{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/misinformation/papers/2","list_of":"/task/misinformation","task":"Misinformation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":13,"rows_per_page":100,"rows":[101,200],"of":1282,"counts":{"archive_papers_tagged":1282,"with_a_code_link":457,"where_syntology_ran_a_sample":75,"not_listed_spam_title":0,"listed":1282,"listed_where_code_ran":75,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":63,"every_run_a_failure_of_syntologys_instrument":12,"listed_with_a_run_with_no_instrument_failure":63,"listed_every_run_a_failure_of_syntologys_instrument":12,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/misinformation","prev":"/task/misinformation","next":"/task/misinformation/papers/3","papers":[{"url":"/paper/before-it-s-too-late-a-state-space-model-for","slug":"before-it-s-too-late-a-state-space-model-for","title":"Before It's Too Late: A State Space Model for the Early Prediction of Misinformation and Disinformation Engagement","date":"2025-02-07","arxiv_id":"2502.04655","repositories_listed":1,"syntology":null},{"url":"/paper/simulating-rumor-spreading-in-social-networks","slug":"simulating-rumor-spreading-in-social-networks","title":"Simulating Rumor Spreading in Social Networks using LLM Agents","date":"2025-02-03","arxiv_id":"2502.01450","repositories_listed":1,"syntology":null},{"url":"/paper/fake-news-detection-after-llm-laundering","slug":"fake-news-detection-after-llm-laundering","title":"Fake News Detection After LLM Laundering: Measurement and Explanation","date":"2025-01-29","arxiv_id":"2501.18649","repositories_listed":1,"syntology":null},{"url":"/paper/modality-interactive-mixture-of-experts-for","slug":"modality-interactive-mixture-of-experts-for","title":"Modality Interactive Mixture-of-Experts for Fake News Detection","date":"2025-01-21","arxiv_id":"2501.12431","repositories_listed":1,"syntology":null},{"url":"/paper/hfmf-hierarchical-fusion-meets-multi-stream","slug":"hfmf-hierarchical-fusion-meets-multi-stream","title":"HFMF: Hierarchical Fusion Meets Multi-Stream Models for Deepfake Detection","date":"2025-01-10","arxiv_id":"2501.05631","repositories_listed":1,"syntology":null},{"url":"/paper/hp-bert-a-framework-for-longitudinal-study-of","slug":"hp-bert-a-framework-for-longitudinal-study-of","title":"HP-BERT: A framework for longitudinal study of Hinduphobia on social media via LLMs","date":"2025-01-07","arxiv_id":"2501.05482","repositories_listed":1,"syntology":null},{"url":"/paper/who-wrote-this-zero-shot-statistical-tests","slug":"who-wrote-this-zero-shot-statistical-tests","title":"Zero-Shot Statistical Tests for LLM-Generated Text Detection using Finite Sample Concentration Inequalities","date":"2025-01-04","arxiv_id":"2501.02406","repositories_listed":1,"syntology":null},{"url":"/paper/navigating-nuance-in-quest-for-political","slug":"navigating-nuance-in-quest-for-political","title":"Navigating Nuance: In Quest for Political Truth","date":"2025-01-01","arxiv_id":"2501.00782","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-generalization-ability-of-machine","slug":"on-the-generalization-ability-of-machine","title":"On the Generalization Ability of Machine-Generated Text Detectors","date":"2024-12-23","arxiv_id":"2412.17242","repositories_listed":1,"syntology":null},{"url":"/paper/mis-information-diffusion-and-the-financial","slug":"mis-information-diffusion-and-the-financial","title":"(Mis)information diffusion and the financial market","date":"2024-12-20","arxiv_id":"2412.16269","repositories_listed":1,"syntology":null},{"url":"/paper/iohunter-graph-foundation-model-to-uncover","slug":"iohunter-graph-foundation-model-to-uncover","title":"IOHunter: Graph Foundation Model to Uncover Online Information Operations","date":"2024-12-19","arxiv_id":"2412.14663","repositories_listed":1,"syntology":null},{"url":"/paper/concept-rot-poisoning-concepts-in-large","slug":"concept-rot-poisoning-concepts-in-large","title":"Concept-ROT: Poisoning Concepts in Large Language Models with Model