Propaganda Identification on Twitter Platform During COVID-19 Pandemic Using LSTM

Online social media has been evolved as a universal platform for sharing information. Termination being shared on these platforms can be dubious or filthy. Propaganda is one of the systematic methods by which behavior of user can be manipulated. In this work, various machine learning methods are used for detecting such types of information on online social media. Data is collected d from Twitter using its API with the help of various ambiguous hashtags. The results showed that proposed Long Short Term Memory (LSTM) based propaganda identification showed better results than other machine learning techniques. An accuracy of 77.15% is achieved using the proposed approach. In the future BERT model can be used for achieving better Accuracy.

Hybrid Approach for Detecting Propagandistic Community and Core Node on Social Networks

People share their views and daily life experiences on social networks and form a network structure. The information shared on social networks can be unreliable, and detecting such kinds of information may reduce mass panic. Propaganda is a kind of biased or unreliable information that can mislead or intend to promote a political cause. The disseminators involved in spreading such information create a sophisticated network structure. Detecting such communities can lead to a safe and reliable network for the users. In this paper, a Boundary-based Community Detection Approach (BCDA) has been proposed to identify the core nodes in a propagandistic community that detects propagandistic communities from social networks with the help of interior and boundary nodes. The approach consists of two phases, one is to detect the community, and the other is to detect the core member. The approach mines nodes from the boundary as well as from the interior of the community structure. The leader Ranker algorithm is used for mining candidate nodes within the boundary, and the Constraint coefficient is used for mining nodes within the boundary. A novel dataset is generated from Twitter. About six propagandistic communities are detected. The core members of the propagandistic community are a combination of a few nodes. The experiments are conducted on a newly collected Twitter dataset consisting of 16 attributes. From the experimental results, it is clear that the proposed model outperformed other related approaches, including Greedy Approach, Improved Community-based 316 Robust Influence Maximization (ICRIM), Community Based Influence Maximization Approach (CBIMA), etc. It was also observed from the experiments that most of the propagandistic information is being shared during trending events around the globe, for example, at times of the COVID-19 pandemic.

Ensemble Approach for Detecting COVID-19 Propaganda on Online Social Networks

COVID-19 affected the entire world due to the unavailability of the vaccine. The social distancing was a contributing factor that gave rise to the usage of Online Social Networks. It has been seen that people share the information that comes to them without verifying its source . One of the common forms of information that is disseminated that have a radical purpose is propaganda. Propaganda is organized and conscious method of molding conclusions and impacting an individual's contemplations to accomplish the ideal aim of proselytizer. For this paper, different propagandistic tweets were shared in the COVID-19 Era. Data regarding COVID-19 propaganda was extracted from Twitter. Labelling of data was performed manually using different propaganda identification techniques and Hybrid feature engineering was used to select the essential features. Ensemble machine learning classifiers were used for performing the binary classification. Adaboost shows an accuracy of 98.7%, which learns from a weak learning algorithm by updating the weights. © 2022 University of Baghdad-College of Science. All rights reserved.

NNPCov19: Artificial Neural Network-Based Propaganda Identification on Social Media in COVID-19 Era

The latest trend of sharing information has evolved many concerns for the current researchers, which are working on computational social sciences. Online social network platforms have become a tool for sharing propagandistic information. This is being used as a lethal weapon in modern days to destabilize democracies and other political or religious events. The COVID-19 affected almost every corner of the world. Various propagandistic tweets were shared on Twitter during the peak time of COVID-19. In this paper, improved artificial neural network algorithm is proposed to classify tweets into propagandistic and nonpropagandistic class. The data are extracted using multiple ambiguous hashtags and are manually annotated into binary class. Hybrid feature engineering is being performed by combining "Term Frequency (TF)/Inverse Document Frequency (IDF)," "Bag of Words," and Tweet Length. The proposed algorithm is compared with logistic regression, support vector machine, and multinomial Naive Bayes. Results showed that improved artificial neural network algorithm outperforms other machine learning algorithms by having 77.15% accuracy, 77% of recall, and 79% precision. In future, deep learning approaches like LSTM may be used for this classification task.

Quantifying Suicidal Ideation on Social Media using Machine Learning: A Critical Review

Suicidal ideation is one of the severe mental health issues and a serious social problem faced by our society. This problem has been usually dealt with through the psychological point of view, using clinical face to face settings. There are various risk factors associated with suicides, including social isolation, anxiety, depression, etc., that decrease the threshold for suicide. The COVID-19 pandemic further increases social isolation, posing a great threat to the human population. Posting suicidal thoughts on social media is gaining much attention due to the social stigma associated with the mental health. Online Social Networks (OSN) are increasingly used to express the suicidal thoughts. Recently, a top Indian actor industry took the harsh step of suicide. The last Instagram posts revealed signs of depression, which if anticipated could have saved the precious life. Recent research indicated that the public information on social media provides valuable insights on detecting the users with the suicidal ideation. The motive of this study is to provide a systematic review of the work done already in the use of social media for suicide prevention and propose a novel classification approach that classifies the suicide related tweets/posts into three levels of distress. Moreover, our proposed classification task which was implemented through various machine learning techniques revealed high accuracy in classifying the suicidal posts. Among all algorithms, the best performing algorithm was that of the decision tree, with an F1 score ranging 0.95-0.97. After thoroughly studying the work achieved by different researchers in the area of suicide prevention, our study critically analyses those works and finds various research gaps and solves some of them. We believe that our work will motivate research community to look into other gaps that will in turn help psychiatrists, psychologists, and counsellors to protect individuals suffering from suicidal ideation. © 2021 University of Baghdad-College of Science. All rights reserved.

Multi-Class Suicide Risk Prediction on Twitter Using Machine Learning Techniques

The enormous growth of Social Networking Sites (SNS) resulted in more virtual engagement of people in the last decade. Amount of data generated through these SNS is enormous, allowing researchers to analyse this Big data. People share their opinions and thoughts related to any topic of interest. As suicide is one the leading cause of death worldwide, it has become a hot topic on which different researchers are working. The Covid19 further amplified the crisis due to social isolation which is the main risk factor for suicide. The problem has usually been analysed and dealt through a physiological point of view using Questionnaires and face to face settings but social stigma prevents its efficacy. In our research, we use well-known machine learning algorithms for multi-classification of Suicidal risk on social media so that individuals having high risk could be identified and counselled properly to save precious human lives. The data has been experimented through four popular machine learning algorithms: Logistic Regression, Multinomial Naïve Bayes, Support Vector Machine and Decision tree. The results generated are impressive with F1 Score ranging from 0.74 to 0.97. The Best performing algorithm was Decision tree that achieved an F-measure of 0.97, 0.94 and 0.96 for classifying suicidal text into three levels of concern. © 2020 IEEE.