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Cited by 47Misinformation and Its ImpactsSpam and Phishing DetectionHate Speech and Cyberbullying Detection

The DOI matches its Crossref record.

Extreme User and Political Rumor Detection on Twitter

Chang, Cheng, Zhang, Yihong, Szabo, Claudia, Sheng, Quan Z.·2016·Lecture Notes in Computer Science
DOI 10.1007/978-3-319-49586-6_54
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Crossref · 10.1007/978-3-319-49586-6_54

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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?

  • Chang et al. (2016) put emphasis on the characteristics of users who post rumours to determine veracity.

    “Chang et al. (2016) put the emphasis on the characteristics of users who post the rumours to determine the veracity.”

    From Detection and Resolution of Rumours in Social Media: A Survey (2019) · cites this paper as Chang et al. (2016)

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 15

  1. Al-Ali, A., Zualkernan, I., Aloul, F.: A mobile GPRS-sensors array for air pollution monitoring. IEEE Sensors J. 10(10), 1666–1671 (2010) · DOI
  2. Castillo, C., Mendoza, M., Poblete, B.: Information credibility on Twitter. In: Proceedings of the 20th International World Wide Web Conference, pp. 675–684 (2011) · DOI · Paper page
  3. Gupta, A., Kumaraguru, P.: Credibility ranking of tweets during high impact events. In: Proceedings of the 1st Workshop on Privacy and Security in Online Social Media, p. 2. ACM (2012) · DOI
  4. Gupta, A., Kumaraguru, P., Castillo, C., Meier, P.: TweetCred: real-time credibility assessment of content on twitter. In: Aiello, L.M., McFarland, D. (eds.) SocInfo 2014. LNCS, vol. 8851, pp. 228–243. Springer, Heidelberg (2014). doi:10.1007/978-3-319-13734-6_16 · DOI
  5. Jaho, E., Tzoannos, E., Papadopoulos, A., Sarris, N.: Alethiometer: a framework for assessing trustworthiness and content validity in social media. In: Proceedings of the 23rd International Conference on World Wide Web Companion, pp. 749–752 (2014) · DOI
  6. Kakol, M., Jankowski-Lorek, M., Abramczuk, K., Wierzbicki, A., Catasta, M.: On the subjectivity and bias of web content credibility evaluations. In: Proceedings of the 22nd International Conference on World Wide Web Companion, pp. 1131–1136 (2013) · DOI
  7. Lee, K., Caverlee, J., Kamath, K.Y., Cheng, Z.: Detecting collective attention spam. In: Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality, pp. 48–55. ACM (2012) · DOI
  8. Mathioudakis, M., Koudas, N.: TwitterMonitor: trend detection over the twitter stream. In: Proceedings of the ACM SIGMOD International Conference on Management of data, pp. 1155–1158 (2010) · DOI
  9. Metaxas, P.T., Finn, S., Mustafaraj, E.: Using twittertrails.com to investigate rumor propagation. In: Proceedings of the 18th ACM Conference Companion on Computer Supported Cooperative Work & Social Computing, pp. 69–72. ACM (2015) · DOI · Paper page
  10. Miyabe, M., Nadamoto, A., Aramaki, E.: How do rumors spread during a crisis? Analysis of rumor expansion and disaffirmation on twitter after 3.11 in Japan. Int. J. Web Inf. Syst. 10(4), 394–412 (2014) · DOI
  11. Morris, M.R., Counts, S., Roseway, A., Hoff, A., Schwarz, J.: Tweeting is believing?: understanding microblog credibility perceptions. In: Proceedings of the ACM Conference on Computer Supported Cooperative Work, pp. 441–450 (2012) · DOI · Paper page
  12. Popescu, A.-M., Pennacchiotti, M.: Detecting controversial events from twitter. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1873–1876 (2010) · DOI
  13. Qazvinian, V., Rosengren, E., Radev, D.R., Mei, Q.: Rumor has it: identifying misinformation in microblogs. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1589–1599. Association for Computational Linguistics (2011)
  14. Unankard, S., Li, X., Sharaf, M.A.: Emerging event detection in social networks with location sensitivity. World Wide Web 18(5), 1393–1417 (2015) · DOI
  15. Zhang, Y., Szabo, C., Sheng, Q.Z., Fang, X.S.: Classifying perspectives on twitter: immediate observation, affection, and speculation. In: Proceedings of the 16th International Conference on Web Information Systems Engineering, Part I, pp. 493–507 (2015)

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Metadata

Authors
Chang, C., Zhang, Y., Szabo, C., & Sheng, Q. Z.
Affiliations
The University of Adelaide (AU)
Corresponding author
Zhang, Yihong
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2016
Pages
751-763
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-319-49586-6_54
OpenAlex
W2553895521
Cited by
47
References
0
Citations by year
2025: 1 · 2024: 6 · 2023: 5 · 2022: 7 · 2021: 3 · 2020: 6 · 2019: 15 · 2018: 3 · 2016: 1
FWCI
19.39
Type
conference paper
Language
en
Open access
closed
Licence
www.springer.com/tdm (unspecified)
Locations
  • Lecture notes in computer science (publishedVersion)
  • Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
Topics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
Keywords
Twitter, rule-based detection, unsupervised classification, political campaigns

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