{"type":"ScholarlyArticle","title":"A Survey on Computational Propaganda Detection","authors":[{"name":"Giovanni Da San Martino","slug":"giovanni-da-san-martino","given":"Giovanni Da San","family":"Martino","affiliation":"Qatar Computing Research Institute , HBKU , Doha , Qatar"},{"name":"Stefano Cresci","slug":"stefano-cresci","given":"Stefano","family":"Cresci","affiliation":"Institute of Informatics and Telematics , IIT-CNR , Pisa , Italy"},{"name":"Alberto Barrón-Cedeño","slug":"alberto-barron-cedeno","given":"Alberto","family":"Barrón-Cedeño","affiliation":"DIT , Alma Mater Studiorum - Università di Bologna , Forlì , Italy"},{"name":"Seunghak Yu","slug":"seunghak-yu","given":"Seunghak","family":"Yu","affiliation":"MIT Computer Science and Artificial Intelligence Laboratory , Cambridge , MA , USA"},{"name":"Roberto Di Pietro","slug":"roberto-di-pietro","given":"Roberto Di","family":"Pietro","affiliation":"College of Science and Engineering , HBKU , Doha , Qatar"},{"name":"Preslav Nakov","slug":"preslav-nakov","given":"Preslav","family":"Nakov","affiliation":"Qatar Computing Research Institute , HBKU , Doha , Qatar"}],"abstract":"Propaganda campaigns aim at influencing people's mindset with the purpose of advancing a specific agenda. They exploit the anonymity of the Internet, the micro-profiling ability of social networks, and the ease of automatically creating and managing coordinated networks of accounts, to reach millions of social network users with persuasive messages, specifically targeted to topics each individual user is sensitive to, and ultimately influencing the outcome on a targeted issue. \r\nIn this survey, we review the state of the art on computational propaganda detection from the perspective of Natural Language Processing and Network Analysis, arguing about the need for combined efforts between these communities. We further discuss current challenges and future research directions.","doi":"10.24963/ijcai.2020/672","url":"https://ijcai.org/proceedings/2020/672","pdf":"https://ijcai.org/proceedings/2020/0672.pdf","markdown":"https://ijcai.org/proceedings/2020/672.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/672","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Survey track","track":"Survey track","number":672,"pages":{"first":4826,"last":4832},"keywords":["Natural Language Processing: general","Machine Learning: general"],"videos":[],"references":[{"position":1,"raw":"[Atanasov et al., 2019] Atanasov et al. Predicting the role of political trolls in social media. In CoNLL, pages 1023- 1034, 2019.","title":"Predicting the role of political trolls in social media","authors":"Atanasov","year":"2019","venue":"CoNLL","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Barrón-Cedeño et al., 2019] Barrón-Cedeño et al. Proppy: Organizing the news based on their propagandistic con- tent. Inf. Process. Manag., 56(5):1849-1864, 2019.","title":"Barrón-Cedeño et al. Proppy: Organizing the news based on their propagandistic content","authors":"Barrón-Cedeño","year":"2019","venue":"Inf. Process. Manag","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bolsover and Howard, 2017] Bolsover and Howard. Com- putational propaganda and political big data: Toward a more critical research agenda. Big Data, 5(4):273-276, 2017.","title":"Computational propaganda and political big data: Toward a more critical research agenda","authors":"Howard ; Bolsover, Howard Bolsover","year":"2017","venue":"Big Data","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cai et al., 2017] Cai et al. Detecting social bots by jointly modeling deep behavior and content information. In CIKM, pages 1995-1998, 2017.","title":"Detecting social bots by jointly modeling deep behavior and content information","authors":"Cai","year":"2017","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chavoshi et al., 2016] Chavoshi et al. DeBot: Twitter bot detection via warped correlation. In ICDM, pages 817- 822, 2016.","title":"DeBot: Twitter bot detection via warped correlation","authors":"Chavoshi","year":"2016","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chen et al., 2013] Chen et al. Battling the internet water army: Detection of hidden paid posters. In ASONAM, pages 116-120, 2013.","title":"Battling the internet water army: Detection of hidden paid posters","authors":"Chen","year":"2013","venue":"ASONAM","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chetan et al., 2019] Chetan et al. Corerank: Ranking to detect users involved in blackmarket-based collusive retweeting activities. In WSDM, pages 330-338, 2019.","title":"Corerank: Ranking to detect users involved in blackmarket-based collusive retweeting activities","authors":"Chetan","year":"2019","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chu et al., 2012] Chu et al. Detecting automation of Twit- ter accounts: Are you a human, bot, or cyborg? TDSC, 9(6):811-824, 2012.","title":"Detecting automation of Twitter accounts: Are you a human, bot, or cyborg?","authors":"Chu","year":"2012","venue":"TDSC","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Conserva, 2003] Conserva. Propaganda Techniques. Au- thorHouse, 2003.","title":"Propaganda Techniques","authors":"Conserva","year":"2003","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Cresci et al., 2017] Cresci et al. The paradigm-shift of so- cial spambots: Evidence, theories, and