Biographically Relevant Tweets – a New Dataset, Linguistic Analysis and Classification Experiments

Michael Wiegand, Rebecca Wilm, Katja Markert


Abstract
We present a new dataset comprising tweets for the novel task of detecting biographically relevant utterances. Biographically relevant utterances are all those utterances that reveal some persistent and non-trivial information about the author of a tweet, e.g. habits, (dis)likes, family status, physical appearance, employment information, health issues etc. Unlike previous research we do not restrict biographical relevance to a small fixed set of pre-defined relations. Next to classification experiments employing state-of-the-art classifiers to establish strong baselines for future work, we carry out a linguistic analysis that compares the predictiveness of various high-level features. We also show that the task is different from established tasks, such as aspectual classification or sentiment analysis.
Anthology ID:
2022.coling-1.323
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
3669–3679
Language:
URL:
https://aclanthology.org/2022.coling-1.323
DOI:
Bibkey:
Cite (ACL):
Michael Wiegand, Rebecca Wilm, and Katja Markert. 2022. Biographically Relevant Tweets – a New Dataset, Linguistic Analysis and Classification Experiments. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3669–3679, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Biographically Relevant Tweets – a New Dataset, Linguistic Analysis and Classification Experiments (Wiegand et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.323.pdf