Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Social Network Analysis and Mining
Erschienen in: Social Network Analysis and Mining 1/2020

01.12.2020 | Review Article

A systematic mapping on automatic classification of fake news in social media

verfasst von: João Victor de Souza, Jorão Gomes Jr., Fernando Marques de Souza Filho, Alessandreia Marta de Oliveira Julio, Jairo Francisco de Souza

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Social media has become the primary source for rumor spreading, and information quality is an increasingly important issue in this context. In the last years, many researchers have been working on methods to improve the rumor classification, especially on the identification of fake news in social media, with good results. However, due to the complexity of natural language, this task presents difficult challenges, and many research opportunities. This survey analyzes 87 distinct publications, which were systematically selected out of 1333 candidates. This work covers eight years of research on fake news applied in social media and presents the main methods, text and user features, and datasets used in literature.
Vorheriger Artikel Enhancing information retrieval performance by using social analysis
Nächster Artikel Deep learning for misinformation detection on online social networks: a survey and new perspectives

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Anhänge
Nur mit Berechtigung zugänglich
Fußnoten
4
Data are available only upon request to the author.
 
Literatur
Agrawal T, Gupta R, Narayanan S (2017) Multimodal detection of fake social media use through a fusion of classification and pairwise ranking systems. In: 25th European signal processing conference (EUSIPCO), IEEE, pp 1045–1049
Ajao O, Bhowmik D, Zargari S (2018) Fake news identification on twitter with hybrid cnn and rnn models. In: Proceedings of the 9th international conference on social media and society, ACM, pp 226–230
Ali J, Khan R, Ahmad N, Maqsood I (2012) Random forests and decision trees. Int J Comput Sci Issues 9(5):272
Allcott H, Gentzkow M (2017) Social media and fake news in the 2016 election. J Econ Perspect 31(2):211–36
Antoniadis S, Litou I, Kalogeraki V (2015) A model for identifying misinformation in online social networks. In: OTM confederated international conferences on the move to meaningful internet systems, Springer, pp 473–482
Baeth MJ, Aktas MS (2017) Detecting misinformation in social networks using provenance data. In: 13th international conference on semantics, knowledge and grids (SKG), IEEE, pp 85–89
Basharat S, Ahmad M (2017) Inferring trust from message features using linear regression and support vector machines. In: International conference on next generation computing technologies, Springer, pp 577–598
Basu A, Walters C, Shepherd M (2003) Support vector machines for text categorization. In: 36th annual hawaii international conference on system sciences, 2003. Proceedings of the IEEE, pp 7
Beğenilmiş E, Uskudarli S (2018) Organized behavior classification of tweet sets using supervised learning methods. In: Proceedings of the 8th international conference on web intelligence, mining and semantics, ACM, p 36
Bhattacharjee SD, Talukder A, Balantrapu BV (2017) Active learning based news veracity detection with feature weighting and deep-shallow fusion. In: International conference on big data (Big Data), IEEE, pp 556–565
Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022MATH
Bodnar T, Tucker C, Hopkinson K, Bilén SG (2014) Increasing the veracity of event detection on social media networks through user trust modeling. In: International Conference on Big Data (Big Data), IEEE, pp 636–643
Boididou C, Papadopoulos S, Zampoglou M, Apostolidis L, Papadopoulou O, Kompatsiaris Y (2018) Detection and visualization of misleading content on twitter. Int J Multimedia Inf Retrieval 7(1):71–86
