Towards Emotional-Aware Truth Discovery in Social Sensing Applications

Marshall, Jermaine; Wang, Dong

doi:10.1109/smartcomp.2016.7501723

Cited by 12 publications

(5 citation statements)

References 35 publications

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“…1) Our proposed model better reflects the opinion dissemination in the presence of fake news than previous models [13]- [17]. It can represent the communication of opinions as well as the update of the reliability of and doubt about friends.…”

Section: Introductionmentioning

confidence: 92%

“…Furthermore, one work improved the accuracy of the model presented by [15] by incorporating the speed of sharing opinions in the expectation maximization algorithm (e.g., a friend shares an opinion immediately or late) [16]. Another work improved the accuracy of the model presented by [15] by incorporating an emotional-aware situation in the expectation maximization algorithm (e.g., a friend shares an emotional opinion that lacks grounds) [17].…”

Section: Identifying Truth Among From Conflicting Informationmentioning

confidence: 99%

“…In this section, we propose an opinion dissemination model considering the presence of fake news, which overcomes the limitations of previous models [13]- [17] while preserving their strengths.…”

Section: Opinion Dissemination Model For Fake Newsmentioning

confidence: 99%

“…On the other hand, previous work on "truth discovery" developed methods, in which a person updates the reliability of and doubt about his or her friends on the basis of opinions received from them [15]- [17]. However, these methods do not consider social interaction (i.e., these models only consider the situations that several persons send opinions independently to only one person, and that they do not exchange their opinions with each other).…”

Section: A Requirements For Opinion Dissemination Modelmentioning

confidence: 99%

“…To address the limitations with previous models [13]- [17], we develop an opinion dissemination model in the presence of fake news that satisfies the following requirements.…”

Section: A Requirements For Opinion Dissemination Modelmentioning

confidence: 99%

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A Fake News Dissemination Model Based on Updating Reliability and Doubt among Individuals

Yoshikawa

Awa

Kusano

et al. 2020

2020 11th International Conference on Awareness Science and Technology (iCAST)

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As social media has become more widely used, fake news has become an increasingly serious problem. The representative countermeasures against fake news are fake news detection and automated fact-checking. However, these countermeasures are not sufficient because people using social media tend to ignore facts that contradict their current beliefs. Therefore, developing effective countermeasures requires understanding the nature of fake news dissemination. Previous models related to this aim have been proposed for describing and analyzing opinion dissemination among people. However, these models are not adequate because they are based on the assumptions that ignore the presence of fake. That is, they assume that people believe their friends equally without doubting and that reliability among people does not change. In this paper, we propose a model that can better describe the opinion dissemination in the presence of fake news. In our model, each person updates the reliability of and doubt about his or her friends and exchanges opinions among each other. Applying the proposed model to artificial and real-world social networks, we found three clues to analyze the nature of fake news dissemination: 1) people can less accurately perceive that fake news is fake than they can perceive that real news is real. 2) it takes much more time for people to perceive fake news to be fake than to perceive real news to be real. 3) the results of findings 1 and 2 concerning fake news are because people become skeptical about friends in the presence of fake news and therefore people do not update opinions much.

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Section: Introductionmentioning

confidence: 92%

Section: Identifying Truth Among From Conflicting Informationmentioning

confidence: 99%

Section: Opinion Dissemination Model For Fake Newsmentioning

confidence: 99%

Section: A Requirements For Opinion Dissemination Modelmentioning

confidence: 99%

“…To address the limitations with previous models [13]- [17], we develop an opinion dissemination model in the presence of fake news that satisfies the following requirements.…”

Section: A Requirements For Opinion Dissemination Modelmentioning

confidence: 99%

See 3 more Smart Citations

A Fake News Dissemination Model Based on Updating Reliability and Doubt among Individuals

Yoshikawa

Awa

Kusano

et al. 2020

2020 11th International Conference on Awareness Science and Technology (iCAST)

View full text Add to dashboard Cite

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Detecting False Messages in the Smartphone Fault Reporting System

Rajoo

Magalingam

Samy

et al. 2019

Advances in Intelligent Systems and Computing

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The emergence of the Internet of Things (IoT) in Smart City allows mobile application developers to develop reporting services with an aim for local citizens to interact with municipalities regarding city issues in an efficient manner. However, the credibility of the messages sent rise as a great challenge when users intentionally send false reports through the application. In this research, an evidence detection framework is developed and divided into three parts that are a data source, IoT device's false text classification engine and output. Text-oriented digital evidence from an IoT mobile reporting service is analyzed to identify suitable text classifier and to build this framework. The Agile model that consists of define, design, build and test is used for the development of the false text classification engine. Focus given on text-based data that does not include encrypted messages. Our proposed framework able to achieve 97% of accuracy and showed the

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