Detecting breaking news rumors of emerging topics in social media

Alkhodair, Sarah A.; Ding, Steven H. H.; Fung, Benjamin C. M.; Liu, Junqiang

doi:10.1016/j.ipm.2019.02.016

Cited by 173 publications

(83 citation statements)

References 3 publications

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“…Recurrent Neural Network (RNN) RNN utilizes the sequential information in the network which is essential in many applications where the embedded structure in the data sequence conveys useful knowledge (Alkhodair et al. 2020 ). The advantage of RNN is its ability to better capture contextual information.…”

Section: Deep Learning For Misinformation Detection: State-of-the-artmentioning

confidence: 99%

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Islam

Liu

Wang

et al. 2020

Soc. Netw. Anal. Min.

196

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Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation. Besides, spreading the massive amount of misinformation in social networks has become a global risk. Therefore, misinformation detection (MID) in social networks has gained a great deal of attention and is considered an emerging area of research interest. We find that several studies related to MID have been studied to new research problems and techniques. While important, however, the automated detection of misinformation is difficult to accomplish as it requires the advanced model to understand how related or unrelated the reported information is when compared to real information. The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection. Therefore, related to the previous issues, we present a comprehensive survey of automated misinformation detection on (i) false information, (ii) rumors, (iii) spam, (iv) fake news, and (v) disinformation. We provide a state-of-the-art review on MID where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve better results. We further show that DL is an effective and scalable technique for the state-of-the-art MID. Finally, we suggest several open issues that currently limit real-world implementation and point to future directions along this dimension.

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Section: Deep Learning For Misinformation Detection: State-of-the-artmentioning

confidence: 99%

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Islam

Liu

Wang

et al. 2020

Soc. Netw. Anal. Min.

196

View full text Add to dashboard Cite

show abstract

“…Future research could address this limitation. Finally, we could also examine the effect of detecting topical shifts on content automatically (Alkhodair, Ding, Fung, & Liu, ) for integration into an overall data‐driven persona system. This could assist content companies in avoiding content becoming stale or having audience segments become bored with the content that is too similar to what they have consumed in the past.…”

Section: Discussionmentioning

confidence: 99%

Capturing the change in topical interests of personas over time

Jansen

Jung

Salminen

2019

Proceedings of the Association for Information Science and Tech

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In this research, we collect monthly content consumption and demographic data from YouTube over two years for a large media publisher. We use automation to generate 15 personas each month and examine the consistency of the generated personas over time. We find that there are 35 unique personas in total for the entire period, reflecting the changes in the underlying audience population. For each persona, we generate topics of interest and identify the top three monthly topics for each of the 35 personas following an identical algorithmic approach each month. We then compare the sets of topical interests of the personas month‐over‐month for the entire two‐year period. Findings show that there is an average 20.2% change in topical interests and that 68% of the personas experience more topical change than topical consistency. Findings suggest that the topical interests of online audiences are fluid and changes in the underlying audience data can occur within a relatively short period, resulting in the need for constant updating of personas using data‐driven methods. The implications for organizations seeking to understand their online audience are that they should employ routine data analysis to detect changes in the audience interests and investigate ways to automate their persona generation processes.

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“…Never before in human history has the role of globalization processes had the impact that it currently has in decision-making processes and societies because of the speed of communication [5]. Globalization also plays a crucial role in the spread of health news, including social media, influencing the way users receive such news [16,17]. In this arena, it is important to highlight that in a globalized world, health content information can be perceived differently depending on the target group or context [18].…”

Section: The Context In Which Fake News Is Spreadmentioning

confidence: 99%

A New Application of Social Impact in Social Media for Overcoming Fake News in Health

Pulido

Ruiz-Eugenio

Redondo-Sama

et al. 2020

IJERPH

177

111

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One of the challenges today is to face fake news (false information) in health due to its potential impact on people's lives. This article contributes to a new application of social impact in social media (SISM) methodology. This study focuses on the social impact of the research to identify what type of health information is false and what type of information is evidence of the social impact shared in social media. The analysis of social media includes Reddit, Facebook, and Twitter. This analysis contributes to identifying how interactions in these forms of social media depend on the type of information shared. The results indicate that messages focused on fake health information are mostly aggressive, those based on evidence of social impact are respectful and transformative, and finally, deliberation contexts promoted in social media overcome false information about health. These results contribute to advancing knowledge in overcoming fake health-related news shared in social media.

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Detecting breaking news rumors of emerging topics in social media

Cited by 173 publications

References 3 publications

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Capturing the change in topical interests of personas over time

A New Application of Social Impact in Social Media for Overcoming Fake News in Health

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