Bond CF Jr, DePaulo BM (2006) Accuracy of deception judgments. Person Soc Psychol Rev 10(3):214–234
Bondielli A, Marcelloni F (2019) A survey on fake news and rumour detection techniques. Inf Sci 497:38–55
Buntain C, Golbeck J (2017) Automatically identifying fake news in popular twitter threads. In: International conference on smart cloud (SmartCloud), IEEE, pp 208–215
Cai G, Bi M, Liu J (2017) A novel rumor detection method based on labeled cascade propagation tree. In: 13th international conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD), IEEE, pp 2185–2194
Cai G, Wu H, Lv R (2014) Rumors detection in chinese via crowd responses. In: Proceedings of the 2014 IEEE/ACM international conference on advances in social networks analysis and mining, IEEE Press, pp 912–917
Castelvecchi D (2016) Can we open the black box of AI? Nature News 538(7623):20
Castillo C, Mendoza M, Poblete B (2013) Predicting information credibility in time-sensitive social media. Internet Res 23(5):560–588
Castillo C, Mendoza M, Poblete B (2011) Information credibility on twitter. In: Proceedings of the 20th international conference on World wide web, ACM, pp 675–684
Chang C, Zhang Y, Szabo C, Sheng Q.Z (2016) Extreme user and political rumor detection on twitter. In: International conference on advanced data mining and applications, Springer, pp 751–763
Chan-Olmsted SM, Cho M, Lee S (2013) User perceptions of social media: a comparative study of perceived characteristics and user profiles by social media. Online J Commun Media Technol 3(4):149–178
Chatterjee R, Agarwal S (2016) Twitter truths: Authenticating analysis of information credibility. In: 3rd International conference on computing for sustainable global development (INDIACom), IEEE, pp 2352–2357
Chen W, Yeo CK, Lau CT, Lee BS (2016) Behavior deviation: an anomaly detection view of rumor preemption. In: 7th annual information technology, electronics and mobile communication conference (IEMCON), IEEE, pp 1–7
Ciampaglia GL, Shiralkar P, Rocha LM, Bollen J, Menczer F, Flammini A (2015) Computational fact checking from knowledge networks. PloS One 10(6):e0128193
Conroy NJ, Rubin VL, Chen Y (2015) Automatic deception detection: methods for finding fake news. Proc Assoc Inf Sci Technol 52(1):1–4
Cooper ID (2016) What is a “mapping study?”. J Med Libr Assoc 104(1):76
Costa C, Murta L (2013) Version control in distributed software development: a systematic mapping study. In: 8th international conference on global software engineering (ICGSE), IEEE, pp 90–99
Della Vedova ML, Tacchini E, Moret S, Ballarin G, DiPierro M, de Alfaro L (2018) Automatic online fake news detection combining content and social signals. In: 2018 22nd conference of open innovations association (FRUCT), IEEE, pp 272–279
Dong M, Yao L, Wang X, Benatallah B, Sheng QZ, Huang H (2018) Dual: a deep unified attention model with latent relation representations for fake news detection. In: International conference on web information systems engineering, Springer, pp 199–209
Fernández-Reyes FC, Shinde S (2018) Evaluating deep neural networks for automatic fake news detection in political domain. In: Ibero-American conference on artificial intelligence, Springer, pp 206–216
Fernandez-Sotos P, Torio I, Fernandez-Caballero A, Navarro E, Gonzalez P, Dompablo M, Rodriguez-Jimenez R (2019) Social cognition remediation interventions: a systematic mapping review. PloS One 14:6
Figueira Á, Sandim M, Fortuna P (2016) An approach to relevancy detection: contributions to the automatic detection of relevance in social networks. In: New advances in information systems and technologies, Springer, pp 89–99
Fortnow L et al (2009) Time for computer science to grow up. Commun ACM 52(8):33–35
Fruchterman TM, Reingold EM (1991) Graph drawing by force-directed placement. Softw Pract Exp 21(11):1129–1164
Garg A, Syal V, Gudlani P, Patel D (2017) Mining credible and relevant news from social networks. In: International conference on big data analytics, Springer, pp 90–102
