Research & Publication

• Franz, M., Zhang, M., Ridout, T. N., Oleinikov, P., Yao, J., Cakmak, F., Franklin Fowler, E. (Forthcoming). Quantifying Data-Driven Campaigning Across Sponsors and Platforms. Media and Communication.

• Ridout, T. N., Cakmak, F. (2024). The Impact of New Transparency in Digital Advertising on Media Coverage. Political Communication. https://doi.org/10.1080/10584609.2024.2303159 

• Ridout, T. N., & Cakmak, F. (Forthcoming). The Continued Relevance of Political Advertising on Television: A Review of Its Effects and Content. In P. R. Baines, P. Harris, C. Panagopoulos, & D. Hejlova (Eds.), Sage Handbook of Political Marketing. Sage.

• Cakmak, F., Zhang, M., Franklin Fowler, E., Oleinikov, P., Floyd, B., Baum, L., Zimmeck, S., Franz, M., Ridout, T. N., Neumann, M., Yao, J., Yu, H., Jacewicz, A., Tassone, I. (2024). 2022 Online Election Advertising Data [Data set]. Figshare (Link Pending).

• Cakmak, F., & Ridout, T. N. (Under Review). When Losing Matters: Emotional Response to Political Loss and News Media Choice. Political Psychology.

• Cakmak, F., Zhang, M., Franklin Fowler, E., Oleinikov, P., Floyd, B., Baum, L., Zimmeck, S., Franz, M., Ridout, T. N., Neumann, M., Yao, J., Yu, H., Jacewicz, A., Tassone, I. (To be Submitted to Scientific Data by September 30th). Comprehensive Political Advertising Datasets from Meta and Google Advertising Platforms.

• Zhang, M., de Paula Moreira, N., Cakmak, F., Moore, S., Floyd, B., Gollust S., Franklin Fowler, E, Niederdeppe, J., Lewis Jr, L. Competitive Messaging on Race and Racism in Local News.

• Franz, M., Franklin Fowler, E., Yao, J., Floyd, B., Zhang, M., Cakmak, F., Oleinikov, P., Baum, L., Ridout, T. N., Neumann, M. Understanding the Scope of Political Advertising on Facebook: A Look at 2020 and 2022.

• Cakmak, F. Politics Get Emotional: Consequences of Affective Polarization on Congressional Dysfunctionality.

• Maintained data pipeline for collecting and analyzing digital and TV political advertising data as part of the Wesleyan Media Project

• Designed and built 9 different GitHub repositories for classification tasks using ad data with machine and deep learning models in R (using caret) and Python (using sklearn, pytorch).

• Extract information on the goal, tone, sentiment and issue of the ad as well as recognize mentioned entities.

• Led two different student teams to build and maintain GitHub repositories above

• Developed complex SQL queries to help analyze candidate spending on political ads by targeting location

• Taught classes on Machine Learning Methods with Audiovisual data and Working with Excel and VBA

Dissertation Thesis

• • Build experimental survey design using JavaScript in Qualtrics to better understand people's emotional reactions to political events as well as their media choice following these reactions

  • Used R with algorithms such as Random Forest, Logistic Regression, and Linear Regression to predict one's media choice and level of polarization

  • Carried out semi-structured interviews with voters and analyzed data using thematic analysis

  • Presented research findings at the Annual Meeting of American Political Science Association

  • Received $8000 in research funding from the highly competitive dissertation improvement grant from American Political Science Association

• Worked as Business Intelligence Analyst with the Corporate Marketing and Sales Strategy Team under Sales department.

• Designed and build an interactive dashboard that updates product sales daily using Power BI.

• This dashboard increased productivity and efficiency among sales teams. Each team were able to save at least 4 hours in work time every week by allowing them to analyze sales reports in real-time.