The DOI matches its Crossref record; 40 of the 42 references it cites were found in a registry or an index.
Tweet, but verify: epistemic study of information verification on Twitter
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“This is what Zubiaga and Ji (2014) found in their study, suggesting that verification work performed by crowd workers tends to favour selection of true labels for inaccurate information.”
From Detection and Resolution of Rumours in Social Media: A Survey (2019) · cites this paper as Zubiaga and Ji 2014
The cited source says what the claim attributes to it.
In the citing paper
“Annotation of credibility perceptions has also been done in previous work, determining how credible claims appear to people (Mitra and Gilbert 2015;Zubiaga and Ji 2014); this could be useful in the context of rumours to identify those that are likely to be misleading for people; however, it has not yet been applied in the context of rumours.”
From Detection and Resolution of Rumours in Social Media: A Survey (2019) · cites this paper as Zubiaga and Ji 2014
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
Content Analysis: An Introduction to its Methodology.
Shelley, M., & Krippendorff, K. (1984) · Journal of the American Statistical Association · doi:10.2307/2288384 · Cited by 24,351
The Elaboration Likelihood Model of Persuasion
Petty, R. E., & Cacioppo, J. T. (1986) · Advances in Experimental Social Psychology · doi:10.1016/s0065-2601(08)60214-2 · Cited by 8,822
Book Review: The Psychology of Attitudes
Chakravarti, D., Eagly, A. H., & Chaiken, S. (1997) · Journal of Marketing Research · doi:10.2307/3151869 · Cited by 7,173
What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., & Moon, S. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772751 · Cited by 6,652
An overview of content analysis
Stemler, S. (2020) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · doi:10.7275/z6fm-2e34 · Cited by 3,017
Misinformation and Its Correction: Continued Influence and Successful Debiasing
Lewandowsky, S., Ecker, U. K. H., Seifert, C. M., Schwarz, N., & Cook, J. (2012) · Psychological Science in the Public Interest · doi:10.1177/1529100612451018 · Cited by 2,920
Information credibility on twitter
Castillo, C., Mendoza, M., & Poblete, B. (2011) · Proceedings of the 20th international conference on World wide web · doi:10.1145/1963405.1963500 · Cited by 2,593
Goldman, A. I. (1999) · doi:10.1093/0198238207.001.0001 · Cited by 2,206
Research works printed but not linked: 1
- Fallis D (2004) On verifying the accuracy of information: philosophical perspectives. Libr Trends 52(3):463–487
Cited by
Sorted by citations
Tandoc, E. C., Lim, Z. W., & Ling, R. S. (2017) · Digital Journalism · doi:10.1080/21670811.2017.1360143 · Cited by 2,745
Detection and Resolution of Rumours in Social Media: A Survey
Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., & Procter, R. (2019) · ACM Computing Surveys · doi:10.1145/3161603 · Cited by 862
Self-citationTucker, J. A., Guess, A., Barbera, P., Vaccari, C., Siegel, A., Sanovich, S., Stukal, D., & Nyhan, B. (2018) · doi:10.2139/ssrn.3144139 · Cited by 1,222
Zubiaga, A., Liakata, M., Procter, R., Wong Sak Hoi, G., & Tolmie, P. (2016) · PLOS ONE · doi:10.1371/journal.pone.0150989 · Cited by 722
Self-citationFake news detection: A hybrid CNN-RNN based deep learning approach
Nasir, J. A., Khan, O. S., & Varlamis, I. (2021) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2020.100007 · Cited by 568
Fame for sale: Efficient detection of fake Twitter followers
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.09.003 · Cited by 475
Too good to be true, too good not to share: the social utility of fake news
