link管理
链接快照平台
- 输入网页链接，自动生成快照
- 标签化管理网页链接
相关文章推荐
|
至今单身的保温杯 · News Archive | Hot ...· 1 周前 · |
|
|
爽快的春卷 · KNIME Python ...· 4 月前 · |
|
|
痴情的火龙果 · 常用PHP/JS/JQ/MYSQL代码手册 ...· 6 月前 · |
|
|
千杯不醉的电脑桌 · 国家服务业扩大开放综合示范区简介· 9 月前 · |
|
|
老实的钥匙扣 · İptv m3u8 vavoo dezor ...· 9 月前 · |
|
|
满身肌肉的蜡烛 · 王者荣耀艾琳怎么玩-王者荣耀艾琳打法教学攻略 ...· 1 年前 · |
link管理
›
A Topic-Agnostic Approach for Identifying Fake News Pages | Companion Proceedings of The 2019 World
|
虚心的排球
5 月前 |
A Topic-Agnostic Approach for Identifying Fake News Pages
Authors
:
Sonia
Castelo
,
Thais
Almeida
,
Anas
Elghafari
,
Aécio
Santos
,
+ 3
,
Kien
Pham
,
Eduardo
Nakamura
,
Juliana
Freire
(Less)
Authors Info & Claims
Sonia
Castelo
,
Thais
Almeida
,
Anas
Elghafari
,
Aécio
Santos
,
+ 3
,
Kien
Pham
,
Eduardo
Nakamura
,
Juliana
Freire
(Less)
Authors Info & Claims
Pages
975
-
980
Abstract
Fake news and misinformation have been increasingly used to manipulate popular opinion and influence political processes. To better understand fake news, how they are propagated, and how to counter their effect, it is necessary to first identify them. Recently, approaches have been proposed to automatically classify articles as fake based on their content. An important challenge for these approaches comes from the dynamic nature of news: as new political events are covered, topics and discourse constantly change and thus, a classifier trained using content from articles published at a given time is likely to become ineffective in the future. To address this challenge, we propose a topic-agnostic (TAG) classification strategy that uses linguistic and web-markup features to identify fake news pages. We report experimental results using multiple data sets which show that our approach attains high accuracy in the identification of fake news, even as topics evolve over time.
References
[1]
Hunt Allcott and Matthew Gentzkow. 2017. Social media and fake news in the 2016 election. Journal of Economic Perspectives 31 (2017), 211–36.
[2]
Vian Bakir and Andrew McStay. 2018. Fake news and the economy of emotions: Problems, causes, solutions. Digital Journalism 6(2018), 154–175.
[3]
Ramy Baly, Georgi Karadzhov, Dimitar Alexandrov, James Glass, and Preslav Nakov. 2018. Predicting Factuality of Reporting and Bias of News Media Sources. In Proc. of the Conf. on Empirical Methods in NLP. 3528–3539.
[4]
Alberto Barrón-Cedeño, Giovanni Da San Martino, Israa Jaradat, and Preslav Nakov. 2019. Proppy: A System to Unmask Propaganda in Online News. In Proc. of the 33th AAAI Conf. on Artificial Intelligence.
[5]
Steven Bird and Edward Loper. 2004. NLTK: the natural language toolkit. In Proc. of the Association for Computational Linguistics on Interactive poster and demonstration sessions. 31.
[6]
James Fairbanks, Natalie Fitch, Nathan Knauf, and Erica Briscoe. 2018. Credibility Assessment in the News: Do we need to read?. In Proc. of the MIS2 Workshop held in conjuction with 11th Int’l Conf. on Web Search and Data Mining. 799–800.
[7]
Pierre Geurts, Damien Ernst, and Louis Wehenkel. 2006. Extremely randomized trees. Machine learning 63(2006), 3–42.
[8]
Jeffrey Gottfried and Elisa Shearer. 2016. News Use across Social Media Platforms 2016. http://www.journalism.org/2016/05/26/news-use-across-social-media-platforms-2016.
[9]
Benjamin D Horne and Sibel Adali. 2017. This just in: fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news. In Proc. of the 2nd Intn’l Workshop on News and Public Opinion.
[10]
Alexander Kraskov, Harald Stögbauer, and Peter Grassberger. 2004. Estimating mutual information. Physical review E 69(2004), 066138.
