{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-neural-networks-for-bot-detection","title":"Deep Neural Networks for Bot Detection","arxiv_id":"1802.04289","date":"2018-02-12","proceeding":null,"authors":["Sneha Kudugunta","Emilio Ferrara"],"abstract":"The problem of detecting bots, automated social media accounts governed by\nsoftware but disguising as human users, has strong implications. For example,\nbots have been used to sway political elections by distorting online discourse,\nto manipulate the stock market, or to push anti-vaccine conspiracy theories\nthat caused health epidemics. Most techniques proposed to date detect bots at\nthe account level, by processing large amount of social media posts, and\nleveraging information from network structure, temporal dynamics, sentiment\nanalysis, etc.\n  In this paper, we propose a deep neural network based on contextual long\nshort-term memory (LSTM) architecture that exploits both content and metadata\nto detect bots at the tweet level: contextual features are extracted from user\nmetadata and fed as auxiliary input to LSTM deep nets processing the tweet\ntext.\n  Another contribution that we make is proposing a technique based on synthetic\nminority oversampling to generate a large labeled dataset, suitable for deep\nnets training, from a minimal amount of labeled data (roughly 3,000 examples of\nsophisticated Twitter bots). We demonstrate that, from just one single tweet,\nour architecture can achieve high classification accuracy (AUC > 96%) in\nseparating bots from humans.\n  We apply the same architecture to account-level bot detection, achieving\nnearly perfect classification accuracy (AUC > 99%). Our system outperforms\nprevious state of the art while leveraging a small and interpretable set of\nfeatures yet requiring minimal training data.","url_abs":"http://arxiv.org/abs/1802.04289v2","url_pdf":"http://arxiv.org/pdf/1802.04289v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-neural-networks-for-bot-detection","repo_url":"https://github.com/ji10bhatt/Capstone-Social-Media-Bot-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"deep-neural-networks-for-bot-detection","repo_url":"https://github.com/warproxxx/Twitter-Bot-or-Not","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sentiment-analysis-on-122-people-passenger","task":"Sentiment Analysis","dataset":"122 People - Passenger Behavior Recognition Data","model":"lstm+bert","rank_in_archive_order":1,"of":1,"metrics":{"1:3 Accuracy":"97"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.04289","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.04289"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ji10bhatt/Capstone-Social-Media-Bot-Detection","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/warproxxx/Twitter-Bot-or-Not","reach":{"status":"unanswered"}}],"summary":{"ran_violates":2},"by_repo_kind":{"listed":{"samples":2,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":2,"samples":[{"code_sha256_prefix":"1ba99dd8abc2dcc8","entry":"hasRepeatedLetters","repo":"ji10bhatt/Capstone-Social-Media-Bot-Detection","repo_kind":"listed","path":"train_tweets.py","file_url":"https://github.com/ji10bhatt/Capstone-Social-Media-Bot-Detection/blob/HEAD/train_tweets.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"1ba99dd8abc2dcc8"}},{"code_sha256_prefix":"e58af39403184ed0","entry":"isAllCaps","repo":"ji10bhatt/Capstone-Social-Media-Bot-Detection","repo_kind":"listed","path":"train_tweets.py","file_url":"https://github.com/ji10bhatt/Capstone-Social-Media-Bot-Detection/blob/HEAD/train_tweets.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"e58af39403184ed0"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}