Predicting Rumor Retweeting Behavior of Social Media Users in Public Emergencies

Tian, Yong; Fan, Rong; Ding, Xuejun; Zhang, Xiaxia; Gan, Tian

doi:10.1109/access.2020.2989180

Cited by 8 publications

(5 citation statements)

References 28 publications

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“…Singh e.t. [ 42 ] have proposed a framework for processing of big data using machine learning approach. The proposed framework showcased fast processing using distributed computing and ability to scale performance of machine learning algorithm.…”

Section: Related Workmentioning

confidence: 99%

Role of twitter user profile features in retweet prediction for big data streams

Sharma

Gupta

2022

Multimed Tools Appl

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To study the various factors influencing the process of information sharing on Twitter is a very active research area. This paper aims to explore the impact of numerical features extracted from user profiles in retweet prediction from the real-time raw feed of tweets. The originality of this work comes from the fact that the proposed model is based on simple numerical features with the least computational complexity, which is a scalable solution for big data analysis. This research work proposes three new features from the tweet author profile to capture the unique behavioral pattern of the user, namely “Author total activity”, “Author total activity per year”, and “Author tweets per year”. The features set is tested on a dataset of 100 million random tweets collected through Twitter API. The binary labels regression gave an accuracy of 0.98 for user-profile features and gave an accuracy of 0.99 when combined with tweet content features. The regression analysis to predict the retweet count gave an R-squared value of 0.98 with combined features. The multi-label classification gave an accuracy of 0.9 for combined features and 0.89 for user-profile features. The user profile features performed better than tweet content features and performed even better when combined. This model is suitable for near real-time analysis of live streaming data coming through Twitter API and provides a baseline pattern of user behavior based on numerical features available from user profiles only.

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Section: Related Workmentioning

confidence: 99%

Role of twitter user profile features in retweet prediction for big data streams

Sharma

Gupta

2022

Multimed Tools Appl

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“…Then Bai, et al proposed a stochastic attention convolutional neural network-based system to detect rumour by using fine-grained and coarse-grained features [2]. Similarly, the identification of retweeting behavior for rumours is presented by Tian, et al [38]. They used reaction time, retweeting frequency and TF-IDF features for model construction and their system achieved an accuracy of 88%.…”

Section: Research Questions and Contributionsmentioning

confidence: 99%

“…Therefore, available solutions cannot be directly applicable at the tweet level. In addition, prior contributions at tweet-level used influence potential [36], network characteristics [13,18,20,36,47], textual features [1,2,13], personal interest [36], temporal, semantic and structural features [3,17,19,38] etc. for rumour detection.…”

Section: Research Questions and Contributionsmentioning

confidence: 99%

“…More specifically, several types of features have been proposed and various models are developed for rumour identification. For instance, influence potential [36], network characteristics [13,18,20,36,47], textual features [1,2,13], personal interest [36], temporal, semantic and structural features [3,17,19,38] etc. It is necessary to utilize some new characteristics to efficiently identify rumours.…”

Section: Introductionmentioning

confidence: 99%

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Rumour identification on Twitter as a function of novel textual and language-context features

Ali

Malik

2022

Multimed Tools Appl

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Social microblogs are one of the popular platforms for information spreading. However, with several advantages, these platforms are being used for spreading rumours. At present, the majority of existing approaches identify rumours at the topic level instead of at the tweet/post level. Moreover, prior studies used the sentiment and linguistic features for rumours identification without considering discrete positive and negative emotions and effective part-of-speech features in content-based approaches. Similarly, the majority of prior studies used content-based approaches for feature generation, and recent context-based approaches were not explored. To cope with these challenges, a robust framework for rumour detection at the tweet level is designed in this paper. The model used word2vec embeddings and bidirectional encoder representations from transformers method (BERT) from context-based and discrete emotions, linguistic, and metadata characteristics from content-based approaches. According to our knowledge, we are the first ones who used these features for rumour identification at the tweet/post level. The framework is tested on four real-life twitter microblog datasets. The results show that the detection model is capable of detecting 97%, 86%, 85%, and 80% of rumours on four datasets respectively. In addition, the proposed framework outperformed the three latest state-of-the-art baselines. BERT model presented the best performance among contextbased approaches, and linguistic features are best performing among content-based approaches as a stand-alone model. Moreover, the utilization of two-step feature selection further improves the detection model performance.

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“…The process is tedious, timeconsuming, and challenging due to increasing information load and dynamics [8]. Development and use of automated real-time rumor tracking and debunking will be of great benefit [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

An Effective Approach for Rumor Detection of Arabic Tweets Using eXtreme Gradient Boosting Method

Gumaei

Al-Rakhami

Hassan

et al. 2022

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Twitter is currently one of the most popular microblogging platforms allowing people to post short messages, news, thoughts, and so on. The Twitter user community is growing very fast. It has an average of 328 million active accounts today, making it one of the most common media for getting information during any influential or important event. Because it is freely used by the public, some credibility checking is required, especially when it comes to events of high importance. Automatic rumor detection in Arabic tweets is a challenging task due to the changes in the structural and morphological nature of the Arabic language, which makes the detection of rumors more difficult than in other languages. In this article, we proposed an effective approach for rumor detection of Arabic tweets using an eXtreme gradient boosting (XGBoost) classifier. We conducted a set of experiments on a public dataset that contained a large number of rumor and non-rumor tweets. The model uses a comprehensive set of features, including content-based, user-based, and topic-based features, allowing one to look at credibility from different angles. The experimental results demonstrated that the proposed XGBoost-based approach achieves 97.18% accuracy on 60% of the dataset as a training set, which is the highest accuracy rate compared with the other methods used in recent related work.

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Predicting Rumor Retweeting Behavior of Social Media Users in Public Emergencies

Cited by 8 publications

References 28 publications

Role of twitter user profile features in retweet prediction for big data streams

Role of twitter user profile features in retweet prediction for big data streams

Rumour identification on Twitter as a function of novel textual and language-context features

An Effective Approach for Rumor Detection of Arabic Tweets Using eXtreme Gradient Boosting Method

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