Spot the bot: Coarse-Grained Partition of Semantic Paths for Bots and Humans
This addresses the need to detect bot-generated content for users in online platforms, but it is incremental as it builds on existing methods for text analysis.
The paper tackled the problem of distinguishing human-written from bot-generated texts by comparing coarse-grained partitions of semantic paths, finding that their structures and clusterizations differ across Russian, English, German, and Vietnamese languages.
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.