Denigrate Comment Detection in Low-Resource Hindi Language Using Attention-Based Residual Networks

Cyberspace has been recognized as a conducive environment for use of various hostile, direct, and indirect behavioural tactics to target individuals or groups. Denigration is one of the most frequently used cyberbullying ploys to actively damage, humiliate, and disparage the online reputation of target by sending, posting, or publishing cruel rumours, gossip, and untrue statements. Previous pertinent studies report detecting profane, vulgar, and offensive words primarily in the English language. This research puts forward a model to detect online denigration bullying in low-resource Hindi language using attention residual networks. The proposed model Hindi Denigrate Comment-Attention Residual Network (HDC-ARN) intends to uncover defamatory posts (denigrate comments) written in Hindi language which stake and vilify a person or an entity in public. Data with 942 denigrate comments and 1499 non-denigrate comments is scraped using certain hashtags from two recent trending events in India: Tablighi Jamaat spiked Covid-19 (April 2020, Event 1) and Sushant Singh Rajput Death (June 2020: Event 2). Only text-based features, that is, the actual content of the post, are considered. The pre-Trained word embedding for Hindi language from fastText is used. The model has three ResNet blocks with an attention layer that generates a post vector for a single input, which is passed through a sigmoid activation function to get the final output as either denigrate (positive class) or non-denigrate (negative class). An F-1 score of 0.642 is achieved on the dataset. © 2021 Association for Computing Machinery.

Problematic Use of Digital Technologies and Its Impact on Mental Health During COVID-19 Pandemic: Assessment Using Machine Learning

Many nations have imposed lockdowns due to the COVID-19 pandemic as a measure to prevent the spread of disease among its population. These lockdowns have confined people at their homes which is leading them to use digital technologies such as Internet, social media, smartphones, more than ever before. The problematic use of these digital technologies may impact their mental and emotional health. This chapter discusses the role of machine learning to assess addiction to various digital technologies and its impact on mental and emotion health and on sleep quality during the COVID-19 pandemic. Three case studies are provided to demonstrate how machine learning can be used to assess these addictions and related disorders during the pandemic. Gaussian mixture clustering is implemented to group people with similar Twitter usage patterns to identify addictive Twitter usage during the pandemic. The results convey that 11.71% of users show addictive Twitter usage patterns and 4.05% of users show highly addictive Twitter usage patterns while 2.70% of users show dangerously addictive usage patterns. “Sadness” and “anger” are the dominating emotions among these users in contrast to “happiness” which is the dominating emotion among non-addictive users. A similar approach is used to cluster students with similar smartphone usage patterns and nomophobia scores to identify nomophobic behavior during the pandemic. The results show that 4.5% of students are at extremely high risk whereas 73% of students are at high risk. A review of studies identifies the emergence of machine learning for assessment of mental and emotional health during the COVID-19 pandemic. A case study on sleep quality assessment using data from wearable sensors convey that sleep quality of students has been reduced significantly during the pandemic with a maximum decrease of 90.90%.

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.