Editing","date":"2024-12-17","arxiv_id":"2412.13341","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":10,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/concept-rot-poisoning-concepts-in-large#ran","syntology_url":"https://syntology.ai/paper/2412.13341","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.13341"}},"official":{"repos":["keltin13/concept-rot"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/invisible-watermarks-attacks-and-robustness","slug":"invisible-watermarks-attacks-and-robustness","title":"Invisible Watermarks: Attacks and Robustness","date":"2024-12-17","arxiv_id":"2412.12511","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":10,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/invisible-watermarks-attacks-and-robustness#ran","syntology_url":"https://syntology.ai/paper/2412.12511","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.12511"}},"official":{"repos":["tomputer-g/idl_war"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/make-satire-boring-again-reducing-stylistic","slug":"make-satire-boring-again-reducing-stylistic","title":"Make Satire Boring Again: Reducing Stylistic Bias of Satirical Corpus by Utilizing Generative LLMs","date":"2024-12-12","arxiv_id":"2412.09247","repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-fact-checking-with-vision-language","slug":"multimodal-fact-checking-with-vision-language","title":"Multimodal Fact-Checking with Vision Language Models: A Probing Classifier based Solution with Embedding Strategies","date":"2024-12-06","arxiv_id":"2412.05155","repositories_listed":1,"syntology":null},{"url":"/paper/sida-social-media-image-deepfake-detection","slug":"sida-social-media-image-deepfake-detection","title":"SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model","date":"2024-12-05","arxiv_id":"2412.04292","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/sida-social-media-image-deepfake-detection#ran","syntology_url":"https://syntology.ai/paper/2412.04292","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.04292"}},"official":null}},{"url":"/paper/textclass-benchmark-a-continuous-elo-rating","slug":"textclass-benchmark-a-continuous-elo-rating","title":"TextClass Benchmark: A Continuous Elo Rating of LLMs in Social Sciences","date":"2024-11-30","arxiv_id":"2412.00539","repositories_listed":1,"syntology":null},{"url":"/paper/icpr-2024-competition-on-multilingual-claim","slug":"icpr-2024-competition-on-multilingual-claim","title":"ICPR 2024 Competition on Multilingual Claim-Span Identification","date":"2024-11-29","arxiv_id":"2411.19579","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-meta-learning-for-robust-deepfake","slug":"adaptive-meta-learning-for-robust-deepfake","title":"Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization","date":"2024-11-12","arxiv_id":"2411.08148","repositories_listed":1,"syntology":null},{"url":"/paper/language-models-as-causal-effect-generators","slug":"language-models-as-causal-effect-generators","title":"Language Models as Causal Effect Generators","date":"2024-11-12","arxiv_id":"2411.08019","repositories_listed":1,"syntology":null},{"url":"/paper/semi-truths-a-large-scale-dataset-of-ai","slug":"semi-truths-a-large-scale-dataset-of-ai","title":"Semi-Truths: A Large-Scale Dataset of AI-Augmented Images for Evaluating Robustness of AI-Generated Image detectors","date":"2024-11-12","arxiv_id":"2411.07472","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/semi-truths-a-large-scale-dataset-of-ai#ran","syntology_url":"https://syntology.ai/paper/2411.07472","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.07472"}},"official":{"repos":["j-kruk/semitruths"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/a-graph-based-approach-to-extracting","slug":"a-graph-based-approach-to-extracting","title":"A graph-based approach to extracting narrative signals from public discourse","date":"2024-11-01","arxiv_id":"2411.00702","repositories_listed":1,"syntology":null},{"url":"/paper/online-detecting-llm-generated-texts-via","slug":"online-detecting-llm-generated-texts-via","title":"Online Detecting LLM-Generated Texts via Sequential Hypothesis Testing by Betting","date":"2024-10-29","arxiv_id":"2410.22318","repositories_listed":1,"syntology":null},{"url":"/paper/can-users-detect-biases-or-factual-errors-in","slug":"can-users-detect-biases-or-factual-errors-in","title":"Can