IRIS 3D

2024

1. Jalali Farahani, F., Hanke, S., Dima, C., Heiberger, R. H., & Staab, S. (2024). Who is targeted? Detecting social group mentions in online political discussions. Companion Publication of the 16th ACM Web Science Conference, 24–25. https://doi.org/10.1145/3630744.3658412
   • BibTeX
   • Link

   BibTeX

   @inproceedings{10.1145/3630744.3658412, abstract = {Social groups are central to political discussions. However, detecting social groups in text often relies on pre-determined socio-demographic categories or supervised learning methods that require extensive hand-labeled datasets. In this paper, we propose a methodology designed to leverage the potential of Large Language Models (LLMs) for the identification and annotation of social groups in text. The experiments show that open LLMs like Llama-2-70B-Chat and Mixtral-8-7B can reliably be used to annotate social groups in a few-shot scenario without the need for supervised learning. The automatically obtained annotations largely match human annotations on random samples from the Reddit Politosphere, resulting in micro-F1 scores of 0.71 and 0.83, respectively.}, address = {New York, NY, USA}, author = {Jalali Farahani, Farane and Hanke, Sara and Dima, Corina and Heiberger, Raphael Heiko and Staab, Steffen}, booktitle = {Companion Publication of the 16th ACM Web Science Conference}, doi = {10.1145/3630744.3658412}, isbn = {9798400704536}, location = {Stuttgart, Germany}, numpages = {2}, pages = {24–25}, publisher = {Association for Computing Machinery}, series = {Websci Companion '24}, title = {Who is targeted? Detecting social group mentions in online political discussions}, url = {https://doi.org/10.1145/3630744.3658412}, year = 2024 }

   Link

   https://doi.org/10.1145/3630744.3658412