Spot the bot: Coarse-Grained Partition of Semantic Paths for Bots and Humans
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Abstract (arXiv)
Nowadays, technology is rapidly advancing: bots are writing comments, articles, and reviews. Due to this fact, it is crucial to know if the text was written by a human or by a bot. This paper focuses on comparing structures of the coarse-grained partitions of semantic paths for human-written and bot-generated texts. We compare the clusterizations of datasets of n-grams from literary texts and texts generated by several bots. The hypothesis is that the structures and clusterizations are different. Our research supports the hypothesis. As the semantic structure may be different for different languages, we investigate Russian, English, German, and Vietnamese languages.
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References · 6
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2022mGPT: Few-Shot Learners Go Multilingualcited by 141
- 2018Deep Neural Networks for Bot Detectioncited by 37
- 2020Twitter Bot Detection Using Bidirectional Long Short-term Memory Neural Networks and Word Embeddingscited by 25
- 2021Detecting Bot-Generated Text by Characterizing Linguistic Accommodation in Human-Bot Interactionscited by 8
- 2021Understanding Transformers for Bot Detection in Twittercited by 1
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