Duffy, A. M., Tandoc, E. C., & Ling, R. (2019) · Information Communication & Society · doi:10.1080/1369118x.2019.1623904 · Cited by 335
Ekström, M., & Westlund, O. (2019) · Media and Communication · doi:10.17645/mac.v7i1.1763 · Cited by 199
Fake News as a Critical Incident in Journalism
Tandoc, E. C., Jenkins, J., & Craft, S. (2018) · Journalism Practice · doi:10.1080/17512786.2018.1562958 · Cited by 188
Fighting fake news in the COVID-19 era: policy insights from an equilibrium model
Hartley, K., & Vu, M. K. (2020) · Policy Sciences · doi:10.1007/s11077-020-09405-z · Cited by 175
Combating Rumor Spread on Social Media: The Effectiveness of Refutation and Warning
Ozturk, P., Li, H., & Sakamoto, Y. (2015) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2015.288 · Cited by 126
Majerczak, P., & Strzelecki, A. (2022) · Behavioral Sciences · doi:10.3390/bs12020051 · Cited by 171
Torres, R., Gerhart, N., & Negahban, A. (2018) · ACM SIGMIS Database: the DATABASE for Advances in Information Systems · doi:10.1145/3242734.3242740 · Cited by 113
Credibility in Online Social Networks: A Survey
Alrubaian, M., Al‐Qurishi, M., Alamri, A. M., Al-Rakhami, M. S., Hassan, M. M., & Fortino, G. (2018) · IEEE Access · doi:10.1109/access.2018.2886314 · Cited by 124
Hardalov, M., Koychev, I., & Nakov, P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-44748-3_17 · Cited by 109
Ekström, M., & Westlund, O. (2019) · Oxford Research Encyclopedia of Communication · doi:10.1093/acrefore/9780190228613.013.806 · Cited by 95
A Parsimonious Language Model of Social Media Credibility Across Disparate Events
Mitra, T., Wright, G. P., & Gilbert, É. (2017) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2998181.2998351 · Cited by 80
Stone, C. B., & Wang, Q. (2018) · Topics in Cognitive Science · doi:10.1111/tops.12369 · Cited by 85
Towards Detecting Rumours in Social Media
Zubiaga, A., Liakata, M., Procter, R., Bontcheva, K., & Tolmie, P. (2015) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · doi:10.48550/arxiv.1504.04712 · Cited by 72
Self-citationCrowdsourcing the Annotation of Rumourous Conversations in Social Media
Zubiaga, A., Liakata, M., Procter, R., Bontcheva, K., & Tolmie, P. (2015) · Proceedings of the 24th International Conference on World Wide Web · doi:10.1145/2740908.2743052 · Cited by 67
Self-citationPu, X., Jiang, Q., & Fan, B. (2022) · Ocean & Coastal Management · doi:10.1016/j.ocecoaman.2022.106188 · Cited by 65
Ansar, W., & Goswami, S. (2021) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2021.100052 · Cited by 65
Twitter data analysis to assess the interest of citizens on the impact of marine plastic pollution
Otero, P., Gago, J., & Quintas, P. (2021) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2021.112620 · Cited by 57
Torres, R., Gerhart, N., & Negahban, A. (2018) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2018.499 · Cited by 56
Reuters tracer: Toward automated news production using large scale social media data
Liu, X., Nourbakhsh, A., Li, Q., Shah, S., Martin, R., & Duprey, J. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8258082 · Cited by 60
Rauchfleisch, A., Artho, X., Metag, J., Post, S., & Schäfer, M. S. (2017) · Social Media + Society · doi:10.1177/2056305117717888 · Cited by 53
A systematic mapping on automatic classification of fake news in social media
de Souza, J. V., Gomes, J., Filho, F. M. D. S., de Oliveira, A. M., & de Souza, J. F. (2020) · Social Network Analysis and Mining · doi:10.1007/s13278-020-00659-2 · Cited by 53
Lanius, C. L., Weber, R., & MacKenzie, W. I. (2021) · Social Network Analysis and Mining · doi:10.1007/s13278-021-00739-x · Cited by 47
“In Flow”! Why Do Users Share Fake News about Environmentally Friendly Brands on Social Media?
