An experimental system for measuring the credibility of news content in Twitter

被引:60
|
作者
Al-Khalifa, Hend S. [1 ]
Al-Eidan, Rasha M. [2 ]
机构
[1] King Saud Univ, Dept Informat Technol, Coll Comp & Informat Sci, Riyadh, Saudi Arabia
[2] King Saud Univ, Dept Comp Sci, Comp Sci, Riyadh, Saudi Arabia
关键词
Blogs; Information media; Communication; Trust; Twitter; Credibility; Web content; Natural language processing; Arabic language;
D O I
10.1108/17440081111141772
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Purpose - Owing to the large amount of information available on Twitter (a micro-blogging service) that is not necessarily true or believable, credibility of news published in such an electronic channel has become an important area for investigation in the field of web credibility. This paper aims to address this issue. Design/methodology/approach - A system was developed to measure the credibility of news content published in Twitter. The system uses two approaches to assign credibility levels (low, high and average) to each tweet. The first approach is based on the similarity between Twitter posts (tweets) and authentic (i. e. verified) news sources. The second approach is based on the similarity with verified news sources in addition to a set of proposed features. Findings - The evaluations of the two approaches showed that assigning credibility levels to Twitter tweets for the first approach has a higher precision and recall. Additional experiments showed that the linking feature has its impact on the second approach results. Research limitations/implications - The proposed system is experimental; thus further experiments are needed to prove these findings. Originality/value - This paper contributes to the research on web credibility. It is believed to be the first which provides a proposed system to evaluate the credibility of Twitter news content automatically.
引用
收藏
页码:130 / +
页数:23
相关论文
共 50 条
  • [1] Measuring opinion credibility in twitter
    Thandar, Mya
    Usanavasin, Sasiporn
    [J]. Advances in Intelligent Systems and Computing, 2015, 361 : 205 - 214
  • [2] Content Credibility Check on Twitter
    Gupta, Priya
    Pathak, Vihaan
    Goyal, Naman
    Singh, Jaskirat
    Varshney, Vibhu
    Kumar, Sunil
    [J]. APPLICATIONS OF COMPUTING AND COMMUNICATION TECHNOLOGIES, ICACCT 2018, 2018, 899 : 197 - 212
  • [3] On the credibility perception of news on Twitter: Readers, topics and features
    Shariff, Shafiza Mohd
    Zhang, Xiuzhen
    Sanderson, Mark
    [J]. COMPUTERS IN HUMAN BEHAVIOR, 2017, 75 : 785 - 796
  • [4] Establishing News Credibility using Sentiment Analysis on Twitter
    Sharf, Zareen
    Jalil, Zakia
    Amir, Wajiha
    Siddiqui, Nudrat
    [J]. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2019, 10 (09) : 209 - 221
  • [5] Establishing news credibility using sentiment analysis on twitter
    Sharf, Zareen
    Jalil, Zakia
    Amir, Wajiha
    Siddiqui, Nudrat
    [J]. International Journal of Advanced Computer Science and Applications, 2019, 10 (09): : 209 - 221
  • [6] FeedReflect: A Tool for Nudging Users to Assess News Credibility on Twitter
    Bhuiyan, Md Momen
    Zhang, Kexin
    Vick, Kelsey
    Horning, Michael A.
    Mitra, Tanushree
    [J]. COMPANION OF THE 2018 ACM CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING (CSCW'18), 2018, : 205 - 208
  • [7] Credibility Analysis of News on Twitter using LSTM: An exploratory study
    Vyas, Piyush
    El-Gayar, Omar
    [J]. AMCIS 2020 PROCEEDINGS, 2020,
  • [8] A MACROSCOPIC ANALYSIS OF NEWS CONTENT IN TWITTER
    Malik, Momin M.
    Pfeffer, Jurgen
    [J]. DIGITAL JOURNALISM, 2016, 4 (08) : 955 - 979
  • [9] A Real-Time System for Credibility on Twitter
    Iftene, Adrian
    Gifu, Daniela
    Miron, Andrei-Remus
    Dudu, Mihai-Stefan
    [J]. PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2020), 2020, : 6166 - 6173
  • [10] In Twitter we trust(ed): How perceptions of Twitter's helpfulness influence news post credibility perceptions and news engagement
    Millet, Barbara
    Tang, Jiajing
    Seelig, Michelle
    Petit, John
    Sun, Ruoyu
    [J]. COMPUTERS IN HUMAN BEHAVIOR, 2024, 155