[11]
David MJ Lazer, Matthew A Baum, Yochai Benkler, Adam J Berinsky, Kelly M Greenhill, Filippo Menczer, Miriam J Metzger, Brendan Nyhan, Gordon Pennycook, David Rothschild, 2018. The science of fake news. Science (2018).
[12]
Annick Lesne. 2014. Shannon Entropy: A Rigorous Notion at The Crossroads Between Probability, Information Theory, Dynamical Systems and Statistical Physics. Mathematical Structures in Computer Science 24 (2014), 240–311.
[13]
James Pennebaker, Ryan Boyd, Kayla Jordan, and Kate Blackburn. 2015. The Development and Psychometric Properties of LIWC2015.
[14]
Verónica Pérez-Rosas, Bennett Kleinberg, Alexandra Lefevre, and Rada Mihalcea. 2018. Automatic Detection of Fake News. In Proc. of Int’l Conf. on Computational Linguistics. 3391–3401.
[15]
Scott Pham. {n. d.}. Analysis of fake news sites and viral posts, 2016 vs. 2017. https://github.com/BuzzFeedNews/2017-12-fake-news-top-50.
[16]
Martin Potthast, Johannes Kiesel, Kevin Reinartz, Janek Bevendorff, and Benno Stein. 2017. A Stylometric Inquiry into Hyperpartisan and Fake News. CoRR abs/1702.05638(2017).
[17]
Hannah Rashkin, Eunsol Choi, Jin Yea Jang, Svitlana Volkova, and Yejin Choi. 2017. Truth of varying shades: Analyzing language in fake news and political fact-checking. In Proc. of the Conf. on Empirical Methods in NLP. 2931–2937.
[18]
Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. 2017. Fake News Detection on Social Media: A Data Mining Perspective. ACM SIGKDD Explorations Newsletter 19 (2017), 22–36.
[19]
Kai Shu, Suhang Wang, and Huan Liu. 2017. Exploiting Tri-Relationship for Fake News Detection. CoRR abs1712.07709(2017).
[20]
Craig Silverman. 2015. Lies, damn lies, and viral content: How news websites spread (and Debunk) online rumors, unverified claims and misinformation. Tow Center for Digital Journalism 168 (2015).
[21]
Craig Silverman and Jeremy Singer-Vine. 2016. Most Americans Who See Fake News Believe It, New Survey Says. BuzzFeed News.
[22]
Liang Wu and Huan Liu. 2018. Tracing fake-news footprints: Characterizing social media messages by how they propagate. In Proc. of the 11th Int’l Conf. on Web Search and Data Mining. 637–645.
[23]
Peng Zhao and Bin Yu. 2006. On model selection consistency of Lasso. Journal of Machine learning research 7 (2006), 2541–2563.
Cited By
View all- Alnabhan M Branco P (2024) BERTGuard: Two-Tiered Multi-Domain Fake News Detection with Class Imbalance Mitigation Big Data and Cognitive Computing 10.3390/bdcc8080093 8 :8 (93) Online publication date: 16-Aug-2024
- Reuben M Friedland L Puzis R Grinberg N Baeza-Yates R Bonchi F (2024) Leveraging Exposure Networks for Detecting Fake News Sources Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 10.1145/3637528.3671539 (5635-5646) Online publication date: 25-Aug-2024
- Wei Z Xu X Hui P Chua T Ngo C Kumar R Lauw H Ka-Wei Lee R (2024) Digital Democracy at Crossroads: A Meta-Analysis of Web and AI Influence on Global Elections Companion Proceedings of the ACM Web Conference 2024 10.1145/3589335.3652003 (1126-1129) Online publication date: 13-May-2024
- Show More Cited By
-
Combating Fake News: A Survey on Identification and Mitigation Techniques
Survey Paper, Research Commentary and Regular PapersThe proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news and mitigation of its widespread impact on public opinion. While much of the earlier research was focused on ...
-
Fake News Research: Theories, Detection Strategies, and Open Problems
KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningFake news has become a global phenomenon due its explosive growth, particularly on social media. The goal of this tutorial is to (1) clearly introduce the concept and characteristics of fake news and how it can be formally differentiated from other ...
-
Tracing Fake-News Footprints: Characterizing Social Media Messages by How They Propagate
WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data MiningWhen a message, such as a piece of news, spreads in social networks, how can we classify it into categories of interests, such as genuine or fake news? Classification of social media content is a fundamental task for social media mining, and most ...
