An Empirical Evaluation of Name Semantic Network for Face Annotation

Kasthuri, A.; Suruliandi, A.; Poongothai, E.

doi:10.2174/2213275912666190204141902

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…Few studies in the state-of-the-art have employed feature-based techniques for annotation in interdisciplinary domains [6,30]. However, the majority of studies for this domain (i.e., fake news) have done manual data annotation through human annotators with domain expertise.…”

Section: Popularitymentioning

confidence: 99%

Disinformation detection on social media: An integrated approach

Rastogi

Bansal

2022

Multimed Tools Appl

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The emergence of social media platforms has amplified the dissemination of false information in various forms. Social media gives rise to virtual societies by providing freedom of expression to users in a democracy. Due to the presence of echo chambers on social media, social science studies play a vital role in the spread of false news. To this aim, we provide a comprehensive framework that is adapted from several scholarly studies. The framework is capable of detecting information into various types, namely real, disinformation and satire based on authenticity as well as intention. The process highlights the use of interdisciplinary approaches derived from fundamental theories of social science and integrating them with modern computational tools and techniques. Few of these theories claim that malicious users suggest writing fabricated content in a different style to attract the audience. Stylebased methods evaluate the intention i.e., the content is written with an intent to mislead the audience or not. However, the writing style can be deceptive. Thus, it is important to involve user-oriented social information to improve model strength. Therefore, the paper used an integrated approach by combining style based and propagation-based features with a total of thirty-one features. The extracted features are divided into ten categories: relative frequency, quantity, complexity, uncertainty, sentiment, subjectivity, diversity, informality, additional, and popularity. The features have been iteratively utilized by supervised classifiers and then selected the best-correlated ones using the ANOVA test. Our experimental results have shown that the selected features are able to distinguish real from disinformation and satirical news. It has been observed that the Ensemble machine learning model outperformed other models over the developed multi-labelled corpus.

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Section: Popularitymentioning

confidence: 99%