Published in 2022
- Automatic detection of depression symptoms in twitter using multimodal analysis
- Authors: Ramin Safa, Peyman Bayat, Leila Moghtader
- Journal: The Journal of Supercomputing
- Depression is the most prevalent mental disorder that can lead to suicide. Due to the tendency of people to share their thoughts on social platforms, social data contain valuable information that can be used to identify users’ psychological states. In this paper, we provide an automated approach to collect and evaluate tweets based on self-reported statements and present a novel multimodal framework to predict depression symptoms from user profiles. We used n-gram language models, LIWC dictionaries, automatic image tagging, and bag-of-visual-words. We consider the correlation-based feature selection and nine different classifiers with standard evaluation metrics to assess the effectiveness of the method. Based on the analysis, the tweets and bio-text alone showed 91% and 83% accuracy in predicting depressive symptoms, respectively, which seems to be an acceptable result. We also believe performance improvements can be achieved by limiting the user domain or presence of clinical information.
Published in 2020
- A recommendation system for finding experts in online scientific communities
Online Scientific Communities
Expert Finding Systems
- Authors: Soroush Javadi, Ramin Safa, Seyed Abolghasem Mirroshandel, Mohammad Azizi
- Journal: Journal of AI and Data Mining
- Online scientific communities are bases that publish books, journals, and scientific papers, and help promote knowledge. The researchers use search engines to find the given information including scientific papers, an expert to collaborate with, and the publication venue, but in many cases due to search by keywords and lack of attention to the content, they do not achieve the desired results at the early stages. Online scientific communities can increase the system efficiency to respond to their users utilizing a customized search. In this paper, using a dataset including bibliographic information of the user’s publication, the publication venues, and other published papers provided as a way to find an expert in a particular context where experts are recommended to a user according to his records and preferences. In this way, a user request to find an expert is presented with keywords that represent certain expertise and the system output will be a certain number of ranked suggestions for a specific user. Each suggestion is the name of an expert who has been identified appropriately to collaborate with the user. In evaluation using the IEEE database, the proposed method reached an accuracy of 71.50 percent which seems to be an acceptable result.
Published in 2017
- Venue recommendation based on paper’s title and co-authors network
Academic Recommender Systems
Social Network Analysis
Publication Venue Recommendation
- Authors: Ramin Safa, Seyed Abolghasem Mirroshandel, Soroush Javadi, Mohammad Azizi
- Journal: Journal of Information Systems and Telecommunication
- Information overload has always been a remarkable topic in scientific research, and one of the available approaches in this field is employing recommender systems. With the spread of these systems in various fields, studies show the need for more attention to applying them in scientific applications. Applying recommender systems to scientific domains, such as paper recommendation, expert recommendation, citation recommendation, and reviewer recommendation, are new and developing topics. With the significant growth of the number of scientific events and journals, one of the most important issues is choosing the most suitable venue for publishing papers, and the existence of a tool to accelerate this process is necessary for researchers. Despite the importance of these systems in accelerating the publication process and decreasing possible errors, this problem has been less studied in related works. So in this paper, an efficient approach will be suggested for recommending related conferences or journals for a researcher’s specific paper. In other words, our system will be able to recommend the most suitable venues for publishing a written paper, by means of social network analysis and content-based filtering, according to the researcher’s preferences and the co-authors’ publication history. We used the minimum available free features and the minimum implementing facilities, which to the best of our knowledge have not seen up to now. In addition, it can be argued that the proposed system overcomes the cold start problem which has always been a remarkable task in recommender systems. The results of evaluation using real-world data show acceptable accuracy in venue recommendations.