Rumour detection using deep learning and filter-wrapper feature selection in benchmark twitter dataset.

Microblogs have become a customary news media source in recent times. But as synthetic text or 'readfakes' scale up the online disinformation operation, unsubstantiated pieces of information on social media platforms can cause significant havoc by misleading people. It is essential to develop models that can detect rumours and curtail its cascading effect and virality. Undeniably, quick rumour detection during the initial propagation phase is desirable for subsequent veracity and stance assessment. Linguistic features are easily available and act as important attributes during the initial propagation phase. At the same time, the choice of features is crucial for both interpretability and performance of the classifier. Motivated by the need to build a model for automatic rumour detection, this research proffers a hybrid model for rumour classification using deep learning (Convolution neural network) and a filter-wrapper (Information gain-Ant colony) optimized Naive Bayes classifier, trained and tested on the PHEME rumour dataset. The textual features are learnt using the CNN which are combined with the optimized feature vector generated using the filter-wrapper technique, IG-ACO. The resultant optimized vector is then used to train the Naïve Bayes classifier for rumour classification at the output layer of CNN. The proposed classifier shows improved performance to the existing works.

Role of Emotion in Excessive Use of Twitter During COVID-19 Imposed Lockdown in India.

The COVID-19 pandemic and the lockdowns to contain it are affecting the daily life of people around the world. People are now using digital technologies, including social media, more than ever before. The objectives of this study were to analyze the social media usage pattern of people during the COVID-19 imposed lockdown and to understand the effects of emotion on the same. We scraped messages posted on Twitter by users from India expressing their emotion or view on the pandemic during the first 40 days of the lockdown. We identified the users who posted frequently and analyzed their usage pattern and their overall emotion during the study period based on their tweets. It was observed that 222 users tweeted frequently during the study period. Out of them, 13.5% were found to be addicted to Twitter and posted 13.67 tweets daily on an average (SD: 4.89), while 3.2% were found to be highly addicted and posted 40.71 tweets daily on an average (SD: 9.90) during the study period. The overall emotion of 40.1% of the users was happiness throughout the study period. However, it was also observed that users who tweeted more frequently were typically angry, disgusted, or sad about the prevailing situation. We concluded that people with a negative sentiment are more susceptible to addictive use of social media.