Credibility Estimation of Stock Comments Based on Publisher and Information Uncertainty Evaluation

Qiu, Qiaoyun; Xu, Ruifeng; Liu, Bin; Gui, Lin; Zhou, Yu

doi:10.1007/978-3-662-45652-1_40

Cited by 3 publications

(2 citation statements)

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“…We can see that some existing works consider calculating and evaluating credibility based on a given topic [41], [46], [53], [54], [60], [70], [88], [93], [95], [121], [140]- [142]. They overlook calculating credibility for a user based on his/her reliability of information/content as well as inter-entity relationships [93].…”

Section: Discussionmentioning

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%

“…This study emphasized modeling tweets to find relevant information. Qiu et al [121] established that tweets prefixed with ''@'' (indicating direct replies to other users) were less frequent during such events. Also established here is that the number of tweets including a URL tends to decrease in crises.…”

Section: ) Cognitive and Perception Approachesmentioning

confidence: 99%

Credibility in Online Social Networks: A Survey

et al. 2019

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Deep learning-based credibility conversation detection approaches from social network

Fadhli

Hlaoua

Omri

2023

Soc. Netw. Anal. Min.

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In recent years, the social networks that have become most exploited sources of information, such as Facebook, Instagram, LinkedIn, and Twitter, have been considered the main sources of non-credible information. False information on these social networks has a negative impact on the credibility of conversations. In this article, we propose a new deep learning-based credibility conversation detection approach in social network environments, called CreCDA. CreCDA is based on: (i) the combination of post and user features in order to detect credible and non-credible conversations; (ii) the integration of multi-dense layers to represent features more deeply and to improve the results; (iii) sentiment calculation based on the aggregation of tweets. In order to study the performance of our approach, we have used the standard PHEME dataset. We compared our approach with the main approaches we have studied in the literature. The results of this evaluation show the effectiveness of sentiment analysis and the combination of text and user levels to analyze conversation credibility. We recorded the mean precision of credible and non-credible conversations at 79%, the mean recall at 79%, the mean F1-score at 79%, the mean accuracy at 81%, and the mean G-Mean at 79%.

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