Papers with bots
A Study of Incorrect Paraphrases in Crowdsourced User Utterances (N19-1)
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| Challenge: | Developing bots requires high quality training samples, especially for unqualified crowd workers. |
| Approach: | They propose an annotated dataset for detecting quality issues in crowdsourced paraphrasing . they propose to use existing tools and services to provide baselines for identifying issues . |
| Outcome: | The proposed dataset provides a baseline for detecting unqualified paraphrases. |
Towards Conversational Recommendation over Multi-Type Dialogs (2020.acl-main)
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| Challenge: | In recent years, there has been a significant increase in the work of conversational recommendation due to the rise of voice-based bots. |
| Approach: | They use a Chinese dialog dataset DuRecDial to study conversational recommendation in the context of multi-type dialogs where bots can proactively lead a conversation from a non-recommendation dialog to a recommendation dialog. |
| Outcome: | The proposed dataset allows to investigate different parts of the overall problem, e.g., how to naturally lead a dialog, how interact with users for recommendation. |
Detecting Bot-Generated Text by Characterizing Linguistic Accommodation in Human-Bot Interactions (2021.findings-acl)
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| Challenge: | Language generation models' democratization makes it easier to generate human-like text at-scale for nefarious activities, from spreading misinformation to targeting specific groups with hate speech. |
| Approach: | They propose to use linguistic alignment to detect bot-generated text rather than using it directly. |
| Outcome: | The proposed methods are more robust across datasets and models if they use information about how people respond to it rather than using the bot's text directly. |
ELF22: A Context-based Counter Trolling Dataset to Combat Internet Trolls (2022.lrec-1)
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| Challenge: | a new dataset aims to automate the method to counter trolls . trolleds cause psychological damage to individuals and increase social costs . |
| Approach: | They propose to use a dataset to generate counter responses by varying counter responses according to a given strategy. |
| Outcome: | The proposed method improves strategy-controlled sentence generation. |
The slurk Interaction Server Framework: Better Data for Better Dialog Models (2022.lrec-1)
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| Challenge: | slurk is a lightweight dialog data collection and testing tool for crowdsourcing platforms. |
| Approach: | They present a lightweight dialog server that allows to set up dialog data collections and run experiments. |
| Outcome: | The slurk software allows to set up dialog data collections and run experiments with no limitations on the number of participants. |
Speaker or Listener? The Role of a Dialog Agent (2020.findings-emnlp)
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| Challenge: | chitchat bots are designed as a passive listener to answer what people ask . however, this passive and relatively simple response mechanism consumes the interest of human beings rapidly. |
| Approach: | They propose a model to transform a passive dialogue agent into a speaker by using external knowledge to express proactivity. |
| Outcome: | The proposed model obtains competitive results on the automatic and manual metrics and improves fluency and engagement of the chatbot significantly. |
Characterizing Social Spambots by their Human Traits (2021.findings-acl)
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| Challenge: | Social spambots are an emerging class of spammers attempting to emulate people . previous studies show that standard spambot detection methods fail to distinguish them from genuine accounts . |
| Approach: | They hypothesize that human-like attributes of social spambots are unhuman-like . they find that social spam bots are extremely similar and average in their expressed personality, demographics, and emotion . |
| Outcome: | The proposed method is based on the human characteristics of social spambots . it shows that social bots are extremely similar and average in their expressed personality, demographics, and emotion . |
ChatMatch: Evaluating Chatbots by Autonomous Chat Tournaments (2022.acl-long)
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| Challenge: | Existing automated evaluation systems of chatbots rely on static chat scripts as ground truth, which is hard to obtain. |
| Approach: | They propose an interactive chatbot evaluation framework that allows chatbots to compete with each other like in a sports tournament. |
| Outcome: | The proposed framework can rank chatbots independently from their model architectures and domains . existing evaluation systems rely on static chat scripts as ground truth . |
Semantic Reshuffling with LLM and Heterogeneous Graph Auto-Encoder for Enhanced Rumor Detection (2025.coling-main)
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| Challenge: | Current methods struggle against complex propagation influenced by bots, coordinated accounts, and echo chambers, which fragment information and increase risks of misjudgments. |
| Approach: | They propose a framework that integrates metapath-based rumor reconstruction and narrative reordering to detect rumors. |
| Outcome: | The proposed model outperforms existing methods and is highly accurate and robust. |
BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency (2023.acl-long)
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Zhenyu Lei, Herun Wan, Wenqian Zhang, Shangbin Feng, Zilong Chen, Jundong Li, Qinghua Zheng, Minnan Luo
| Challenge: | Existing methods to identify bots rely on text or networks alone . text-graph interactions and semantic consistency are essential improvements to combat bot evolution. |
| Approach: | They propose to combine text-graph interaction and semantic Consistency to model Twitter bots' behavior based on attention weights and a text-graphic interaction module to enable information exchange across modalities in the learning process. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two widely adopted datasets and the results are consistent with previous work. |
BotPercent: Estimating Bot Populations in Twitter Communities (2023.findings-emnlp)
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| Challenge: | Existing approaches to bot detection are agnostic to social environments the bots operate in . however, standard approaches are not a good fit for the social environments they operate in. |
| Approach: | They propose a method that estimates the percentage of Twitter bots given a community . they use Twitter bot detection datasets and feature-, text-, and graph-based models adjusted to a particular community based on Twitter . |
| Outcome: | The proposed method achieves state-of-the-art in community-level Twitter bot detection across balanced and imbalanced class distribution settings. |