Users Detect Biases or Factual Errors in Generated Responses in Conversational Information-Seeking?","date":"2024-10-28","arxiv_id":"2410.21529","repositories_listed":1,"syntology":null},{"url":"/paper/shallow-diffuse-robust-and-invisible","slug":"shallow-diffuse-robust-and-invisible","title":"Shallow Diffuse: Robust and Invisible Watermarking through Low-Dimensional Subspaces in Diffusion Models","date":"2024-10-28","arxiv_id":"2410.21088","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/shallow-diffuse-robust-and-invisible#ran","syntology_url":"https://syntology.ai/paper/2410.21088","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.21088"}},"official":{"repos":["liwd190019/shallow-diffuse"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/subjective-qa-measuring-subjectivity-in","slug":"subjective-qa-measuring-subjectivity-in","title":"SubjECTive-QA: Measuring Subjectivity in Earnings Call Transcripts' QA Through Six-Dimensional Feature Analysis","date":"2024-10-28","arxiv_id":"2410.20651","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/subjective-qa-measuring-subjectivity-in#ran","syntology_url":"https://syntology.ai/paper/2410.20651","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.20651"}},"official":{"repos":["gtfintechlab/subjective-qa"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/llm-robustness-against-misinformation-in","slug":"llm-robustness-against-misinformation-in","title":"LLM Robustness Against Misinformation in Biomedical Question Answering","date":"2024-10-27","arxiv_id":"2410.21330","repositories_listed":1,"syntology":null},{"url":"/paper/detection-of-human-and-machine-authored-fake","slug":"detection-of-human-and-machine-authored-fake","title":"Detection of Human and Machine-Authored Fake News in Urdu","date":"2024-10-25","arxiv_id":"2410.19517","repositories_listed":1,"syntology":null},{"url":"/paper/are-llms-good-zero-shot-fallacy-classifiers","slug":"are-llms-good-zero-shot-fallacy-classifiers","title":"Are LLMs Good Zero-Shot Fallacy Classifiers?","date":"2024-10-19","arxiv_id":"2410.15050","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/are-llms-good-zero-shot-fallacy-classifiers#ran","syntology_url":"https://syntology.ai/paper/2410.15050","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.15050"}},"official":{"repos":["panfjcharlotte98/fallacy_detection"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/the-computational-anatomy-of-humility","slug":"the-computational-anatomy-of-humility","title":"The Computational Anatomy of Humility: Modeling Intellectual Humility in Online Public Discourse","date":"2024-10-19","arxiv_id":"2410.15182","repositories_listed":1,"syntology":null},{"url":"/paper/critical-questions-generation-motivation-and","slug":"critical-questions-generation-motivation-and","title":"Critical Questions Generation: Motivation and Challenges","date":"2024-10-18","arxiv_id":"2410.14335","repositories_listed":1,"syntology":null},{"url":"/paper/teaching-models-to-balance-resisting-and","slug":"teaching-models-to-balance-resisting-and","title":"Teaching Models to Balance Resisting and Accepting Persuasion","date":"2024-10-18","arxiv_id":"2410.14596","repositories_listed":1,"syntology":null},{"url":"/paper/a-comparative-study-of-translation-bias-and","slug":"a-comparative-study-of-translation-bias-and","title":"A Comparative Study of Translation Bias and Accuracy in Multilingual Large Language Models for Cross-Language Claim Verification","date":"2024-10-14","arxiv_id":"2410.10303","repositories_listed":1,"syntology":null},{"url":"/paper/yesterday-s-news-benchmarking-multi","slug":"yesterday-s-news-benchmarking-multi","title":"Yesterday's News: Benchmarking Multi-Dimensional Out-of-Distribution Generalisation of Misinformation Detection Models","date":"2024-10-12","arxiv_id":"2410.18122","repositories_listed":1,"syntology":null},{"url":"/paper/covlm-leveraging-consensus-from-vision","slug":"covlm-leveraging-consensus-from-vision","title":"CoVLM: Leveraging Consensus from Vision-Language Models for Semi-supervised Multi-modal Fake News Detection","date":"2024-10-06","arxiv_id":"2410.04426","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-reliability-of-large-language-models","slug":"on-the-reliability-of-large-language-models","title":"On