@inproceedings{han-etal-2026-toward,
title = "Toward Detecting Modern Social Media Agents: Dataset, Experimental Analysis, and an Agent-Based Solution",
author = "Han, Yi and
Liao, Lizi and
Lu, Haiqi and
Liu, Yuanxing and
Zhang, Weinan and
Liu, Ting",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1032/",
pages = "21801--21820",
ISBN = "979-8-89176-462-0",
abstract = "With the advancement of large language models (LLMs) and agent technologies, social bots have shed traditional stereotypes and evolved into autonomous, highly intelligent social media agents. This paper studies whether current social bot detection research is sufficient to identify such agents and discusses potential detection directions. We construct a social media agent dataset from authentic agent accounts on Weibo and Moltbook to evaluate prior detection approaches. Experimental results across various detection algorithms reveal that existing methods suffer from out-of-distribution (OOD) failures and exhibit a cognitive blind spot regarding agent-specific features. Through multidimensional feature comparisons, we find that such agents counterintuitively do not represent a transitional form between humans and traditional bots, but instead follow a distinct pattern that explains the observed generalization failures and feature limitations. Finally, motivated by the excellent performance of LLMs, we propose Agent-Detect-Agent, an online detection agent with tool calling. Experiments demonstrate that this agent further enhances the detection capability of base LLMs and achieves consistently good performance across different account types."
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<abstract>With the advancement of large language models (LLMs) and agent technologies, social bots have shed traditional stereotypes and evolved into autonomous, highly intelligent social media agents. This paper studies whether current social bot detection research is sufficient to identify such agents and discusses potential detection directions. We construct a social media agent dataset from authentic agent accounts on Weibo and Moltbook to evaluate prior detection approaches. Experimental results across various detection algorithms reveal that existing methods suffer from out-of-distribution (OOD) failures and exhibit a cognitive blind spot regarding agent-specific features. Through multidimensional feature comparisons, we find that such agents counterintuitively do not represent a transitional form between humans and traditional bots, but instead follow a distinct pattern that explains the observed generalization failures and feature limitations. Finally, motivated by the excellent performance of LLMs, we propose Agent-Detect-Agent, an online detection agent with tool calling. Experiments demonstrate that this agent further enhances the detection capability of base LLMs and achieves consistently good performance across different account types.</abstract>
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%0 Conference Proceedings
%T Toward Detecting Modern Social Media Agents: Dataset, Experimental Analysis, and an Agent-Based Solution
%A Han, Yi
%A Liao, Lizi
%A Lu, Haiqi
%A Liu, Yuanxing
%A Zhang, Weinan
%A Liu, Ting
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F han-etal-2026-toward
%X With the advancement of large language models (LLMs) and agent technologies, social bots have shed traditional stereotypes and evolved into autonomous, highly intelligent social media agents. This paper studies whether current social bot detection research is sufficient to identify such agents and discusses potential detection directions. We construct a social media agent dataset from authentic agent accounts on Weibo and Moltbook to evaluate prior detection approaches. Experimental results across various detection algorithms reveal that existing methods suffer from out-of-distribution (OOD) failures and exhibit a cognitive blind spot regarding agent-specific features. Through multidimensional feature comparisons, we find that such agents counterintuitively do not represent a transitional form between humans and traditional bots, but instead follow a distinct pattern that explains the observed generalization failures and feature limitations. Finally, motivated by the excellent performance of LLMs, we propose Agent-Detect-Agent, an online detection agent with tool calling. Experiments demonstrate that this agent further enhances the detection capability of base LLMs and achieves consistently good performance across different account types.
%U https://aclanthology.org/2026.findings-emnlp.1032/
%P 21801-21820
Markdown (Informal)
[Toward Detecting Modern Social Media Agents: Dataset, Experimental Analysis, and an Agent-Based Solution](https://aclanthology.org/2026.findings-emnlp.1032/) (Han et al., Findings 2026)
ACL
- Yi Han, Lizi Liao, Haiqi Lu, Yuanxing Liu, Weinan Zhang, and Ting Liu. 2026. Toward Detecting Modern Social Media Agents: Dataset, Experimental Analysis, and an Agent-Based Solution. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 21801–21820, Budapest, Hungary. Association for Computational Linguistics.