Obadă, D.-R., & Dabija, D. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19084861 · Cited by 57
Crowdsourcing Truthfulness: The Impact of Judgment Scale and Assessor Bias
Barbera, D. L., Roitero, K., Demartini, G., Mizzaro, S., & Spina, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-45442-5_26 · Cited by 43
Recursive Ground Truth Estimator for Social Data Streams
Yao, S., Amin, M. T. A., Su, L., Hu, S., Li, S., Wang, S., Zhao, Y., Abdelzaher, T., Kaplan, L., Aggarwal, C. C., et al. (2016) · ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1109/ipsn.2016.7460719 · Cited by 42
Zhu, J., Zheng, S., Kaabar, M. K. A., & Yue, X.-G. (2022) · Frontiers in Environmental Science · doi:10.3389/fenvs.2022.1052797 · Cited by 40
Herrera‐Peco, I., Jiménez-Gómez, B., Deudero, J. J. P., de Gracia, E. B., & Ruíz-Núñez, C. (2021) · Healthcare · doi:10.3390/healthcare9060662 · Cited by 44
Mining social media for newsgathering: A review
Zubiaga, A. (2019) · Online Social Networks and Media · doi:10.1016/j.osnem.2019.100049 · Cited by 40
Self-citationA Model for Identifying Misinformation in Online Social Networks
Antoniadis, S., Litou, I., & Kalogeraki, V. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-26148-5_32 · Cited by 33
Creating corroborated crisis reports from social media data through formal concept analysis
Andrews, S., Gibson, H. A., Domdouzis, K., & Akhgar, B. (2016) · Journal of Intelligent Information Systems · doi:10.1007/s10844-016-0404-9 · Cited by 31
A Unified Perspective for Disinformation Detection and Truth Discovery in Social Sensing: A Survey
Xu, F., Sheng, V. S., & Wang, M. (2021) · ACM Computing Surveys · doi:10.1145/3477138 · Cited by 28
Abstract Mindsets Increase Believability of Spatially Distant Online Messages
Sungur, H., Hartmann, T., & van Koningsbruggen, G. M. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01056 · Cited by 28
Efficient and timely misinformation blocking under varying cost constraints
Litou, I., Kalogeraki, V., Katakis, I., & Gunopulos, D. (2017) · Online Social Networks and Media · doi:10.1016/j.osnem.2017.07.001 · Cited by 27
Yan, W., Liu, Y.-L., Mamaeva, V., Dong, F., Tao, G., Li, R., & Yang, H. (2026) · Telecommunications Policy · doi:10.1016/j.telpol.2025.103117 · Cited by 28
DFKI-DKT at SemEval-2017 Task 8: Rumour Detection and Classification using Cascading Heuristics
Srivastava, A., Rehm, G., & Schneider, J. M. (2017) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s17-2085 · Cited by 26
Who's in the Crowd Matters: Cognitive Factors and Beliefs Predict Misinformation Assessment Accuracy
Kaufman, R. A., Haupt, M. R., & Dow, S. P. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555611 · Cited by 30
Location impact on source and linguistic features for information credibility of social media
Aladhadh, S., Zhang, X., & Sanderson, M. (2018) · Online Information Review · doi:10.1108/oir-03-2018-0087 · Cited by 25
Stone, C. B., & Jay, A. C. V. (2019) · Applied Cognitive Psychology · doi:10.1002/acp.3564 · Cited by 27
Trninić, D., Vukelić, A. K., & Bokan, J. (2021) · Societies · doi:10.3390/soc12010003 · Cited by 34
Facebook and Fake News in the “Anglophone Crisis” in Cameroon
Tatchou, C. N. (2020) · African Journalism Studies · doi:10.1080/23743670.2020.1812102 · Cited by 22
Identifying and Tracking Sentiments and Topics from Social Media Texts during Natural Disasters
Yang, M., Mei, J., Ji, H., wei, Z., Zhao, Z., & Chen, X. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1055 · Cited by 22
Self-citationReal-Time and Cost-Effective Limitation of Misinformation Propagation
