Computer Science student wins award for research into drug abuse detection online

Computer Science student wins award for research into drug abuse detection online

Friday, May 13, 2022

Media Contact: Jordan Bishop | Editor, Department of Brand Management | 405-744-7193 |

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

This year in CADRE 2022, Khaled Mohammed Saifuddin — a Ph.D. candidate in the Department of Computer Science — won the first place 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 use of opioid drugs has increased markedly since the early 2000s. Recently, the US government declared a national emergency to slow down the death rate related to drug abuse (DA). In this research work, Khaled presented a graph-based unique model that can automatically detect DA from openly available social media data.

At first, to accomplish the target, a significant amount of Twitter posts were collected based on a list of keywords that included some drug names and drug abuse terms as well. After that, the text data were represented as graph data called text graphs, which are capable of handling complex structures and capturing local and global word-to-word co-occurrence.

Two different types of text graphs were constructed from the tweets: document-level text-graph and corpus-level text graphs. Afterwards, different Graph Neural Networks were applied to get the representation of nodes and graphs.

Finally, the representations were passed to a machine learning classifier to classify whether a tweet related to DA 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%, almost 20% better than the baselines.

For more information on the OSU Department of Computer Science, go to

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