Mining User Opinion Influences on Twitter Social Network: Find that Friend who Leads your Opinion Using Bayesian Method and a New Emotional PageRank Algorithm

Ngaffo, Armielle Noulapeu; Ayeb, Walid El; Choukair, Zièd

doi:10.1109/iwcmc.2019.8766571

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…In this study, PageRank and HITS social network analysis methods are used. And various studies such as PageRank algorithm [18], website search algorithm [19][20][21], search in social networks [22], finding influential people in social networks [23][24][25][26] are also used. Similarly, the HITS (Hyperlink-Induced Topic Search) algorithm [27], the website search algorithm [28] were used in various studies for search social networks [29,30] and to find influential people on social networks [31,32].…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Turkish Fake News in Twitter with Machine Learning Algorithms

2021

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Social media has affected people's information sources. Since most of the news on social media is not verified by a central authority, it may contain fake news for various reasons such as advertising and propaganda. Considering an average of 500 million tweets were posted daily on Twitter alone in the year of 2020, it is possible to control each share only with smart systems. In this study, we use Natural Language Processing methods to detect fake news for Turkish-language posts on certain topics on Twitter. Furthermore, we examine the follow/follower relations of the users who shared fake-real news on the same subjects through social network analysis methods and visualization tools. Various supervised and unsupervised learning algorithms have been tested with different parameters. The most successful F1 score of fake news detection was obtained with the support vector machines algorithm with 0.9. People who share fake/true news can help in the separation of subgroups in the social network created by people and their followers. The results show that fake news propagation networks may show different characteristics in their own subject based on the follow/follower network.

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Section: Literature Reviewmentioning

confidence: 99%

Detection of Turkish Fake News in Twitter with Machine Learning Algorithms

2021

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Explaining sentiment analysis results on social media texts through visualization

Jain

Kumar

Nayyar

et al. 2023

Multimed Tools Appl

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Today, Artificial Intelligence is achieving prodigious real-time performance, thanks to growing computational data and power capacities. However, there is little knowledge about what system results convey; thus, they are at risk of being susceptible to bias, and with the roots of Artificial Intelligence (“AI”) in almost every territory, even a minuscule bias can result in excessive damage. Efforts towards making AI interpretable have been made to address fairness, accountability, and transparency concerns. This paper proposes two unique methods to understand the system’s decisions aided by visualizing the results. For this study, interpretability has been implemented on Natural Language Processing-based sentiment analysis using data from various social media sites like Twitter, Facebook, and Reddit. With Valence Aware Dictionary for Sentiment Reasoning (“VADER”), heatmaps are generated, which account for visual justification of the result, increasing comprehensibility. Furthermore, Locally Interpretable Model-Agnostic Explanations (“LIME”) have been used to provide in-depth insight into the predictions. It has been found experimentally that the proposed system can surpass several contemporary systems designed to attempt interpretability.

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