Ghafari SM, Yakhchi S, Beheshti A, Orgun M (2018) Social context-aware trust prediction: methods for identifying fake news. In: International conference on web information systems engineering, Springer, pp 161–177
Giasemidis G, Kaplis N, Agrafiotis I, Nurse J (2018) A semi-supervised approach to message stance classification. IEEE Trans Knowl Data Eng 32:1–11
Giasemidis G, Singleton C, Agrafiotis I, Nurse JR, Pilgrim A, Willis C, Greetham DV (2016) Determining the veracity of rumours on twitter. In: International conference on social informatics, Springer, pp 185–205
Giglietto F, Iannelli L, Valeriani A, Rossi L (2019) ‘fake news’ is the invention of a liar: how false information circulates within the hybrid news system. Curr Sociol 67(4):625–642
Granik M, Mesyura V (2017) Fake news detection using naive bayes classifier. In: First Ukraine conference on electrical and computer engineering (UKRCON), IEEE, pp 900–903
Guo Q, Huang WW, Huang K, Liu X (2015) Information credibility: a probabilistic graphical model for identifying credible influenza posts on social media. In: International conference on smart health, Springer, pp 131–142
Gupta S, Thirukovalluru R, Sinha M, Mannarswamy S (2018) Cimtdetect: a community infused matrix-tensor coupled factorization based method for fake news detection. In: 2018 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), IEEE, pp 278–281
Gupta M, Zhao P, Han J (2012) Evaluating event credibility on twitter. In: Proceedings of the 2012 SIAM international conference on data mining, SIAM, pp 153–164
Habib A, Asghar MZ, Khan A, Habib A, Khan A (2019) False information detection in online content and its role in decision making: a systematic literature review. Soc Netw Anal Min 9(1):50
Haddaway NR, Bernes C, Jonsson BG, Hedlund K (2016) The benefits of systematic mapping to evidence-based environmental management. Ambio 45(5):613–620
Hamdi T, Slimi H, Bounhas I, Slimani Y (2020) A hybrid approach for fake news detection in twitter based on user features and graph embedding. In: International conference on distributed computing and internet technology, Springer, pp 266–280
Hashish IA, Motta G, Ma T, Liu K (2017) An analysis of social data credibility for services systems in smart cities–credibility assessment and classification of tweets. In: cloud infrastructures, services, and IoT Systems for Smart Cities, Springer, pp 119–130
Hassan D (2018) A text mining approach for evaluating event credibility on twitter. In: 2018 IEEE 27th international conference on enabling technologies: infrastructure for collaborative enterprises (WETICE), IEEE, pp 171–174
Helmstetter S, Paulheim H (2018) Weakly supervised learning for fake news detection on twitter. In: 2018 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), IEEE, pp 274–277
Huh M, Liu A, Owens A, Efros AA (2018) Fighting fake news: image splice detection via learned self-consistency. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 101–117
Jain S, Sharma V, Kaushal R (2016) Towards automated real-time detection of misinformation on twitter. In: International conference on advances in computing, communications and informatics (ICACCI), IEEE, pp 2015–2020
Janssen B, Habib M, Van Keulen M (2017) Truth assessment of objective facts extracted from tweets: A case study on world cup 2014 game facts. In: 13th International Conference on Web Information Systems and Technologies, WEBIST 2017. SCITEPRESS
Jin Z, Cao J, Zhang Y, Zhou J, Tian Q (2017) Novel visual and statistical image features for microblogs news verification. IEEE Trans Multimedia 19(3):598–608
Jin Z, Cao J, Jiang YG, Zhang Y (2014) News credibility evaluation on microblog with a hierarchical propagation model. In: International conference on data mining (ICDM), IEEE, pp 230–239