the Reliability of Large Language Models to Misinformed and Demographically-Informed Prompts","date":"2024-10-06","arxiv_id":"2410.10850","repositories_listed":1,"syntology":null},{"url":"/paper/sonar-a-synthetic-ai-audio-detection","slug":"sonar-a-synthetic-ai-audio-detection","title":"Where are we in audio deepfake detection? A systematic analysis over generative and detection models","date":"2024-10-06","arxiv_id":"2410.04324","repositories_listed":1,"syntology":null},{"url":"/paper/econ-on-the-detection-and-resolution-of","slug":"econ-on-the-detection-and-resolution-of","title":"ECon: On the Detection and Resolution of Evidence Conflicts","date":"2024-10-05","arxiv_id":"2410.04068","repositories_listed":1,"syntology":null},{"url":"/paper/take-it-easy-label-adaptive-self","slug":"take-it-easy-label-adaptive-self","title":"Take It Easy: Label-Adaptive Self-Rationalization for Fact Verification and Explanation Generation","date":"2024-10-05","arxiv_id":"2410.04002","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/take-it-easy-label-adaptive-self#ran","syntology_url":"https://syntology.ai/paper/2410.04002","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.04002"}},"official":{"repos":["jingyng/label-adaptive-self-rationalization"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/optimizing-adaptive-attacks-against-content","slug":"optimizing-adaptive-attacks-against-content","title":"Optimizing Adaptive Attacks against Watermarks for Language Models","date":"2024-10-03","arxiv_id":"2410.02440","repositories_listed":1,"syntology":null},{"url":"/paper/auction-based-regulation-for-artificial","slug":"auction-based-regulation-for-artificial","title":"Auction-Based Regulation for Artificial Intelligence","date":"2024-10-02","arxiv_id":"2410.01871","repositories_listed":1,"syntology":null},{"url":"/paper/fake-it-until-you-break-it-on-the-adversarial","slug":"fake-it-until-you-break-it-on-the-adversarial","title":"Fake It Until You Break It: On the Adversarial Robustness of AI-generated Image Detectors","date":"2024-10-02","arxiv_id":"2410.01574","repositories_listed":1,"syntology":null},{"url":"/paper/loki-an-open-source-tool-for-fact","slug":"loki-an-open-source-tool-for-fact","title":"Loki: An Open-Source Tool for Fact Verification","date":"2024-10-02","arxiv_id":"2410.01794","repositories_listed":1,"syntology":null},{"url":"/paper/threatgram-101-extreme-telegram-replies-data","slug":"threatgram-101-extreme-telegram-replies-data","title":"ThreatGram 101 - Extreme Telegram Replies Data with Threat Levels","date":"2024-09-30","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/explainable-artifacts-for-synthetic-western","slug":"explainable-artifacts-for-synthetic-western","title":"Explainable Artifacts for Synthetic Western Blot Source Attribution","date":"2024-09-27","arxiv_id":"2409.18881","repositories_listed":1,"syntology":null},{"url":"/paper/chbench-a-chinese-dataset-for-evaluating","slug":"chbench-a-chinese-dataset-for-evaluating","title":"CHBench: A Chinese Dataset for Evaluating Health in Large Language Models","date":"2024-09-24","arxiv_id":"2409.15766","repositories_listed":1,"syntology":null},{"url":"/paper/fmdllama-financial-misinformation-detection","slug":"fmdllama-financial-misinformation-detection","title":"FMDLlama: Financial Misinformation Detection based on Large Language Models","date":"2024-09-24","arxiv_id":"2409.16452","repositories_listed":1,"syntology":null},{"url":"/paper/llm-echo-chamber-personalized-and-automated","slug":"llm-echo-chamber-personalized-and-automated","title":"LLM Echo Chamber: personalized and automated disinformation","date":"2024-09-24","arxiv_id":"2409.16241","repositories_listed":1,"syntology":null},{"url":"/paper/xtrust-on-the-multilingual-trustworthiness-of","slug":"xtrust-on-the-multilingual-trustworthiness-of","title":"XTRUST: On the Multilingual Trustworthiness of Large Language Models","date":"2024-09-24","arxiv_id":"2409.15762","repositories_listed":1,"syntology":null},{"url":"/paper/2409-14285","slug":"2409-14285","title":"ESPERANTO: Evaluating Synthesized Phrases to Enhance Robustness