WWW '19: Companion Proceedings of The 2019 World Wide Web Conference
May 2019
1331 pages
ISBN:
9781450366755
DOI:
10.1145/3308560
- Editors:
-
Ling Liu
Georgia Tech, USA
, -
Ryen White
Microsoft Research, USA
-
View Citations57Total Citations
-
889Total Downloads
- Downloads (Last 12 months) 106
- Downloads (Last 6 weeks) 13
Reflects downloads up to 16 Oct 2024
Other Metrics
Citations
Cited By
View all- Alnabhan M Branco P (2024) BERTGuard: Two-Tiered Multi-Domain Fake News Detection with Class Imbalance Mitigation Big Data and Cognitive Computing 10.3390/bdcc8080093 8 :8 (93) Online publication date: 16-Aug-2024
- Reuben M Friedland L Puzis R Grinberg N Baeza-Yates R Bonchi F (2024) Leveraging Exposure Networks for Detecting Fake News Sources Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 10.1145/3637528.3671539 (5635-5646) Online publication date: 25-Aug-2024
- Wei Z Xu X Hui P Chua T Ngo C Kumar R Lauw H Ka-Wei Lee R (2024) Digital Democracy at Crossroads: A Meta-Analysis of Web and AI Influence on Global Elections Companion Proceedings of the ACM Web Conference 2024 10.1145/3589335.3652003 (1126-1129) Online publication date: 13-May-2024
- Li H Jiang Y Li X Wang C Chen Y Li H (2024) Perspective Collaboration for Multi-Domain Fake News Detection International Journal of Pattern Recognition and Artificial Intelligence 10.1142/S0218001424500034 38 :03 Online publication date: 27-Mar-2024
- Wu L Yu D Liu P Gao C Wang Z (2024) Heuristic Heterogeneous Graph Reasoning Networks for Fact Verification IEEE Transactions on Neural Networks and Learning Systems 10.1109/TNNLS.2023.3282380 35 :10 (14959-14973) Online publication date: Oct-2024
- Ren Q Zhou B Yan D Guo W (2024) Fake News Classification Using Tensor Decomposition and Graph Convolutional Network IEEE Transactions on Computational Social Systems 10.1109/TCSS.2023.3270331 11 :2 (2190-2200) Online publication date: Apr-2024
- Liu D Liu B Chen Y Wu W Cao J Hou Y (2024) Cross-Domain Rumor Detection Based on Dual-Modal Domain Alignment 2024 9th International Conference on Signal and Image Processing (ICSIP) 10.1109/ICSIP61881.2024.10671563 (544-548) Online publication date: 12-Jul-2024
- Giri M Eswaran S Honnavalli P D D (2024) Automated and Interpretable Fake News Detection With Explainable Artificial Intelligence Journal of Applied Security Research 10.1080/19361610.2024.2356431 (1-21) Online publication date: 22-May-2024
- Matthee M Hattingh M Normund-van Wyk L (2024) The use of heat maps to explore perceived visual indicators of online fake news Procedia Computer Science 10.1016/j.procs.2024.06.346 239 (1687-1695) Online publication date: 2024
- Wu D Tan Z Zhao H Jiang T Geng N (2024) Domain- and category-style clustering for general fake news detection via contrastive learning Information Processing & Management 10.1016/j.ipm.2024.103725 61 :4 (103725) Online publication date: Jul-2024
- Show More Cited By
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader .
eReaderHTML Format
View this article in HTML Format.
HTML FormatMedia
Figures
Other
Tables
Share
Share
Affiliations
Sonia
Castelo
New York Univ.
Thais
Almeida
Federal Univ. of Amazonas
Anas
Elghafari
New York Univ.
Aécio
Santos
New York Univ.
Kien
Pham
New York Univ.
Eduardo
Nakamura
Federal Univ. of Amazonas
Juliana
Freire
New York Univ.
Export Citations
Your Search Results Download Request
We are preparing your search results for download ...
We will inform you here when the file is ready.
Download now!
Your Search Results Download Request
Your file of search results citations is now ready.
Download now!
Your Search Results Download Request
Your search export query has expired. Please try again.
推荐文章
|
|
千杯不醉的电脑桌 · 国家服务业扩大开放综合示范区简介 9 月前 |