Kawabe T, Namihira Y, Suzuki K, Nara M, Sakurai Y, Tsuruta S, Knauf R (2015) Tweet credibility analysis evaluation by improving sentiment dictionary. In: Congress on evolutionary computation (CEC), IEEE, pp 2354–2361
Kawabe T, Namihira Y, Suzuki K, Nara M, Yamamoto Y, Tsuruta S, Knauf R (2015) A part-of-speech based sentiment classification method considering subject-predicate relation. In: International conference on systems, man, and cybernetics (SMC), IEEE, pp 999–1004
Kim J (2019) Author-based analysis of conference versus journal publication in computer science. J Assoc Inf Sci Technol 70(1):71–82
Kitchenham B (2004) Procedures for performing systematic reviews. Keele UK Keele Univ 33(2004):1–26
Kitchenham B (2010) What’s up with software metrics?-a preliminary mapping study. J Syst Softw 83(1):37–51
Knight W (2017) The dark secret at the heart of AL. Technol Rev 120(3):54–61
Krishnan S, Chen M (2018) Identifying tweets with fake news. In: 2018 IEEE international conference on information reuse and integration (IRI), IEEE, pp 460–464
Kumar KK, Geethakumari G (2014) Detecting misinformation in online social networks using cognitive psychology. Human-Centric Comput Inf Sci 4(1):14
Kwon S, Cha M, Jung K (2017) Rumor detection over varying time windows. PloS One 12:1
Lendvai P, Reichel UD, Declerck T (2016) Factuality drift assessment by lexical markers in resolved rumors. In: SEMANTiCS 2016. ACM Press
Liang G, Yang J, Xu C (2016) Automatic rumors identification on sina weibo. In: 12th International conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD), IEEE, pp 1523–1531
Lim WY, Lee ML, Hsu W (2016) Claimfinder: a framework for identifying claims in microblogs. In: # Microposts, pp 13–20
Liu Q, Yu F, Wu S, Wang L (2018) Mining significant microblogs for misinformation identification: an attention-based approach. ACM Trans Intell Syst Technol 9(5):50
Liu Y, Jin X, Shen H, Cheng X (2017) Do rumors diffuse differently from non-rumors? a systematically empirical analysis in sina weibo for rumor identification. In: Pacific-Asia conference on knowledge discovery and data mining, Springer, pp 407–420
Ma J, Gao W, Wei Z, Lu Y, Wong KF (2015) Detect rumors using time series of social context information on microblogging websites. In: Proceedings of the 24th ACM international on conference on information and knowledge management, ACM, pp 1751–1754
Ma J, Gao W, Wong KF (2017) Detect rumors in microblog posts using propagation structure via kernel learning. In: Proceedings of the 55th annual meeting of the association for computational linguistics (Volume 1: Long Papers), vol. 1, pp 708–717
Ma B, Lin D, Cao D (2017) Content representation for microblog rumor detection. In: advances in computational intelligence systems, Springer, pp 245–251
Mendels G, Levitan SI, Lee KZ, Hirschberg J (2017) Hybrid acoustic-lexical deep learning approach for deception detection. In: INTERSPEECH, pp 1472–1476
Meyer B, Choppy C, Staunstrup J, Van Leeuwen J (2009) Research evaluation for computer science. Commun ACM 52(4):31–34
Mitra T, Gilbert E (2015) Credbank: a large-scale social media corpus with associated credibility annotations. In: Ninth International AAAI Conference on Web and Social Media
Moin R, Zahoor-ur Rehman KM, Alzahrani ME, Saleem MQ (2018) Framework for rumors detection in social media. Int J Adv Comput Sci Appl 9:5
Mondal T, Pramanik P, Bhattacharya I, Boral N, Ghosh S (2018) Analysis and early detection of rumors in a post disaster scenario. Inf Syst Front 2018:1–19
Nabipourshiri R, Abu-Salih B, Wongthongtham P (2018) Tree-based classification to users’ trustworthiness in osns. In: Proceedings of the 2018 10th international conference on computer and automation engineering, ACM, pp 190–194
Neiva FW, David JMN, Braga R, Campos F (2016) Towards pragmatic interoperability to support collaboration: a systematic review and mapping of the literature. Inf Softw Technol 72:137–150
