Finding a line between trusted and untrusted information on tweets through sequence classification

Pritzkau, Albert; Winandy, Steffen; Krumbiegel, Theresa

doi:10.1109/icmcis52405.2021.9486423

“…However, certain features were good candidates for differentiating trusted and untrusted users such as high R hind (u i ), L hind (u i ), Inf (u i ), Sen s (u i ), T wt cr (u i ), R s (u i ). In Table 3, the features marked with * were used for classification in the existing literature [3], [58], [61] while the features marked with ∩ were based on the correlation among the features. The impact of the individual feature is shown in Figure 3.…”

Section: Active Learning and ML Modelsmentioning

confidence: 99%

Trust and Believe – Should We? Evaluating the Trustworthiness of Twitter Users

Khan

2021

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Social networking and micro-blogging services, such as Twitter, play an important role in sharing digital information. Despite the popularity and usefulness of social media, there have been many instances where corrupted users found ways to abuse it, as for instance, through raising or lowering user's credibility. As a result, while social media facilitates an unprecedented ease of access to information, it also introduces a new challenge -that of ascertaining the credibility of shared information. Currently, there is no automated way of determining which news or users are credible and which are not. Hence, establishing a system that can measure the social media user's credibility has become an issue of great importance. Assigning a credibility score to a user has piqued the interest of not only the research community but also most of the big players on both sides -such as Facebook, on the side of industry, and political parties on the societal one. In this work, we created a model which, we hope, will ultimately facilitate and support the increase of trust in the social network communities. Our model collected data and analysed the behaviour of 50,000 politicians on Twitter. Influence score, based on several chosen features, was assigned to each evaluated user. Further, we classified the political Twitter users as either trusted or untrusted using random forest, multilayer perceptron, and support vector machine. An active learning model was used to classify any unlabelled ambiguous records from our dataset. Finally, to measure the performance of the proposed model, we used precision, recall, F1 score, and accuracy as the main evaluation metrics.

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“…There are various ways for X user classification such as using Machine Learning (ML) [6], and Natural Language Processing (NLP) [7]. Among these, ML models have been widely used in various research to classify X users into different categories based on their profiles, activity, and content of the tweets.…”

Section: Introductionmentioning

confidence: 99%

Seeing and Believing: Evaluating the Trustworthiness of Twitter Users

Khan

¹

,

Michalas

²

2021

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Social networking and micro-blogging services, such as Twitter, play an important role in sharing digital information. Despite the popularity and usefulness of social media, there have been many instances where corrupted users found ways to abuse it, as for instance, through raising or lowering user's credibility. As a result, while social media facilitates an unprecedented ease of access to information, it also introduces a new challenge -that of ascertaining the credibility of shared information. Currently, there is no automated way of determining which news or users are credible and which are not. Hence, establishing a system that can measure the social media user's credibility has become an issue of great importance. Assigning a credibility score to a user has piqued the interest of not only the research community but also most of the big players on both sides -such as Facebook, on the side of industry, and political parties on the societal one. In this work, we created a model which, we hope, will ultimately facilitate and support the increase of trust in the social network communities. Our model collected data and analysed the behaviour of 50,000 politicians on Twitter. Influence score, based on several chosen features, was assigned to each evaluated user. Further, we classified the political Twitter users as either trusted or untrusted using random forest, multilayer perceptron, and support vector machine. An active learning model was used to classify any unlabelled ambiguous records from our dataset. Finally, to measure the performance of the proposed model, we used precision, recall, F1 score, and accuracy as the main evaluation metrics.

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