tools for the arms race. In WWW Companion, pages 963-972, 2017.","title":"The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race","authors":"Cresci","year":"2017","venue":"WWW Companion","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Cresci et al., 2020] Cresci et al. Emergent properties, mod- els, and laws of behavioral similarities within groups of Twitter users. Comput. Commun., 150:47-61, 2020.","title":"Emergent properties, models, and laws of behavioral similarities within groups of Twitter users","authors":"Cresci","year":"2020","venue":"Comput. Commun","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Da San Martino et al., 2019a] Da San Martino et al. Find- ings of the NLP4IF-2019 shared task on fine-grained pro- paganda detection. In NLP4IF@EMNLP, pages 162-170, 2019.","title":"Da San Martino et al. Findings of the NLP4IF-2019 shared task on fine-grained propaganda detection","authors":"Martino Da San","year":"2019","venue":"NLP4IF@EMNLP","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Da San Martino et al., 2019b] Da San Martino et al. Fine- grained analysis of propaganda in news articles. In EMNLP, pages 5640-5650, 2019.","title":"Da San Martino et al. Finegrained analysis of propaganda in news articles","authors":"Martino Da San","year":"2019","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Darwish et al., 2017] Darwish et al. Seminar users in the Arabic Twitter sphere. In SocInfo, pages 91-108, 2017.","title":"Seminar users in the Arabic Twitter sphere","authors":"Darwish","year":"2017","venue":"SocInfo","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Echeverrìa et al., 2018] Echeverrìa et al. LOBO: Evaluation of generalization deficiencies in Twitter bot classifiers. In ACSAC, pages 137-146, 2018.","title":"LOBO: Evaluation of generalization deficiencies in Twitter bot classifiers","authors":"Echeverrìa","year":"2018","venue":"ACSAC","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Goodfellow et al., 2014] Ian Goodfellow et al. Generative adversarial nets. In NIPS, pages 2672-2680, 2014.","title":"Generative adversarial nets","authors":"Ian Goodfellow","year":"2014","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Grimme et al., 2017] Grimme et al. Social bots: Human- like by means of human control? Big Data, 5(4):279-293, 2017.","title":"Social bots: Humanlike by means of human control?","authors":"Grimme","year":"2017","venue":"Big Data","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Habernal et al., 2017] Habernal et al. Argotario: Compu- tational argumentation meets serious games. In EMNLP, pages 7-12, 2017.","title":"Argotario: Computational argumentation meets serious games","authors":"Habernal","year":"2017","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Ins, 1938] How to detect propaganda. In Publications of the Institute for Propaganda Analysis, pages 210-218. 1938.","title":"How to detect propaganda","authors":"Ins","year":"1938","venue":"Publications of the Institute for Propaganda Analysis","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Jiang et al., 2016] Jiang et al. Inferring lockstep behavior from connectivity pattern in large graphs. Knowledge and Information Systems, 48(2):399-428, 2016.","title":"Inferring lockstep behavior from connectivity pattern in large graphs","authors":"Jiang","year":"2016","venue":"Knowledge and Information Systems","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Jowett and O'Donnell, 2012] Jowett and O'Donnell. Pro- paganda and Persuasion. SAGE, 2012.","title":"Jowett and O'Donnell. Propaganda and Persuasion","authors":"O' Jowett, Donnell","year":"2012","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Kudugunta and Ferrara, 2018] Kudugunta and Ferrara. Deep neural networks for bot detection. Information Sciences, 467:312-322, 2018.","title":"Kudugunta and Ferrara. Deep neural networks for bot detection","authors":"Ferrara Kudugunta","year":"2018","venue":"Information Sciences","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Kumar et al., 2017] Kumar et al. An army of me: Sockpup- pets in online discussion communities. In WWW, pages 857-866, 2017.","title":"An army of me: Sockpuppets in online discussion communities","authors":"Kumar","year":"2017","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Lee and Kim, 2014] Lee and Kim. Early filtering of ephemeral malicious accounts on Twitter. Comput. Com- mun., 54:48-57, 2014.","title":"Early filtering of ephemeral malicious accounts on Twitter","authors":"Lee, Kim ; ] Lee, Kim","year":"2014","venue":"Comput. Commun","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Li et al., 2016] Li et al. A survey on truth discovery. SIGKDD Explor. Newsl., 17(2):1-16, 2016.","title":"A survey on truth discovery","authors":"Li","year":"2016","venue":"SIGKDD Explor. Newsl","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Linvill and Patrick, 2018] Linvill and Patrick. Troll facto- ries: The Internet Research Agency and state-sponsored agenda building. Resource Centre on Media Freedom in Europe, 2018.","title":"Linvill and Patrick. Troll factories: The Internet Research Agency and state-sponsored agenda building","authors":"Patrick Linvill","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Liu et al., 2017] Liu et al. HoloScope: Topology-and-spike aware fraud detection. In CIKM, pages 1539-1548, 