Nguyen T.N, Li C, Niederée C (2017) On early-stage debunking rumors on twitter: leveraging the wisdom of weak learners. In: international conference on social informatics, Springer, pp 141–158
Okoro E, Abara B, Umagba A, Ajonye A, Isa Z (2018) A hybrid approach to fake news detection on social media. Nigerian J Technol 37(2):454–462
Özgöbek Ö, Gulla JA (2017) Towards an understanding of fake news. In: CEUR workshop proceedings
Pal A, Chua AY (2018) Classification of rumors and counter-rumors. In: 4th international conference on information management (ICIM), IEEE, pp 81–85
Pan J.Z, Pavlova S, Li C, Li N, Li Y, Liu J (2018) Content based fake news detection using knowledge graphs. In: International semantic web conference, Springer, pp 669–683
Parikh S.B, Atrey P.K (2018) Media-rich fake news detection: a survey. In: 2018 IEEE conference on multimedia information processing and retrieval (MIPR), IEEE, pp 436–441
Patterson D, Snyder L, Ullman J (1999) Evaluating computer scientists and engineers for promotion and tenure. Comput Res News 1999:6
Perryman CL (2016) Mapping studies. J Med Libr Assoc 104(1):79
Petersen K, Vakkalanka S, Kuzniarz L (2015) Guidelines for conducting systematic mapping studies in software engineering: an update. Inf Softw Technol 64:1–18
Petersen K, Feldt R, Mujtaba S, Mattsson M (2008) Systematic mapping studies in software engineering. In: 12th international conference on evaluation and assessment in software engineering (EASE) 12, pp 1–10
Petticrew M, Roberts H (2006) Systematic reviews in the social sciences: a practical guide. 2006. Malden USA: Blackwell Publishing CrossRef Google Scholar
Qian F, Gong C, Sharma K, Liu Y (2018) Neural user response generator: fake news detection with collective user intelligence. In: IJCAI, pp 3834–3840
Rajdev M, Lee K (2015) Fake and spam messages: Detecting misinformation during natural disasters on social media. In: IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (WI-IAT), IEEE, vol 1, pp 17–20
Rubin VL, Chen Y, Conroy NJ (2015) Deception detection for news: three types of fakes. In: Proceedings of the 78th ASIS&T annual meeting: information science with impact: research in and for the community, p. 83. American society for information science
Ruchansky N, Seo S, Liu Y (2017) Csi: A hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 797–806
Sabbeh SF, Baatwah SY (2018) Arabic news credibility on twitter: an enhanced model using hybrid features. J Theor Appl Inf Technol 96:8
Sahana V, Pias AR, Shastri R, Mandloi S (2015) Automatic detection of rumoured tweets and finding its origin. In: International conference on computing and network communications (CoCoNet), IEEE, pp 607–612
Sampson J, Morstatter F, Wu L, Liu H (2016) Leveraging the implicit structure within social media for emergent rumor detection. In: Proceedings of the 25th ACM International on conference on information and knowledge management, ACM, pp 2377–2382
Shabani S, Sokhn M (2018) Hybrid machine-crowd approach for fake news detection. In: 2018 IEEE 4th international conference on collaboration and internet computing (CIC), IEEE, pp 299–306
Shariff S.M, Zhang X (2014) A survey on deceptions in online social networks. In: Computer and information sciences (ICCOINS), 2014 International Conference on IEEE, pp 1–6
Shu K, Sliva A, Wang S, Tang J, Liu H (2017) Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor Newsl 19(1):22–36
Shu K, Mahudeswaran D, Wang S, Lee D, Liu H (2018) Fakenewsnet: a data repository with news content, social context and dynamic information for studying fake news on social media. arXiv preprint arXiv:​1809.​01286
Sicilia R, Giudice SL, Pei Y, Pechenizkiy M, Soda P (2018) Twitter rumour detection in the health domain. Expert Syst Appl 110:33–40
Tacchini E, Ballarin G, Della Vedova M.L, Moret S, de Alfaro L (2017) Some like it hoax: automated fake news detection in social networks. arXiv preprint arXiv:​1704.​07506
