Prevalence of internet gaming addiction and its association with sleep quality in medical students.

Background: In Asia, there are approximately 2.3 billion internet users. Addiction to internet gaming takes a multifaceted toll on an individual's physical and mental well-being, casting a long shadow over their daily endeavors and also their sleep cycle. Aim: To study the prevalence of internet gaming addiction and how it affects sleep quality in medical students. Materials and Methods: A cross-sectional study was performed with a sample size (n = 112) in the Government Medical College, Datia (M.P.), and data were collected using a semi-structured proforma including the Internet Gaming Disorder Scale and Mini Sleep Questionnaire in the study population. Results: In the study population with age group 18-28 years (mean age: 21 ± 1.7 years), the majority of them belong to the Hindu religion (91.1%), nuclear family (66.1%), and urban community (75.9%). Most students (n = 74) had mild to moderate sleep difficulty related to internet gaming addiction, that is, 65.1%. The most common response was "sometimes" on the Internet Gaming Disorder Scale. A significant association was observed between gaming disorder and parameters of sleep mainly in waking up too early, daytime sleepiness, snoring, feeling tired, and headache upon waking up with gaming disorder. Conclusion: Among medical students, gaming addiction is significantly associated with poor sleep quality. Steps need to be taken to promote healthy internet use to improve sleep quality and mitigate negative effects to avoid long-term health impacts.

CanarDeep: a hybrid deep neural model with mixed fusion for rumour detection in social data streams.

The unrelenting trend of doctored narratives, content spamming, fake news and rumour dissemination on social media can lead to grave consequences that range from online intimidating and trolling to lynching and riots in real- life. It has therefore become vital to use computational techniques that can detect rumours, do fact-checking and inhibit its amplification. In this paper, we put forward a model for rumour detection in streaming data on social platforms. The proposed CanarDeep model is a hybrid deep neural model that combines the predictions of a hierarchical attention network (HAN) and a multi-layer perceptron (MLP) learned using context-based (text + meta-features) and user-based features, respectively. The concatenated context feature vector is generated using feature-level fusion strategy to train HAN. Eventually, a decision-level late fusion strategy using logical OR combines the individual classifier prediction and outputs the final label as rumour or non-rumour. The results demonstrate improved performance to the existing state-of-the-art approach on the benchmark PHEME dataset with a 4.45% gain in F1-score. The model can facilitate well-time intervention and curtail the risk of widespread rumours in streaming social media by raising an alert to the moderators.