Biography

I am a double-degree PhD candidate in Computer Science at the University of Eastern Finland and Linnaeus University. In my PhD research, I am supervised by Prof. Pasi Fränti and Prof. Mikko Laitinen. My research focuses on the clustering and modeling of large-scale social networks, and my primary area of study involves extensive datasets obtained from Twitter (recently renamed X). Additionally, my research extends to gathering and analyzing data from various other social media platforms, including Facebook, YouTube, and Discord, as part of my academic studies. I also hold a bachelor's degree in Software Engineering and a master's degree in Artificial Intelligence and Robotics.

I moved to Finland (Joensuu) in 2018. Joensuu is located in the heart of Finland's eastern region of North Karelia. It is a place where coffee consumption is as much a survival strategy as it is a social pastime and where the locals might tell you, half-jokingly, that the city has two seasons: winter and July. I came here in 2018 first as a project researcher, and later, in 2019, I started my doctoral studies.

Research interests

• (Graph) Clustering, Social Network Modeling, Data Mining
• Natrual Language Processing: Topice modeling, Information Retrieval, Sentiment Analyis
• (Deep) Neural Networks, Generative Models, Autoencoders

Skills

• General programming: Python
• Version control: Git
• Computing and visualization: Gephi, MATLAB, QGIS, LATEX
• Scripting and Automation: Bash, PowerShell
• Web and Application Servers: Apache
• Infrastructure and Cloud Computing: Terraform, OpenStack
• Databases: Elasticsearch, PostgreSQL, MongoDB
• Deep Learning Frameworks: PyTorch, TensorFlow, Keras
• Web Development: Django, Flask, Dash, PHP, HTML, CSS, JavaScript, Bootstrap

Journal articles

1. Fatemi, M., Laitinen, M., & Fränti, P. (2024). Strong and Weak-tie Ego Networks: How to Measure the Strength? [Draft]
2. Laitinen, M., Rautionaho, P., Halonen, M., & Fatemi, M. (2024). Do we swear more with friends or with acquaintances? F##k in social networks. [Submitted to Lingua Special Issue on Bad Language and Vulgarity in Online and Public Discourse].
3. Fatemi, M., Sieranoja, S., Laitinen, M., & Fränti, P. (2024). Detecting Connectivity Patterns in Nordic Twittersphere by Cluster Analysis [submitted to SN Computer Science].
4. Laitinen, M., & Fatemi, M. (2023). Data-intensive sociolinguistics using social media. Annales Academiae Scientiarum Fennicae, 2023(2), Article 2. Link
5. Kopacheva, E., Fatemi, M., & Kucher, K. (2023). Using social-media-network ties for predicting intended protest participation in Russia. Online Social Networks and Media, 37–38, 100273. Link
6. Laitinen, M., Fatemi, M., & Lundberg, J. (2020). Size Matters: Digital Social Networks and Language Change. Frontiers in Artificial Intelligence, 3. Link
7. Fatemi, M., & Safayani, M. (2019). Joint sentiment/topic modeling on text data using a boosted restricted Boltzmann Machine. Multimedia Tools and Applications, 78(15), 20637–20653. Link

Conference articles

1. Laitinen, M., Fatemi, M., & Salimi, M. (2024). Weak-tie hypothesis in digital networks. 52nd annual meeting of New Ways of Analyzing Variation (NWAV), 7-9 November, Miami, Florida.
2. Laitinen, M., & Fatemi, M. (2024). Testing the weak-tie hypothesis with social media. Of the 11th Conference on Computer-Mediated Communication and Social Media Corpora, 46. Link
3. Schweinberger, M., Fatemi, M., Hames, S., Haugh, M., Laitinen, M., Rautionaho, P., & Takahashi, M. (2023, June 21). Who swears most – and in what social settings?. ISLE7. Brisbane, Australia.
4. Kucher, K., Fatemi, M., & Laitinen, M. (2021). Towards Visual Sociolinguistic Network Analysis. 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory. 3, 248–255. Link
5. Fatemi, M., Kucher, K., Laitinen, M., & Fränti, P. (2021). Self-Similarity of Twitter Users. 2021 Swedish Workshop on Data Science (SweDS), 1–7. Link
6. Laitinen, M., & Fatemi, M. (2020). Working with Complex Metadata: Computational Approaches to Digital Social Networks. ICAME 41, May 20-24 2020. Heidelberg, Germany.
7. Laitinen, M., Tyrkkö, J., Levin, M., Lakaw, A., Fatemi, M., & Ihrmark, D. (2019). Americanization in the Nordic Contexts on Twitter. ICAME 40, June 1-5 2019. Neuchâtel, Switzerland. Link

Book chapters

1. Laitinen, M., & Fatemi, M. (2022). Big and rich social networks in computational sociolinguistics. In Social and Regional Variation in World Englishes. Routledge. Link

Extended abstracts

1. Fatemi, M., Laitinen, M., & Fränti, P. (2023). ”Your Words and Network Ties Could Reveal Your Location,” International Society for the Linguistics of English (ISLE7), Brisbane, Australia.

Nordic Tweet Stream (NTS)

Link: https://nordictweetstream.fi
Description: NTS is a web application tool that we developed as part of a project funded by the Research Council of Finland and its Research Infrastructure program. It allows users to search, subset, visualize, and download user-generated social media data from the Nordic region. The NTS dataset contains nearly 74 million messages from over 888 thousand user accounts from January 2013 to May 2023. We hosted the tool on CSC cPouta, which is based on OpenStack. The backend and database primarily utilize Django, Elasticsearch, and PostgreSQL.

Machine Learning Group at UEF

Link: https://cs.uef.fi/ml
Description: The research group that I am affiliated with and conducting my PhD studies at the University of Eastern Finland. Our group primarily concentrates on clustering methods, location-based services, data and web mining, and optimizing healthcare applications. We are also engaged in activities such as the traveling salesman problem (TSP), Twitter data analysis, intelligent robotics, algorithmic music composition, and biomedical signal analysis.

Center for Data Intensive Sciences and Applications (DISA) at LNU

Link: https://lnu.se/en/disa
Description: In my PhD studies at Linnaeus University, I a member of the Centre for Data Intensive Sciences and Applications (DISA). In DISA, the concentration is on harnessing the power of Big Data across various domains, including engineering, the sciences, and the humanities. Beyond academia, DISA actively partners with industry and public sectors, leveraging data to address real-world challenges and drive innovation in IT, healthcare, and more, benefiting both research communities and external stakeholders.

Nordic Twitter Network 2022 (NTN-2022)

Link: https://github.com/NTN_2022
Description: The NTN-2022 dataset is a network dataset focused on Twitter users within the Nordic countries between November 2016 and November 2022. This dataset is intended for academic research and analysis focused on the empirical structure of social relations within the Nordic Twittersphere. Users are encouraged to cite our paper in any resulting work.