Content Credibility Check on Twitter

Gupta, Priya; Pathak, Vihaan; Goyal, Naman; Singh, Jaskirat; Varshney, V. P.; Kumar, Sunil

doi:10.1007/978-981-13-2035-4_18

Cited by 2 publications

(2 citation statements)

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“…In NLP, it is a well-informed and popular approach. It allows for detection in favor of the audience, and from the text, it determines whether the audience found the objective to be against, in favor of, or impartial to the target [41]. The objective could be an individual, an association, an administration strategy, a development, an item, and so forth.…”

Section: Proposed Approach: Inference Enginementioning

confidence: 99%

“…Priya Gupta et al in [41] described different features of evaluating the believability of client-produced content on Twitter, and a novel continuous framework to survey the trustworthiness of tweets was proposed. The discussed framework was implemented to accomplish this by relegating a score or rating to content on Twitter to show its dependability.…”

Section: Proposed Approach: Inference Enginementioning

confidence: 99%

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Ternion: An Autonomous Model for Fake News Detection

et al. 2021

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In recent years, the consumption of social media content to keep up with global news and to verify its authenticity has become a considerable challenge. Social media enables us to easily access news anywhere, anytime, but it also gives rise to the spread of fake news, thereby delivering false information. This also has a negative impact on society. Therefore, it is necessary to determine whether or not news spreading over social media is real. This will allow for confusion among social media users to be avoided, and it is important in ensuring positive social development. This paper proposes a novel solution by detecting the authenticity of news through natural language processing techniques. Specifically, this paper proposes a novel scheme comprising three steps, namely, stance detection, author credibility verification, and machine learning-based classification, to verify the authenticity of news. In the last stage of the proposed pipeline, several machine learning techniques are applied, such as decision trees, random forest, logistic regression, and support vector machine (SVM) algorithms. For this study, the fake news dataset was taken from Kaggle. The experimental results show an accuracy of 93.15%, precision of 92.65%, recall of 95.71%, and F1-score of 94.15% for the support vector machine algorithm. The SVM is better than the second best classifier, i.e., logistic regression, by 6.82%.

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Section: Proposed Approach: Inference Enginementioning

confidence: 99%

Section: Proposed Approach: Inference Enginementioning

confidence: 99%