Litou, I., Kalogeraki, V., Katakis, I., & Gunopulos, D. (2016) · IEEE International Conference on Mobile Data Management (MDM) · doi:10.1109/mdm.2016.33 · Cited by 20
Wen, N. (2019) · Journal of Risk Research · doi:10.1080/13669877.2019.1673799 · Cited by 19
Social Media and Microblogs Credibility: Identification, Theory Driven Framework, and Recommendation
Qureshi, K. A., Malick, R. A. S., & Sabih, M. (2021) · IEEE Access · doi:10.1109/access.2021.3114417 · Cited by 23
Integrating Content Authentication Support in Media Services
Katsaounidou, A. N., & Dimoulas, C. A. (2017) · IGI Global eBooks · doi:10.4018/978-1-5225-2255-3.ch254 · Cited by 19
Zhukova, E. (2022) · Visual Communication · doi:10.1177/1470357219888673 · Cited by 17
Roitero, K., Soprano, M., Fan, S., Spina, D., Mizzaro, S., & Demartini, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.06915 · Cited by 17
Early Detection of Rumor Veracity in Social Media
Dang, A., Moh’d, A., Islam, A., & Milios, E. (2019) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2019.284 · Cited by 15
Virtual lab coats: The effects of verified source information on social media post credibility
Geels, J., Graßl, P., Schraffenberger, H., Tanis, M., & Kleemans, M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0302323 · Cited by 20
A Framework to Detect Fake Tweet Images on Social Media
Parikh, S. B., Khedia, S. R., & Atrey, P. K. (2019) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm.2019.00-37 · Cited by 15
Singhal, M., Kumarswamy, N., Kinhekar, S., & Nilizadeh, S. (2023) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v17i1.22189 · Cited by 15
Attention to misleading and contentious tweets in the case of Hurricane Harvey
Cheong, S., & Babcock, M. (2020) · Natural Hazards · doi:10.1007/s11069-020-04430-w · Cited by 14
A Survey of Social Network Forensics
Karabiyik, Ü., Canbaz, M. A., Aksoy, A., Tuna, T., Akbaş, E., Gonen, B., & Aygün, R. S. (2016) · The journal of digital forensics, security and law · doi:10.15394/jdfsl.2016.1430 · Cited by 15
Jamialahmadi, S., Sahebi, I., Sabermahani, M. M., Shariatpanahi, S. P., Dadlani, A., & Maham, B. (2022) · IEEE Access · doi:10.1109/access.2022.3216835 · Cited by 14
Early Detection of Social Media Hoaxes at Scale
Zubiaga, A., & Jiang, A. (2020) · ACM Transactions on the Web · doi:10.1145/3407194 · Cited by 16
Self-citationCan the crowd judge truthfulness? A longitudinal study on recent misinformation about COVID-19
Roitero, K., Soprano, M., Portelli, B., Luise, M. D., Spina, D., Mea, V. D., Serra, G., Mizzaro, S., & Demartini, G. (2024) · Figshare · Cited by 13
The mnemonic consequences associated with sharing personal photographs on social media
Stone, C. B., & Zwolinski, A. (2022) · Memory, Mind & Media · doi:10.1017/mem.2022.6 · Cited by 13
Analytics and Evolving Landscape of Machine Learning for Emergency Response
Hong, M., & Akerkar, R. A. (2019) · Learning and analytics in intelligent systems · doi:10.1007/978-3-030-15628-2_11 · Cited by 12
Exploring Commenting Behavior in the COVID-19 Super-Topic on Weibo
Chen, Y., Wang, K.-R., Xu, W., & Huang, Y. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3451717 · Cited by 12
Crowdsourcing Incident Information for Emergency Response using Open Data Sources in Smart Cities
Zuo, F., Kurkcu, A., Özbay, K. M. A., & Gao, J. (2018) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/0361198118798736 · Cited by 12
Roitero, K., Demartini, G., Mizzaro, S., & Spina, D. (2018) · Institutional Research Information System (University of Udine) · doi:10.1000/632faketemporaryforimport · Cited by 11