in AI Detection for Text Origination","date":"2024-09-22","arxiv_id":"2409.14285","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-knowledge-drift-in-llms-through","slug":"understanding-knowledge-drift-in-llms-through","title":"Understanding Knowledge Drift in LLMs through Misinformation","date":"2024-09-11","arxiv_id":"2409.07085","repositories_listed":1,"syntology":null},{"url":"/paper/sequential-classification-of-misinformation","slug":"sequential-classification-of-misinformation","title":"Sequential Classification of Misinformation","date":"2024-09-07","arxiv_id":"2409.04860","repositories_listed":1,"syntology":null},{"url":"/paper/a-longitudinal-sentiment-analysis-of","slug":"a-longitudinal-sentiment-analysis-of","title":"A longitudinal sentiment analysis of Sinophobia during COVID-19 using large language models","date":"2024-08-29","arxiv_id":"2408.16942","repositories_listed":1,"syntology":null},{"url":"/paper/grounding-fallacies-misrepresenting","slug":"grounding-fallacies-misrepresenting","title":"Grounding Fallacies Misrepresenting Scientific Publications in Evidence","date":"2024-08-23","arxiv_id":"2408.12812","repositories_listed":1,"syntology":null},{"url":"/paper/conflictbank-a-benchmark-for-evaluating-the","slug":"conflictbank-a-benchmark-for-evaluating-the","title":"ConflictBank: A Benchmark for Evaluating the Influence of Knowledge Conflicts in LLM","date":"2024-08-22","arxiv_id":"2408.12076","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":8,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/conflictbank-a-benchmark-for-evaluating-the#ran","syntology_url":"https://syntology.ai/paper/2408.12076","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.12076"}},"official":{"repos":["zhaochen0110/conflictbank"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/evidence-backed-fact-checking-using-rag-and","slug":"evidence-backed-fact-checking-using-rag-and","title":"Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs","date":"2024-08-22","arxiv_id":"2408.12060","repositories_listed":1,"syntology":null},{"url":"/paper/image-tell-me-your-story-predicting-the","slug":"image-tell-me-your-story-predicting-the","title":"\"Image, Tell me your story!\" Predicting the original meta-context of visual misinformation","date":"2024-08-19","arxiv_id":"2408.09939","repositories_listed":1,"syntology":{"n":17,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/image-tell-me-your-story-predicting-the#ran","syntology_url":"https://syntology.ai/paper/2408.09939","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.09939"}},"official":{"repos":["ukplab/5pils"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/web-retrieval-agents-for-evidence-based","slug":"web-retrieval-agents-for-evidence-based","title":"Web Retrieval Agents for Evidence-Based Misinformation Detection","date":"2024-08-15","arxiv_id":"2409.00009","repositories_listed":1,"syntology":null},{"url":"/paper/fact-or-fiction-improving-fact-verification","slug":"fact-or-fiction-improving-fact-verification","title":"Fact or Fiction? Improving Fact Verification with Knowledge Graphs through Simplified Subgraph Retrievals","date":"2024-08-14","arxiv_id":"2408.07453","repositories_listed":1,"syntology":null},{"url":"/paper/crowd-intelligence-for-early-misinformation","slug":"crowd-intelligence-for-early-misinformation","title":"Crowd Intelligence for Early Misinformation Prediction on Social Media","date":"2024-08-08","arxiv_id":"2408.04463","repositories_listed":1,"syntology":null},{"url":"/paper/tackling-noisy-clients-in-federated-learning","slug":"tackling-noisy-clients-in-federated-learning","title":"Tackling Noisy Clients in Federated Learning with End-to-end Label Correction","date":"2024-08-08","arxiv_id":"2408.04301","repositories_listed":1,"syntology":{"n":14,"n_ran":12,"n_constructed":0,"n_ran_checked":9,"n_instrument":3,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":3,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/tackling-noisy-clients-in-federated-learning#ran","syntology_url":"https://syntology.ai/paper/2408.04301","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.04301"}},"official":{"repos":["sprinter1999/fedelc"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/a-logical-fallacy-informed-framework-for","slug":"a-logical-fallacy-informed-framework-for","title":"A