Graduate Research Student
Sep 2022 - Present, Waterloo, ON, Canada
I am a research student affiliated with Critical ML lab. My research focuses on Machine Learning, Big Data Analytics, and Optimization.
Dec 2020 - May 2022, Dhaka, Bangladesh
A data science lab devoted to social media mining, opinion mining, and social computing directed by Dr. Saddam Hossian.
Peer-reviewed Journal Papers
[J.1] Ahmed Shahriar Sakib, Md. Saddam Hossain Mukta, Fariha Rowshan Huda, A.K.M. Najmul Islam, Tohedul Islam and Mohammed Eunus Ali, “Identifying Insomnia from Social Media Posts: Psycholinguistic Analyses of User Tweets”, Journal of Medical Internet Research (JMIR), 23(12):e27613, Dec 2021. [IF-7.08]
Summary : The purpose of this research is to build an insomnia prediction model from users’ psycholinguistic patterns, i.e., word usage, semantics, and their Big5 personality traits as derived from tweets. Using Twitter’s advanced search technique, users were collected based on tweet keywords (e.g., “insomnia”, “sleepless” ) from six different countries where English is the first language and divided into two groups - insomniac and non-insomniac (YES and NO). Psycholinguistic tools - LIWC and Empath were used to build psycholinguistic profiles of the users from their word choices and the semantic relationships between the words of their tweets. Also to find the relationship between a user’s personality traits and insomnia, IBM Watson Personality Insights API was used. Feature selection was performed via Fishers’ linear discriminant analysis using IBM SPSS. Moreover, to find the contextual relationships between words in a sentence BERT word-embedding vectors were generated using Sentence Transformers. Finally, a double-weighted ensemble classification model was developed to predict insomnia from both psycholinguistic and personality traits as derived from user tweets.
Peer-reviewed Conference Papers
[C.1] Md. Saddam Hossain Mukta, Ahmed Shahriar Sakib, Md. Adnanul Islam, Mohammed Eunus Ali, Mohiuddin Ahmed and Mumshad Ahamed Rifat, “Friends’ Influence Driven Users’ Value Change Prediction from Social Media Usage”, Presented at 2021 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS 2021)
Summary : In this study, we show that we can predict the value change of a person by considering both the influence of her friends and her social media usage. This is the first work in the literature that relates the influence of social media friends on the human value dynamics of a user. We propose a Bounded Confidence Model (BCM) based value dynamics model from 275 different ego networks in Facebook that predicts how social influence may persuade a person to change her value over time. We use a particle swarm optimization based hyperparameter tuning technique to optimize our proposed model’s hyperparameters by applying regression.
Thesis : Can We Predict Insomnia From Tweets?
Summary : In this research, we proposed an approach to predict insomnia from tweets. Tweets from around 1800 users were collected and psycholinguistic features were extracted using LIWC2007 and Empath. Feature selection process invloved two different approaches - i) regsubsets for LIWC feature set and ii) Fisher’s discriminant analysis using IBM SPSS for Empath feature set. After performing the discriminant analysis, empath feature set did not show acceptable correlation with the target variable (Insomnia Yes or No). Therefore, using LIWC feature set classic machine learning models were applied to predict insomnia. The best-performing model was Random Forest with an accuracy of around 70%. This study aims to raise awareness and provide a prognosis about insomnia before it turns into a health-hazardous form.
Details about this work can be found here - PDF.