Can the crowd judge truthfulness? A longitudinal study on recent misinformation about COVID-19
Roitero, K., Soprano, M., Portelli, B., Luise, M. D., Spina, D., Mea, V. D., Serra, G., Mizzaro, S., & Demartini, G. (2021) · Personal and Ubiquitous Computing · doi:10.1007/s00779-021-01604-6 · Cited by 23
Watch ’n’ Check: Towards a Social Media Monitoring Tool to Assist Fact-Checking Experts
Cerone, A., Naghizade, E., Scholer, F., Mallal, D., Skelton, R., & Spina, D. (2020) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa49011.2020.00085 · Cited by 12
Mutual Influence of Users Credibility and News Spreading in Online Social Networks
Carchiolo, V., Longheu, A., Malgeri, M., Mangioni, G., & Previti, M. (2021) · Future Internet · doi:10.3390/fi13050107 · Cited by 13
Enhancing information retrieval performance by using social analysis
Khalifi, H., Dahir, S., Qadi, A. E., & Ghanou, Y. (2020) · Social Network Analysis and Mining · doi:10.1007/s13278-020-00635-w · Cited by 10
Litou, I., Boutsis, I., & Kalogeraki, V. (2015) · Information Systems · doi:10.1016/j.is.2015.12.002 · Cited by 10
Ayyasamy, R. K., Ponnusamy, C., Bhargavi, K. N., Cherukuvada, S., Babu, G. C., Amutha, S., & Gamu, D. T. (2025) · Scientific Reports · doi:10.1038/s41598-025-25311-x · Cited by 9
Chin, Y. S., & Zanuddin, H. B. (2022) · Media Asia · doi:10.1080/01296612.2022.2067945 · Cited by 10
Mo, Z., Guo, Y., & Pan, D. (2024) · Aslib Journal of Information Management · doi:10.1108/ajim-10-2023-0439 · Cited by 10
Positive Information Diffusion for Rumor Containment in Online Social Networks
Tripathi, R., & Rao, S. (2020) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets48256.2020.9027478 · Cited by 9
TweetDIS: A Large Twitter Dataset for Natural Disasters Built using Weak Supervision
Tekumalla, R., & Banda, J. M. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020214 · Cited by 11
Rumor containment in peer-to-peer message sharing online social networks
Tripathi, R., & Rao, S. (2022) · International Journal of Data Science and Analytics · doi:10.1007/s41060-021-00293-x · Cited by 8
RPI-BLENDER TAC-KBP2013 Knowledge Base Population System
Yu, D., Li, H., Cassidy, T., Li, Q., Huang, H., Chen, Z., Ji, H., Zhang, Y., & Roth, D. (2013) · Theory and applications of categories · Cited by 8
Self-citationMeinert, J., Aker, A., & Krämer, N. (2019) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2019.313 · Cited by 7
Feng, B. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1351820 · Cited by 8
Analysis of Online Toxicity Detection Using Machine Learning Approaches
Anjum, & Katarya, R. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-8542-2_29 · Cited by 6
Using Location-Based Social Networks for Time-Constrained Information Dissemination
Litou, I., Boutsis, I., & Kalogeraki, V. (2014) · IEEE International Conference on Mobile Data Management (MDM) · doi:10.1109/mdm.2014.26 · Cited by 5
Fact-Checking, Fake News, Propaganda, and Media Bias: Truth Seeking in the Post-Truth Era
Nakov, P., & Martino, G. D. S. (2020) · doi:10.18653/v1/2020.emnlp-tutorials.2 · Cited by 7
Tekumalla, R., & Banda, J. M. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020594 · Cited by 4
Costly Displays in a Digital World: Signalling Trustworthiness on Social Media
Reimann, R. (2022) · Social Epistemology · doi:10.1080/02691728.2022.2150990 · Cited by 4
Sharma, U., & Kumar, S. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-2641-8_2 · Cited by 4
Integrating Content Authentication Support in Media Services
Katsaounidou, A. N., & Dimoulas, C. A. (2018) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/978-1-5225-7766-9.ch031 · Cited by 3