Logical Fallacy-Informed Framework for Argument Generation","date":"2024-08-07","arxiv_id":"2408.03618","repositories_listed":1,"syntology":null},{"url":"/paper/guiding-sentiment-analysis-with-hierarchical","slug":"guiding-sentiment-analysis-with-hierarchical","title":"Guiding Sentiment Analysis with Hierarchical Text Clustering: Analyzing the German X/Twitter Discourse on Face Masks in the 2020 COVID-19 Pandemic","date":"2024-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/generalized-tampered-scene-text-detection-in","slug":"generalized-tampered-scene-text-detection-in","title":"Revisiting Tampered Scene Text Detection in the Era of Generative AI","date":"2024-07-31","arxiv_id":"2407.21422","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/generalized-tampered-scene-text-detection-in#ran","syntology_url":"https://syntology.ai/paper/2407.21422","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.21422"}},"official":{"repos":["qcf-568/ostf"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pixelmod-improving-soft-moderation-of-visual","slug":"pixelmod-improving-soft-moderation-of-visual","title":"PIXELMOD: Improving Soft Moderation of Visual Misleading Information on Twitter","date":"2024-07-30","arxiv_id":"2407.20987","repositories_listed":1,"syntology":null},{"url":"/paper/can-editing-llms-inject-harm","slug":"can-editing-llms-inject-harm","title":"Can Editing LLMs Inject Harm?","date":"2024-07-29","arxiv_id":"2407.20224","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":3,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/can-editing-llms-inject-harm#ran","syntology_url":"https://syntology.ai/paper/2407.20224","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.20224"}},"official":null}},{"url":"/paper/imaginet-a-multi-content-dataset-for","slug":"imaginet-a-multi-content-dataset-for","title":"ImagiNet: A Multi-Content Benchmark for Synthetic Image Detection","date":"2024-07-29","arxiv_id":"2407.20020","repositories_listed":1,"syntology":null},{"url":"/paper/harmfully-manipulated-images-matter-in","slug":"harmfully-manipulated-images-matter-in","title":"Harmfully Manipulated Images Matter in Multimodal Misinformation Detection","date":"2024-07-27","arxiv_id":"2407.19192","repositories_listed":1,"syntology":null},{"url":"/paper/similarity-over-factuality-are-we-making","slug":"similarity-over-factuality-are-we-making","title":"Similarity over Factuality: Are we making progress on multimodal out-of-context misinformation detection?","date":"2024-07-18","arxiv_id":"2407.13488","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/similarity-over-factuality-are-we-making#ran","syntology_url":"https://syntology.ai/paper/2407.13488","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.13488"}},"official":{"repos":["stevejpapad/outcontext-misinfo-progress"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/search-engines-llms-or-both-evaluating","slug":"search-engines-llms-or-both-evaluating","title":"Evaluating Search Engines and Large Language Models for Answering Health Questions","date":"2024-07-17","arxiv_id":"2407.12468","repositories_listed":1,"syntology":null},{"url":"/paper/scientific-qa-system-with-verifiable-answers","slug":"scientific-qa-system-with-verifiable-answers","title":"Scientific QA System with Verifiable Answers","date":"2024-07-16","arxiv_id":"2407.11485","repositories_listed":1,"syntology":null},{"url":"/paper/soft-prompts-go-hard-steering-visual-language","slug":"soft-prompts-go-hard-steering-visual-language","title":"Self-interpreting Adversarial Images","date":"2024-07-12","arxiv_id":"2407.08970","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/soft-prompts-go-hard-steering-visual-language#ran","syntology_url":"https://syntology.ai/paper/2407.08970","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.08970"}},"official":{"repos":["tingwei-zhang/soft-prompts-go-hard"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-survey-of-datasets-for-information","slug":"a-survey-of-datasets-for-information","title":"A Survey of Datasets for Information