2017.","title":"HoloScope: Topology-and-spike aware fraud detection","authors":"Liu","year":"2017","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Mazza et al., 2019] Mazza et al. RTbust: Exploiting tem- poral patterns for botnet detection on Twitter. In WebSci, pages 183-192, 2019.","title":"RTbust: Exploiting temporal patterns for botnet detection on Twitter","authors":"Mazza","year":"2019","venue":"WebSci","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Mintz et al., 2009] Mintz et al. Distant supervision for re- lation extraction without labeled data. In ACL-AFNLP, pages 1003-1011, 2009.","title":"Distant supervision for relation extraction without labeled data","authors":"Mintz","year":"2009","venue":"ACL-AFNLP","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Pacheco et al., 2020] Pacheco et al. Unveiling coordinated groups behind White Helmets disinformation. In WWW Companion, pages 611-616, 2020.","title":"Unveiling coordinated groups behind White Helmets disinformation","authors":"Pacheco","year":"2020","venue":"WWW Companion","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Rangel and Rosso, 2019] Rangel and Rosso. Overview of the 7th author profiling task at PAN 2019: Bots and gender profiling in Twitter. In CLEF, 2019.","title":"Rangel and Rosso. Overview of the 7th author profiling task at PAN 2019: Bots and gender profiling in Twitter","authors":"Rosso Rangel","year":"2019","venue":"CLEF","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Rashkin et al., 2017] Rashkin et al. Truth of varying shades: Analyzing language in fake news and political fact- checking. In EMNLP, pages 2931-2937, 2017.","title":"Truth of varying shades: Analyzing language in fake news and political factchecking","authors":"Rashkin","year":"2017","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Ratkiewicz et al., 2011] Ratkiewicz et al. Truthy: Mapping the spread of astroturf in microblog streams. In WWW, pages 249-252, 2011.","title":"Ratkiewicz et al. Truthy: Mapping the spread of astroturf in microblog streams","authors":"Ratkiewicz","year":"2011","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Shu at al., 2017] Shu at al. Fake news detection on so- cial media: A data mining perspective. SIGKDD Explor. Newsl., 19(1):22-36, 2017.","title":"Shu at al. Fake news detection on social media: A data mining perspective","authors":"Shu","year":"2017","venue":"SIGKDD Explor. Newsl","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Sun et al., 2017] Sun et al. Revisiting unreasonable effec- tiveness of data in deep learning era. In ICCV, pages 843- 852, 2017.","title":"Revisiting unreasonable effectiveness of data in deep learning era","authors":"Sun","year":"2017","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Thorne and Vlachos, 2018] Thorne and Vlachos. Auto- mated fact checking: Task formulations, methods and fu- ture directions. In COLING, pages 3346-3359, 2018.","title":"Thorne and Vlachos. Automated fact checking: Task formulations, methods and future directions","authors":"Vlachos Thorne","year":"2018","venue":"COLING","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wu et al., 2020] Wu et al. Using improved conditional gen- erative adversarial networks to detect social bots on Twit- ter. IEEE Access, 8:36664-36680, 2020.","title":"Using improved conditional generative adversarial networks to detect social bots on Twitter","authors":"Wu","year":"2020","venue":"IEEE Access","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Yang et al., 2015] Yang et al. VoteTrust: Leveraging friend invitation graph to defend against social network sybils. TDSC, 13(4):488-501, 2015.","title":"VoteTrust: Leveraging friend invitation graph to defend against social network sybils","authors":"Yang","year":"2015","venue":"TDSC","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Yang et al., 2018] Yang et al. Unsupervised text style trans- fer using language model discriminators. In NIPS, pages 7287-7298, 2018.","title":"Unsupervised text style transfer using language model discriminators","authors":"Yang","year":"2018","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Yang et al., 2019] Yang et al. Arming the public with arti- ficial intelligence to counter social bots. Human Behavior and Emerging Technologies, 1(1):48-61, 2019.","title":"Arming the public with artificial intelligence to counter social bots","authors":"Yang","year":"2019","venue":"Human Behavior and Emerging Technologies","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Zellers et al., 2019] Zellers et al. Defending against neural fake news. In NIPS, pages 9051-9062, 2019.","title":"Defending against neural fake news","authors":"Zellers","year":"2019","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Zhang et al., 2016] Zhang et al. The rise of social botnets: Attacks and countermeasures. TDSC, 15(6):1068-1082, 2016.","title":"The rise of social botnets: Attacks and countermeasures","authors":"Zhang","year":"2016","venue":"TDSC","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhou et al., 2019] Zhou et al. Fake news: Fundamental the- ories, detection strategies and challenges. In WSDM, pages 836-837, 2019.","title":"Fake news: Fundamental theories, detection strategies and challenges","authors":"Zhou","year":"2019","venue":"WSDM","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}