Assessing the Impacts of Social Media on Youth: A Case Study of University Students of Quetta

Shazadi Irrum, Dr. Romana Ambreen , Dr. Ahmed Saeed — Mon, 09 Jun 2025 00:00:00 +0500

ABSTRACT

Background: Social media is a remarkable innovation in this modern era, and majority of the young generations are highly engaged with social media. The excessive use of social media among university students impacts their academic performance, health, and social behavior.

Objective: This study aims to find out what are the impacts of social media on university students in Quetta city, Pakistan.

Methods: Data were collected from 400 respondents, including both male and female students aged 18-25 from four universities in Quetta city using a semi-structured questionnaire. Stratified sampling was employed to ensure representation across different subgroups. Descriptive statistics were utilized to analyzed the data.

Results: The findings of the current study indicate that university students in Quetta City engage with various social media platforms, which exert both positive and negative influences on their academic performance and overall well-being. Specifically, 36% of respondents reported positive impacts, while 49% indicated that social media has a detrimental effect on their academic performance. Additionally, 15% of respondents acknowledged both positive and negative impacts of social media. 71% of respondents reported that excessive social media use negatively impacts their physical and mental well-being. 29% of respondents indicated that social media contributed to psychological issues, including stress, anxiety, sadness, and insomnia.

Conclusions: Given these findings, the government needs to implement educational programs in universities to raise awareness about the responsible use of social media, online safety, and the risks of excessive usage. Such initiatives will help students navigate the challenges posed by social media more effectively, promoting a healthier balance between online and offline activities.

Drought Prediction in Balochistan: A Comparative Study of Arimax and Machine Learning Models

Ms. Sabiha Munir, Dr. Farhat Iqbal — Sun, 31 Dec 2023 00:00:00 +0500

Background: Drought is harmful to the environment and human life and has a major impact on reducing the quality of life. It’s a natural disaster whose negative effects spill into farms, water resources, and ecosystems, causing crop failures and food insecurity. Thus, it is one of the important global issues. Objective: In this research, we intend to model drought in different regions of Balochistan, Pakistan, using machine learning and traditional methods. The data of monthly precipitation and minimum and maximum temperature from 1951 to 2017 from five stations in Balochistan were used. Methods: The commonly used method of Autoregressive Integrated Moving Average with independent variables (ARIMAX) was compared with three machine learning methods, namely Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN). The Standardized Precipitation Index (SPI) based on three, six and nine months was chosen as a drought indicator. Four different models using the lagged values of variables were developed. To evaluate the accuracy of these models, three statistical measures, RMSE, MAE, and R2 were used. Results: Based on the results of this study, we found that the RF method with the M1 model (with two lagged values of variables) provided satisfactory results of R2 at each station we studied. Additionally, the RF model showed the best results for the Panjgur station. In this study, we obtained R2 values 0.825, 0.756, 0.584, 0.731, 0.902 for Dalbandin, Quetta, Sibi, Zhob and Panjgur stations, respectively. These results were average training and testing results for each station. The RMSE, MAE and R2 values of the Panjgur station were 0.177, 0.115, 0.974, 0.337, 0.227, 0.902 during the training and testing phases, respectively. Conclusion: We found that the RF model has a high potential to forecast drought more precisely than other alternative approaches due to its great accuracy and the outcomes of this study will help the researchers accurately predict droughts.

Journal of Geography and Social Sciences

Assessing the Impacts of Social Media on Youth: A Case Study of University Students of Quetta

Drought Prediction in Balochistan: A Comparative Study of Arimax and Machine Learning Models