Social Media Veracity Detection System Using Calibrate Classifier
Suthanthiradevi, P., & Karthika, S. (2020) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-63467-4_7 · Cited by 3
Dey, S. K., & Dey, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4704485 · Cited by 3
Roitero, K., Soprano, M., Fan, S., Spina, D., Mizzaro, S., & Demartini, G. (2020) · Institutional Research Information System (University of Udine) · doi:10.1145/3397271.3401112 · Cited by 35
Euskahaldun: euskararen aldeko martxa baten sare sozialetako islaren bilketa eta analisia
Zubiaga, A. (2015) · EKAIA Euskal Herriko Unibertsitateko Zientzi eta Teknologi Aldizkaria · doi:10.1387/ekaia.14550 · Cited by 2
Self-citationDetecting Automatically Generated Tweets Using Lexical Analysis and Profile Credibility
Wickramarathna, N. C., & Ganegoda, G. U. (2019) · International Conference on Information Technology Research (ICITR) · doi:10.1109/icitr49409.2019.9407800 · Cited by 2
Fake News and SDG16: The Situation in Ghana
Mensah, K., Awini, G., & Mensah, G. K. (2021) · Springer eBooks · doi:10.1007/978-3-030-69770-9_15 · Cited by 2
A survey of information propaganda mechanism under the cross-medium
Wu, L., Zhang, J., & Yuan, R. A. O. (2017) · Scientia Sinica Informationis · doi:10.1360/n112017-00053 · Cited by 2
A Bayesian Social Platform for Inclusive and Evidence-Based Decision-Making
Devitt, S. K., Pearce, T. R., Chowdhury, A. K., & Mengersen, K. L. (2022) · Routledge eBooks · doi:10.4324/9780367808952-66 · Cited by 1
The brand persona:operationalizing a synthesis of brand equity and social capital
Chicotsky, B. K. (2017) · ProQuest Demo Repository · Cited by 2
Mining Social Media for Newsgathering.Preprint
Zubiaga, A. (2018) · arXiv (Cornell University) · Cited by 2
Self-citationIn Search of Credible NewsPreprint
Hardalov, M., Koychev, I., & Nakov, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.08125 · Cited by 2
Automatic Detection of Disinformation: A Systematic Mapping Study
Ertürk, M., İrkey, T., Gök, B., & Gökċen, H. (2024) · Journal of Polytechnic · doi:10.2339/politeknik.1307037 · Cited by 1
Metadata
- Authors
- Zubiaga, A., & Ji, H.
- Affiliations
- Queens College, CUNY (US)
- Rensselaer Polytechnic Institute (US)
- Corresponding author
- Zubiaga, Arkaitz
- Venue
- Social Network Analysis and Mining
- Published
- 2014-03-25
- Volume
- 4
- Issue
- 1
- ISSN
- 1869-5450 (ISSN-L)
- 1869-5469
- OpenAlex
- W2110785023
- Cited by
- 125
- References
- 42
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 10 · 2023: 5 · 2022: 15 · 2021: 15 · 2020: 16 · 2019: 14 · 2018: 15 · 2017: 10 · 2016: 7 · 2015: 9 · 2014: 1
- FWCI
- 29.31
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Social Network Analysis and Mining (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Warwick Research Archive Portal (University of Warwick) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (CAREER, W911NF-09-2-0053, 0953149)
- Rensselaer Polytechnic Institute
- Defense Advanced Research Projects Agency (FA8750-13-2-0041, FA8750, W911NF-09-2-0053)
- Army Research Laboratory (Cooperative Agreement No. W911NF-09-2-0053, W911NF, No. W911NF-09-2-0053, W911NF-09-2-0053)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Public Relations and Crisis Communication
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- fact-checking, Twitter, perceived credibility, hoaxes, misinformation, Hurricane Sandy, epistemology, trust in information, corroboration, author verification, natural disasters
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review