Prof. Dr. Amina Muazam

Chairperson,
Department of Applied Psychology
Lahore College for Women University, Lahore

Short Bio:

Prof. Dr. Amina Muazzam received her PhD degree in Applied Psychology on HEC competitive scholarship. She is Charles Wallace fellow and actively working in the Mental Health division in Manchester University, UK. She has been elected “Director at Large” for the International Council for Psychologists for two consecutive terms. (2018-2021& 2022-2025). She has impact factor of 21.89 with 400+ citations. She has published around 75 research papers in prestigious journals. She has presented her work across Europe, Canada, America and far East Asia. She has written two books and one chapter in IGI. She also worked as Director Research and IN charge Central Research Lab where she diligently worked to enhance the quality of research in university. She has developed World Health Organization Rehabilitation Policy 2030 as a world lead for mental health groups. She is a PI/co-PI of several research projects. She is working as a core member of technical work for launch of presidential tele-mental helpline. Currently, she is serving as a tenured professor and Chairperson Department of Applied Psychology.

Keynote Topic: Social Mining for Sustainable Cities: Thematic Study of Gender-Based Violence Coverage in News Articles and Domestic Violence in Relation to COVID-19

Abstract

We argue that social computing and its diverse applications can contribute to the attainment of sustainable development goals (SDGs)—specifically to the SDGs concerning gender equality and empowerment of all women and girls, and to make cities and human settlements inclusive. To achieve the above goals for the sustainable growth of societies, it is crucial to study gender-based violence (GBV) in a smart city context, which is a common component of violence across socio-economic groups globally. This paper analyzes the nature of news articles reported in English newspapers of Pakistan, India, and the UK—accumulating 12,693 gender-based violence-related news articles. For the qualitative textual analysis, we employ Latent Dirichlet allocation for topic modeling and propose a Doc2Vec based word-embeddings model to classify gender-based violence-related content, called GBV2Vec. Further, by leveraging GBV2Vec, we also build an online tool that analyzes the sensitivity of Gender-based violence-related content from the textual data. We run a case study on GBV concerning COVID-19 by feeding the data collected through Google News API. Finally, we show different news reporting trends and the nature of the gender-based violence committed during the testing times of COVID-19. The approach and the toolkit that this paper proposes will be of great value to decision-makers and human rights activists, given the prompt and coordinated performance against gender-based violence in smart city context—and can contribute to the achievement of SDGs for sustainable growth of human societies.

Keywords: Social mining, sustainable cities, gender-based violence, domestic violence, COVID-19