Browsing by Author "Hussna, Asma Ul"

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    A graph mining-based approach to analyze the dynamics of the Twitter community of COVID-19 misinformation disseminators
    (BRAC University, 2024-04) Hussna, Asma Ul; Alam, Md. Golam Rabiul
    The abundant dissemination of misinformation on social networks has emerged as a worldwide threat, exerting an implicit influence on public opinion and endangering the progress of social, political, and public health domains in general. Amidst the rapid worldwide dissemination of the COVID-19 virus, unfortunately, misinformation about COVID-19 is being created and disseminated at a startling rate. The dissemination of misleading information has led to vast disorientation, social disruptions, and severe repercussions for health-related issues. Moreover, the dissemination of fake or misleading information via social media networking, particularly Twitter, during the COVID-19 pandemic has resulted in an extensive proliferation of information, commonly referred to as an “infodemic.” In order to combat the dissemination of fake news, we have proposed a research model that can predict fake news related to the COVID-19 issue on social media data using classical classification methods such as multinomial na¨ıve bayes classifiers, logistic regression classifiers, and support vector machine classifiers. In addition, we have applied a deep learning-based algorithm named DistilBERT to accurately predict fake COVID-19 news. These approaches have been used in this paper to compare which technique is much more convenient for accurately predicting fake news about COVID-19 on social media posts. The objective of this study is to understand how information is deviating and misinformation is spreading through social media during the COVID- 19 pandemic. Also, this research aims to examine the ecosystem of individuals who spread misinformation, with the objectives of comprehending their collective actions, identifying the most influential disseminators, and examining their online personas and profiles. We leverage the UUIG (User-User Interaction Graph) to capture the misinformation disseminators’ behavioral interactions. The following research analysis reveals the following significant findings: (a) the population of disseminators is growing rapidly even though today; (b) the community of disseminators comprises professional spreaders; above 3% of the fake news spreading population dominates others; and (c) they exhibit a high degree of collaboration among the fake news spreaders; we observe five big communities of collaborators. Our work represents a notable advancement in utilizing publicly available online data to gain insights into the community that spreads malicious misinformation about COVID-19.
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    Prerona- mental health Bengali chatbot for digital counselling
    (BRAC University, 2020-10) Hussna, Asma Ul; Laz, Azmiri Newaz Khan; Sikder, Md Shammyo; Uddin, Jia; Esfar-E- Alam, A.M.
    ”Human”,”Machine”,”Interaction” this word refers to the modern era of technology where researchers give emphasis on the interaction between computer machines and humans. We humans cannot confine ourselves depending on only human-based health care systems. The current time of digitalization brings with it progress and additional opportunities for medical care in general and clinical psychology in particular. In this paper, we have presented a model named PRERONA: a psychological Bengali chatbot for depressed persons. PRERONA is designed to help depression struggling especially for Bengali users who are lack attention or do not have anyone to talk about their problem. The main goal of this chatbot ”Prerona” is to give instant answers to questions and queries as well as to give proper mental health to Bengalis in times of depression. It is able to carry out real-time chats in initially three languages, Bangla, English, and Korean. Natural language processing (NLP) is used here to successfully implement these languages. First, it matches the input with the query that exists in the database, then it identifies each matching sentence with the input, and finally, it selects the one with the highest suite in response to the input. To test the proposed chatbot, we have interviewed 300 persons to come up with the datasets. Existing chatbots such as Woebot, Wysa, Moodkit, etc. do not allow users to express them. However, PRERONA allows the user space to talk about whatever they want and it is intelligent enough to ask questions as well as answer questions.