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Yayın ComStreamClust: a communicative multi-agent approach to text clustering in streaming data(Springer Science and Business Media Deutschland GmbH, 2023-12) Najafi, Ali; Gholipour-Shilabin, Araz; Dehkharghani, Rahim; Mohammadpur-Fard, Ali; Asgari-Chenaghlu, MeysamTopic detection is the task of determining and tracking hot topics in social media. Twitter is arguably the most popular platform for people to share their ideas with others about different issues. One such prevalent issue is the COVID-19 pandemic. Detecting and tracking topics on these kinds of issues would help governments and healthcare companies deal with this phenomenon. In this paper, we propose a novel, multi-agent, communicative clustering approach, so-called ComStreamClust for clustering sub-topics inside a broader topic, e.g., the COVID-19 and the FA CUP. The proposed approach is parallelizable, and can simultaneously handle several data-point. The LaBSE sentence embedding is used to measure the semantic similarity between two tweets. ComStreamClust has been evaluated by several metrics such as keyword precision, keyword recall, and topic recall. Based on topic recall on different number of keywords, ComStreamClust obtains superior results when compared to the existing methods.Yayın Mental disorder and suicidal ideation detection from social media using deep neural networks(Springer, 2024-12) Ezerceli, Özay; Dehkharghani, RahimDepression and suicidal ideation are global reasons for life-threatening injury and death. Mental disorders have increased especially among young people in recent years, and early detection of those cases can prevent suicide attempts. Social media platforms provide users with an anonymous space to interact with others, making them a secure environment to discuss their mental disorders. This paper proposes a solution to detect depression/suicidal ideation using natural language processing and deep learning techniques. We used Transformers and a unique model to train the proposed model and applied it to three diferent datasets: SuicideDetection, CEASEv2.0, and SWMH. The proposed model is evaluated using the accuracy, precision, recall, and ROC curve. The proposed model outperforms the state-of-theart in the SuicideDetection and CEASEv2.0 datasets, achieving F1 scores of 0.97 and 0.75, respectively. However, in the SWMH data set, the proposed model is 4% points behind the state-of-the-art precision providing the F1 score of 0.68. In the real world, this project could help psychologists in the early detection of depression and suicidal ideation for a more efcient treatment. The proposed model achieves state-of-the-art performance in two of the three datasets, so they could be used to develop a screening tool that could be used by mental health professionals or individuals to assess their own risk of suicide. This could lead to early intervention and treatment, which could save lives.