Tandoc EC Jr, Lim ZW, Ling R (2018) Defining fake news: a typology of scholarly definitions. Dig Journal 6(2):137–153
Teyssou D, Spangenberg J (2019) Video verification: motivation and requirements. In: Video verification in the fake news era, Springer, pp 3–14
Tolosi L, Tagarev A, Georgiev G (2016) An analysis of event-agnostic features for rumour classification in twitter. In: Tenth International AAAI conference on web and social media
Torky M, Meligy A, Ibrahim H, Hassanein AE (2017) Colored petri net model for blocking misleading information propagation in online social networks. In: International conference on advanced intelligent systems and informatics, Springer, pp 600–609
Ulicny B, Kokar M.M (2011) Toward formal reasoning with epistemic policies about information quality in the twittersphere. In: Proceedings of the 14th international conference on information fusion (FUSION), IEEE, pp 1–8
Vardi MY (2010) Revisiting the publication culture in computing research. Commun ACM 53(3):5–5
Viviani M, Pasi G (2017) Credibility in social media: opinions, news, and health information-a survey. Wiley Interdiscipl Rev Data Min Knowl Discov 7(5):e1209
Vohra M, Kakkar M (2018) Detection of rumor in social media. In: 2018 8th international conference on cloud computing, data science & engineering (Confluence), IEEE, pp 485–490
Vosoughi S, Mohsenvand M, Roy D (2017) Rumor gauge: predicting the veracity of rumors on twitter. ACM Trans Knowl Discov Data (TKDD) 11(4):50
Vrettas G, Sanderson M (2015) Conferences versus journals in computer science. J Assoc Inf Sci Technol 66(12):2674–2684
Wang Y, Ma F, Jin Z, Yuan Y, Xun G, Jha K, Su L, Gao J (2018) Eann: event adversarial neural networks for multi-modal fake news detection. In: Proceedings of the 24th acm sigkdd international conference on knowledge discovery & data mining, ACM, pp 849–857
Wang S, Moise I, Helbing D, Terano T (2017) Early signals of trending rumor event in streaming social media. In: 41st annual computer software and applications conference (COMPSAC), IEEE, vol 2, pp 654–659
Wang L, Wang Y, De Melo G, Weikum G (2018) Five shades of untruth: Finer-grained classification of fake news. In: 2018 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), IEEE, pp 593–594
Wu L, Li J, Hu X, Liu H (2017) Gleaning wisdom from the past: Early detection of emerging rumors in social media. In: Proceedings of the 2017 SIAM international conference on data mining, SIAM, pp 99–107
Wu L, Li J, Morstatter F, Liu H (2018) Toward relational learning with misinformation. In: Proceedings of the 2018 SIAM international conference on data mining, SIAM, pp 711–719
Wu L, Liu H (2018) Tracing fake-news footprints: Characterizing social media messages by how they propagate. In: Proceedings of the Eleventh ACM international conference on web search and data mining, ACM pp. 637–645
Wu S, Liu Q, Liu Y, Wang L, Tan T (2016) Information credibility evaluation on social media. In: AAAI, pp 4403–4404
Wu L, Rao Y, Yu H, Wang Y, Nazir A (2018) False information detection on social media via a hybrid deep model. In: International conference on social informatics, Springer, pp 323–333
Xie B, Wang Y, Chen C, Xiang Y (2016) Gatekeeping behavior analysis for information credibility assessment on weibo. In: International conference on network and system security, Springer, pp 483–496
Yavary A, Sajedi H, Abadeh MS (2020) Information verification in social networks based on user feedback and news agencies. Soc Netw Anal Min 10(1):2
Yavary A, Sajedi H (2018) Rumor detection on twitter using extracted patterns from conversational tree. In: 4th international conference on web research (ICWR), IEEE, pp 78–85
Yong Z, Yao H, Wu Y (2018) Rumors detection in sina weibo based on text and user characteristics. In: 2018 2nd IEEE advanced information management, communicates, electronic and automation control conference (IMCEC), IEEE, pp 1380–1386