Diffusion Tasks","date":"2024-07-06","arxiv_id":"2407.05161","repositories_listed":1,"syntology":null},{"url":"/paper/securespectra-safeguarding-digital-identity","slug":"securespectra-safeguarding-digital-identity","title":"SecureSpectra: Safeguarding Digital Identity from Deep Fake Threats via Intelligent Signatures","date":"2024-07-01","arxiv_id":"2407.00913","repositories_listed":1,"syntology":null},{"url":"/paper/assessing-good-bad-and-ugly-arguments","slug":"assessing-good-bad-and-ugly-arguments","title":"Assessing Good, Bad and Ugly Arguments Generated by ChatGPT: a New Dataset, its Methodology and Associated Tasks","date":"2024-06-21","arxiv_id":"2406.15130","repositories_listed":1,"syntology":null},{"url":"/paper/behonest-benchmarking-honesty-of-large","slug":"behonest-benchmarking-honesty-of-large","title":"BeHonest: Benchmarking Honesty in Large Language Models","date":"2024-06-19","arxiv_id":"2406.13261","repositories_listed":1,"syntology":null},{"url":"/paper/cram-credibility-aware-attention-modification","slug":"cram-credibility-aware-attention-modification","title":"CrAM: Credibility-Aware Attention Modification in LLMs for Combating Misinformation in RAG","date":"2024-06-17","arxiv_id":"2406.11497","repositories_listed":1,"syntology":null},{"url":"/paper/evading-ai-generated-content-detectors-using","slug":"evading-ai-generated-content-detectors-using","title":"SilverSpeak: Evading AI-Generated Text Detectors using Homoglyphs","date":"2024-06-17","arxiv_id":"2406.11239","repositories_listed":1,"syntology":null},{"url":"/paper/post-hoc-utterance-refining-method-by-entity","slug":"post-hoc-utterance-refining-method-by-entity","title":"Post-hoc Utterance Refining Method by Entity Mining for Faithful Knowledge Grounded Conversations","date":"2024-06-16","arxiv_id":"2406.10809","repositories_listed":1,"syntology":null},{"url":"/paper/raemollm-retrieval-augmented-llms-for-cross","slug":"raemollm-retrieval-augmented-llms-for-cross","title":"RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning based on Emotional Information","date":"2024-06-16","arxiv_id":"2406.11093","repositories_listed":1,"syntology":null},{"url":"/paper/cutting-through-the-noise-to-motivate-people","slug":"cutting-through-the-noise-to-motivate-people","title":"Cutting through the noise to motivate people: A comprehensive analysis of COVID-19 social media posts de/motivating vaccination","date":"2024-06-15","arxiv_id":"2407.03190","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-density-uncertainty-quantification","slug":"semantic-density-uncertainty-quantification","title":"Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space","date":"2024-05-22","arxiv_id":"2405.13845","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/semantic-density-uncertainty-quantification#ran","syntology_url":"https://syntology.ai/paper/2405.13845","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.13845"}},"official":{"repos":["cognizant-ai-labs/semantic-density-paper"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/tailoring-vaccine-messaging-with-common","slug":"tailoring-vaccine-messaging-with-common","title":"Tailoring Vaccine Messaging with Common-Ground Opinions","date":"2024-05-17","arxiv_id":"2405.10861","repositories_listed":1,"syntology":null},{"url":"/paper/credible-unreliable-or-leaked-evidence","slug":"credible-unreliable-or-leaked-evidence","title":"Credible, Unreliable or Leaked?: Evidence Verification for Enhanced Automated Fact-checking","date":"2024-04-29","arxiv_id":"2404.18971","repositories_listed":1,"syntology":null},{"url":"/paper/exposing-text-image-inconsistency-using","slug":"exposing-text-image-inconsistency-using","title":"Exposing Text-Image Inconsistency Using Diffusion Models","date":"2024-04-28","arxiv_id":"2404.18033","repositories_listed":1,"syntology":null},{"url":"/paper/inside-the-echo-chamber-linguistic","slug":"inside-the-echo-chamber-linguistic","title":"Inside the echo chamber: Linguistic underpinnings of misinformation on Twitter","date":"2024-04-24","arxiv_id":"2404.15925","repositories_listed":1,"syntology":null},{"url":"/paper/dynamicity-aware-social-bot-detection-with","slug":"dynamicity-aware-social-bot-detection-with","title":"BotDGT: