Computer science student wins award for research on detecting online addiction

Friday, May 13, 2022

Media contact: Jordan Bishop | Editor, Brand Management Department | 405-744-7193 | [email protected]

The Oklahoma State University Coalition for the Advancement of Digital Research and Education (CADRE) recently partnered with Dell Technologies and Intel to recognize a student’s outstanding use of data science and computer science.

This year in CADRE 2022, Khaled Mohammed Saifuddin — a Ph.D. candidate in the Department of Computer Science — won first place in the Dell Intel Student Award for Outstanding Use of Data Science and Computing for his research work with advisor Dr. Esra Akbas, “Drug Abuse Detection in Twitter-sphere: Graph-Based Approach”.

The rate of non-medical opioid use has increased markedly since the early 2000s. Recently, the US government declared a national emergency to slow the rate of drug-related (AD) death. In this research work, Khaled presented a unique graph-based model that can automatically detect AD from freely available social media data.

In the beginning, to achieve the goal, a large number of Twitter posts were collected based on a list of keywords that also included drug names and drug abuse terms. After that, textual data was represented as graphical data called text graphics, which can handle complex structures and capture local and global word-by-word co-occurrence.

Two different types of text graphs were constructed from tweets: document-level text graphs and corpus-level text graphs. Then, different graphical neural networks were applied to obtain the representation of nodes and graphs.

Finally, the representations were fed to a machine learning classifier to classify whether a tweet was DA-related or not. Thus, the text classification problem was presented as a node and graph classification problem.

The experimental result shows that the proposed model outperforms the state-of-the-art baseline models with a maximum accuracy of 96.4%, which is almost 20% better than the baselines.

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