set of posts (known as ‘toots’ on Mastodon) related to the hashtag #intelligenzaartificiale from the past two years were collected. Co-occurrence networks between hashtags and cross-author referrals within the same toots were analyzed. Next, structural topic modeling was utilized to identify four topics and their most important keywords. A comparison was then made between the predictive ability of a set of words and that of a transformer using a classifier, resulting in similar findings. Furthermore, a SHAP analysis was conducted to demonstrate the impact of individual words on the classification model, providing an explanation of the contribution of individual features.

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Analysis of Mastodon's Italian Messages: Networks, Topics, LLM and Machine Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Livio Finos,
  • Andrea Sciandra

摘要

This study examines Italian-language content on the social media platform Mastodon, employing some typical tools for textual analysis. A set of posts (known as ‘toots’ on Mastodon) related to the hashtag #intelligenzaartificiale from the past two years were collected. Co-occurrence networks between hashtags and cross-author referrals within the same toots were analyzed. Next, structural topic modeling was utilized to identify four topics and their most important keywords. A comparison was then made between the predictive ability of a set of words and that of a transformer using a classifier, resulting in similar findings. Furthermore, a SHAP analysis was conducted to demonstrate the impact of individual words on the classification model, providing an explanation of the contribution of individual features.