This report presents an exploratory analysis conducted on the r/Turkey community to understand the news sharing and consumption practices on the Reddit platform in the context of Turkey. For this study, posts made by users on the Reddit Turkey (r/Turkey) page between April 1, 2024 and June 23, 2024 were obtained using the Reddit API service. The collected data includes 2185 posts in the “hot” category shared by active users who followed the community during the 84-day period.

The study aimed to answer the following questions:

• What is the general polarity (positive, negative, neutral) of the posts on the r/Turkey community page?
• What are the themes of the posts on the r/Turkey community page?
• What sources are used in the news shared on the r/Turkey community page?
• What are the prominent expressions in the news shared on the r/Turkey community page?

A multi-method approach was chosen to answer the aforementioned research questions using the collected data (n=2185). Within the framework of this approach, text analysis, sentiment analysis, and content analysis were performed on the posts to obtain findings on the content, polarity status, news category, etc. of the posts shared on the r/Turkey community.

The goal of sentiment analysis is to correctly classify text in either polarity or basic emotion categories and to gain insights into the content of the text and the judgments it makes. In this study, the most recent artificial intelligence language model offered by OpenAI, ChatGPT4o was used due to the lack of accurate sentiment analysis solutions for Turkish texts in particular. In the sentiment analysis process carried out using the OpenAI API service, 2185 posts were classified as positive, negative, and neutral. Approximately 10% of the data was coded by a Phd student and an agreement rate of approximately 80% was achieved between ChatGPT and the coder. The sentiment analysis findings were used to answer the first research question.

The goal of quantitative text analysis is to treat text as data and perform measurements and calculations on it rather than focusing on the discourses and structures behind the text. In this context, frequency analysis was performed to find the prominent expressions in the header texts of the posts on the r/Turkey page. The R programming software (R Core Team, 2023) and the associated Quanteda library (Benoit et al., 2018) were used in the text analysis process.

Content analysis aims to make the analysis of media content systematic, repeatable, and quantitative. In this way, more objective and reliable results can be obtained in the analysis. The content analysis phase of the research consists of two steps, the first of which is completely automated coding using artificial intelligence and the second of which is based on manual coding. In the first step, it was observed which of the eight determined themes the posts on the page belonged to. The classification of the posts was completely carried out by the ChatGPT4o model, and again 10% of the data was manually coded to measure the reliability of this process. An agreement rate of close to 75% was achieved between ChatGPT4o and the manual coder. In the second step, the source usage in 603 posts was examined and it was manually examined whether media outlets or social media platforms were used as sources for the shared news.