Automated Credibility Assessment on Twitter

Lorek, Krzysztof; Suehiro-Wiciński, Jacek; Jankowski-Lorek, Michał; Gupta, Amit

doi:10.7494/csci.2015.16.2.157

Cited by 37 publications

(2 citation statements)

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“…The automated credibility evaluation of information on social media is a crucial task [33,48,49] where content is generated and propagated at an unusual rate. It seems impossible for human operators to detect and prevent misleading content from being propagated timely related to globally significant and fast changing events.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing data quality to mine credible patterns

Imran

Awada

2021

Journal of Information Science

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The importance of big data is widely accepted in various fields. Organisations spend a lot of money to collect, process and mine the data to identify patterns. These patterns facilitate their future decision-making process to improve the organisational performance and profitability. However, among discovered patterns, there are some meaningless and misleading patterns which restrict the effectiveness of decision-making process. The presence of data discrepancies, noise and outliers also impacts the quality of discovered patterns and leads towards missing strategic goals and objectives. Quality inception of these discovered patterns is vital before utilising them in making predictions, decision-making process or strategic planning. Mining useful and credible patterns over social media is a challenging task. Often, people spread targeted content for character assassination or defamation of brands. Recently, some studies have evaluated the credibility of information over social media based on users’ surveys, experts’ judgement and manually annotating Twitter tweets to predict credibility. Unfortunately, due to the large volume and exponential growth of data, these surveys and annotation-based information credibility techniques are not efficiently applicable. This article presents a data quality and credibility evaluation framework to determine the quality of individual data instances. This framework provides a way to discover useful and credible patterns using credibility indicators. Moreover, a new Twitter bot detection algorithm is proposed to classify tweets generated by Twitter bots and real users. The results of conducted experiments showed that the proposed model generates a positive impact on improving classification accuracy and quality of discovered patterns.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing data quality to mine credible patterns

Imran

Awada

2021

Journal of Information Science

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“…The fifth step was to develop a machine learning classification and clustering model to categorize the tweets into given credibility classes. The multilayer perceptron (MLP), support vector machine (SVM), naive Bayes (NB), and random forest (RF) classification models were applied for classifying tweets into its respective credibility class [1], [3]- [5], [7]- [14], [21], [23], [26]. Whereas K-Mean algorithm was used for finding the clusters of identical tweets based on the features set.…”

Section: Introductionmentioning

confidence: 99%

Identification of user’s credibility on twitter social networks

Ahmad

Rizvi

2021

IJEECS

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<p>Twitter is one of the most influential social media platforms, facilitates the spreading of information in the form of text, images, and videos. However, the credibility of posted content is still trailed by an interrogation mark. Introduction: In this paper, a model has been developed for finding the user’s credibility based on the tweets which they had posted on Twitter social networks. The model consists of machine learning algorithms that assist not only in categorizing the tweets into credibility classes but also helps in finding user’s credibility ratings on the social media platform. Methods and results: The dataset and associated features of 100,000 tweets were extracted and pre-processed. Furthermore, the credibility class labelling of tweets was performed using four different human annotators. The meaning cloud and natural language understanding platforms were used for calculating the polarity, sentiment, and emotions score. The K-Means algorithm was applied for finding the clusters of tweets based on features set, whereas, random forest, support vector machine, naïve Bayes, K-nearest-neighbours (KNN), J48 decision tree, and multilayer perceptron were used for classifying the tweets into credibility classes. A significant level of accuracy, precision, and recall was provided by all the classifiers for all the given credibility classes.</p>

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Identification of Credibility Content Measures for Twitter and Sina-Weibo Social Networks

Ahmad

Rizvi

2019

Proceedings of ICETIT 2019

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Automated Credibility Assessment on Twitter

Cited by 37 publications

References 10 publications

Enhancing data quality to mine credible patterns

Enhancing data quality to mine credible patterns

Identification of user’s credibility on twitter social networks

Identification of Credibility Content Measures for Twitter and Sina-Weibo Social Networks

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