Yoshida Z, Aritsugi M (2018) Rumor detection in twitter with social graph structures. In: Third international congress on information and communication technology, Springer, pp 589–598
Yu F, Liu Q, Wu S, Wang L, Tan T (2017) A convolutional approach for misinformation identification. In: Proceedings of the 26th international joint conference on artificial intelligence, AAAI Press, pp 3901–3907
Zhang DY, Han R, Wang D, Huang C (2016) On robust truth discovery in sparse social media sensing. In: International conference on big data (Big Data), IEEE, pp 1076–1081
Zhang Y, Chen W, Yeo CK, Lau CT, Lee BS (2016) A distance-based outlier detection method for rumor detection exploiting user behaviorial differences. In: International conference on data and software engineering (ICoDSE), IEEE, pp 1–6
Zhang H, Li J, Xiao Y (2013) Hadoop cellular automata for identifying rumor in social networks. In: International conference on information science and cloud computing companion (ISCC-C), IEEE, pp 37–42
Zhang Q, Yang Y, Ma H, Wu YN (2019) Interpreting cnns via decision trees. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6261–6270
Zhang Q, Zhang S, Dong J, Xiong J, Cheng X (2015) Automatic detection of rumor on social network. In: Natural language processing and Chinese computing, Springer, pp 113–122
Zhao Z, Resnick P, Mei Q (2015) Enquiring minds: early detection of rumors in social media from enquiry posts. In: Proceedings of the 24th international conference on world wide web, pp 1395–1405. International world wide web conferences steering committee
Zhou L, Burgoon JK, Nunamaker JF, Twitchell D (2004) Automating linguistics-based cues for detecting deception in text-based asynchronous computer-mediated communications. Group Decis Negot 13(1):81–106
Zhou X, Cao J, Jin Z, Xie F, Su Y, Chu D, Cao X, Zhang J (2015) Real-time news certification system on sina weibo. In: Proceedings of the 24th international conference on world wide web, ACM, pp 983–988
Zou J, Fekri F, McLaughlin SW (2015) Mining streaming tweets for real-time event credibility prediction in twitter. In: International conference on advances in social networks analysis and mining, ACM, pp 1586–1589
Zubiaga A, Ji H (2014) Tweet, but verify: epistemic study of information verification on twitter. Soc Netw Anal Min 4(1):163
Zubiaga A, Liakata M, Procter R, Hoi GWS, Tolmie P (2016) Analysing how people orient to and spread rumours in social media by looking at conversational threads. PloS One 11(3):e0150989
Zubiaga A, Aker A, Bontcheva K, Liakata M, Procter R (2018) Detection and resolution of rumours in social media: a survey. ACM Comput Surv (CSUR) 51(2):32
Zubiaga A, Liakata M, Procter R (2017) Exploiting context for rumour detection in social media. In: International conference on social informatics, Springer, pp 109–123
Metadaten
Titel
A systematic mapping on automatic classification of fake news in social media
verfasst von
João Victor de Souza
Jorão Gomes Jr.
Fernando Marques de Souza Filho
Alessandreia Marta de Oliveira Julio
Jairo Francisco de Souza
Publikationsdatum
01.12.2020
Verlag
Springer Vienna
Erschienen in
Social Network Analysis and Mining / Ausgabe 1/2020
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-020-00659-2

Weitere Artikel der Ausgabe 1/2020

Social Network Analysis and Mining 1/2020 Zur Ausgabe

Original Article

ArAutoSenti: automatic annotation and new tendencies for sentiment classification of Arabic messages

Original Article

A novel framework based on bi-objective optimization and LAN2FIS for Twitter sentiment analysis

Original Paper

Channel retrieval: finding relevant broadcasters on Telegram

Original Article

FauxWard: a graph neural network approach to fauxtography detection using social media comments

Original Article

A cross-country study on cultural similarities based on book preferences

Correction

Correction to: Diachronic equivalence: an examination of the international news network