Dynamicity-aware Social Bot Detection with Dynamic Graph Transformers","date":"2024-04-23","arxiv_id":"2404.15070","repositories_listed":1,"syntology":null},{"url":"/paper/misinformation-resilient-search-rankings-with","slug":"misinformation-resilient-search-rankings-with","title":"Misinformation Resilient Search Rankings with Webgraph-based Interventions","date":"2024-04-13","arxiv_id":"2404.08869","repositories_listed":1,"syntology":null},{"url":"/paper/rumour-evaluation-with-very-large-language","slug":"rumour-evaluation-with-very-large-language","title":"Rumour Evaluation with Very Large Language Models","date":"2024-04-11","arxiv_id":"2404.16859","repositories_listed":1,"syntology":null},{"url":"/paper/a-more-realistic-evaluation-setup-for","slug":"a-more-realistic-evaluation-setup-for","title":"A (More) Realistic Evaluation Setup for Generalisation of Community Models on Malicious Content Detection","date":"2024-04-02","arxiv_id":"2404.01822","repositories_listed":1,"syntology":null},{"url":"/paper/nlp-systems-that-can-t-tell-use-from-mention","slug":"nlp-systems-that-can-t-tell-use-from-mention","title":"NLP Systems That Can't Tell Use from Mention Censor Counterspeech, but Teaching the Distinction Helps","date":"2024-04-02","arxiv_id":"2404.01651","repositories_listed":1,"syntology":null},{"url":"/paper/diffusionface-towards-a-comprehensive-dataset","slug":"diffusionface-towards-a-comprehensive-dataset","title":"DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis","date":"2024-03-27","arxiv_id":"2403.18471","repositories_listed":1,"syntology":null},{"url":"/paper/numtemp-a-real-world-benchmark-to-verify","slug":"numtemp-a-real-world-benchmark-to-verify","title":"QuanTemp: A real-world open-domain benchmark for fact-checking numerical claims","date":"2024-03-25","arxiv_id":"2403.17169","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/numtemp-a-real-world-benchmark-to-verify#ran","syntology_url":"https://syntology.ai/paper/2403.17169","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.17169"}},"official":{"repos":["factiverse/QuanTemp"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/mmidr-teaching-large-language-model-to","slug":"mmidr-teaching-large-language-model-to","title":"MMIDR: Teaching Large Language Model to Interpret Multimodal Misinformation via Knowledge Distillation","date":"2024-03-21","arxiv_id":"2403.14171","repositories_listed":1,"syntology":null},{"url":"/paper/ax-to-grind-urdu-benchmark-dataset-for-urdu","slug":"ax-to-grind-urdu-benchmark-dataset-for-urdu","title":"Ax-to-Grind Urdu: Benchmark Dataset for Urdu Fake News Detection","date":"2024-03-20","arxiv_id":"2403.14037","repositories_listed":1,"syntology":null},{"url":"/paper/incentivizing-news-consumption-on-social","slug":"incentivizing-news-consumption-on-social","title":"Incentivizing News Consumption on Social Media Platforms Using Large Language Models and Realistic Bot Accounts","date":"2024-03-20","arxiv_id":"2403.13362","repositories_listed":1,"syntology":null},{"url":"/paper/correcting-misinformation-on-social-media","slug":"correcting-misinformation-on-social-media","title":"Correcting misinformation on social media with a large language model","date":"2024-03-17","arxiv_id":"2403.11169","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-conflicts-for-llms-a-survey","slug":"knowledge-conflicts-for-llms-a-survey","title":"Knowledge Conflicts for LLMs: A Survey","date":"2024-03-13","arxiv_id":"2403.08319","repositories_listed":1,"syntology":null},{"url":"/paper/unveiling-the-truth-exploring-human-gaze","slug":"unveiling-the-truth-exploring-human-gaze","title":"Unveiling the Truth: Exploring Human Gaze Patterns in Fake Images","date":"2024-03-13","arxiv_id":"2403.08933","repositories_listed":1,"syntology":null},{"url":"/paper/conspemollm-conspiracy-theory-detection-using","slug":"conspemollm-conspiracy-theory-detection-using","title":"ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model","date":"2024-03-11","arxiv_id":"2403.06765","repositories_listed":1,"syntology":null}],"record_sha256":"17c744a37be2d4879afd18e1766f6c262242d21bb470e5cc2e3f8aa7d50cd719","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}