Measuring the credibility of Arabic text content in Twitter

Al-Eidan, Rasha M. BinSultan; Al‐Khalifa, Hend S.; Al-Salman, AbdulMalik S.

doi:10.1109/icdim.2010.5664223

Cited by 25 publications

(11 citation statements)

References 17 publications

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“…Therefore, we still haven't seen a decisive work that could establish a systematic credibility metric in OSNs to detect and evaluate misinformation that integrates reliability and reputation, though most of the research provides a good foundation for future efforts. In principle, future work should include extending experiments to larger datasets and partial datasets; some studies used small datasets [29], [57], [59], [72], [81], [133], [143]. Other open problems include assessing the credibility of target pages to which URLs redirect.…”

Section: Discussionmentioning

confidence: 99%

“…At the post level, the task is to analyze the content attributes of a tweet to assess its credibility score and determine whether it is trustworthy [51], [52]. Research on this topic is divided into offline systematization of already present input [29], [53]- [57] and real-time systems that use only the data accessible in each post (not considering complete historical, user, or topic data) [58]- [61]. Starting from tweet attributes, characteristics like total # if characters in the tweet, total # of words it contains, total # of questions, and total # of uppercase characters are computed.…”

Section: A Post Levelmentioning

confidence: 99%

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Credibility in Online Social Networks: A Survey

et al. 2019

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The importance of information credibility in society cannot be underestimated given that it is at the heart of all decision-making. Generally, more information is better; however, knowing the value of this information is essential for the decision-making processes. Information credibility defines a measure of the fitness of the information for consumption. It can also be defined in terms of reliability, which denotes the probability that a data source will appear credible to the users. A challenge in this topic is that there is a great deal of literature that has developed different credibility dimensions. In addition, information science dealing with online social networks has grown in complexity, attracting interest from researchers in information science, psychology, human-computer interaction, communication studies, and management studies, all of whom have studied the topic from different perspectives. This work will attempt to provide an overall review of the credibility assessment literature over the period 2006-2017 as applied to the context of the microblogging platform, Twitter. The known interpretations of credibility will be examined, particularly as they relate to the Twitter environment. In addition, we investigate levels of credibility assessment features. We then discuss recent works, addressing a new taxonomy of credibility analysis and assessment techniques. At last, a cross-referencing of literature is performed while suggesting new topics for future studies of credibility assessment in a social media context.

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Section: Discussionmentioning

confidence: 99%

Section: A Post Levelmentioning

confidence: 99%

Credibility in Online Social Networks: A Survey

et al. 2019

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“…The present assessment focuses on filtering Arabic content on Twitter, whereas most research on this topic has focused on the English language, which uses the Roman alphabet. Al-Eidan et al [17] provided two methods with the aim of evaluating Arabic content; the first of these proposed systems applies "the similarity between Twitter posts and authentic news sources, while the second approach uses a set of proposed features" [17]. These techniques sound similar to the approach proposed by Sriram et al [14] with classes.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Rogue Content and Arabic Spammers on Twitter

Alharbi

Aljaedi

2019

Future Internet

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Twitter is one of the most popular online social networks for spreading propaganda and words in the Arab region. Spammers are now creating rogue accounts to distribute adult content through Arabic tweets that Arabic norms and cultures prohibit. Arab governments are facing a huge challenge in the detection of these accounts. Researchers have extensively studied English spam on online social networks, while to date, social network spam in other languages has been completely ignored. In our previous study, we estimated that rogue and spam content accounted for approximately three quarters of all content with Arabic trending hashtags in Saudi Arabia. This alarming rate, supported by autonomous concurrent estimates, highlights the urgent need to develop adaptive spam detection methods. In this work, we collected a pure data set from spam accounts producing Arabic tweets. We applied lightweight feature engineering based on rogue content and user profiles. The 47 generated features were analyzed, and the best features were selected. Our performance results show that the random forest classification algorithm with 16 features performs best, with accuracy rates greater than 90%.

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“…In total, we gather features from 17 different papers, some of which we have already discussed. We gather features from works that use classifiers to automatically predict credibility [8,50,21,28,49,9,16,15,7], features from works that use learning to rank algorithms [18,17], and features gleaned from works that take hybrid or other appraoches to quantify and model credibility [40,24,45,1,42,3]. In Table 2.…”

Section: Popular Features From Previous Workmentioning

confidence: 99%

Features for Ranking Tweets Based on Credibility and Newsworthiness

Ross

Thirunarayan

2016

2016 International Conference on Collaboration Technologies and Systems (CTS)

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We create a robust and general feature set for learning to rank algorithms that rank tweets based on credibility and newsworthiness. In previous works, it has been demonstrated that when the training and testing data are from two distinct time periods, the ranker performs poorly. We improve upon previous work by creating a feature set that does not over fit a particular year or set of topics. This is critical given how people utilize social media changes as time progresses, and the topics discussed vary. In addition, we are constantly gaining new tweet data. Thus, it is important to be able to have a set of features that can perform well across many different topics, and across different years. In our approach, we present a methodology for selecting features based on how they can capture credibility and newsworthiness regardless of year and topic. In order to derive such features, we use the studies done on credibility perception of social media as well as the clues provided in past works in this domain. We also present new features that, to our knowledge, have not been used in previous works in this domain.iii

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Measuring the credibility of Arabic text content in Twitter

Cited by 25 publications

References 17 publications

Credibility in Online Social Networks: A Survey

Credibility in Online Social Networks: A Survey

Predicting Rogue Content and Arabic Spammers on Twitter

Features for Ranking Tweets Based on Credibility and Newsworthiness

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