Introduction of the Research

The tone and framing of the LGBTQ+ community in Turkish media were analyzed in this research. News articles, which serve as primary sources of information about current events and, therefore, most people trust, have the power to influence the conceptions, beliefs, and behaviors of individuals. Thus, this research provides insights into how the Turkish media, i.e., news articles, represent the LGBTQ+ community. It creates a foundation for researchers to analyze whether media influences Turkish people in terms of how they view LGBTQ+ individuals. LGBTQ+ individuals in Turkey experience societal and economic pressures as they are often viewed as unnecessary identities and marginalized within the community. Mainstream media contributes significantly to this perception, as it often denies them the platform to represent themselves, defend their rights, and expose them to hate speech. In response, many LGBT individuals turn to alternative media to advocate for their rights, express themselves openly, and address the challenges they face.

Methodology of the Research

The Turkish news articles dataset, which was obtained from Kaggle and included 42 thousand unlabeled news articles, was used for this research. Through AntConc, articles that contained keywords related to the LGBTQ+ community were extracted. The chosen keywords were as follows:

Then, sentiment analysis was executed using Python. As the news articles dataset did not have labels, sentiment analysis models based on BERTurk for Turkish and XLM-RoBERTa, found on Hugging Face, were utilized. The sentences were labeled and scored as negative, positive, and neutral through XLM-RoBERTa; and negative and positive through BERTurk models. The datasets were visualized using R through the ggplot2 package.

Datasets

After executing sentiment analysis using the aforementioned models, two datasets were created for the visualization step. Both datasets had ‘File Number’, ‘Text’, ‘Sentiment Label’, and ‘Sentiment Score’ columns. The first dataset (the first five rows will be displayed), whose sentiment analysis was based on the XLM-RoBERTa model, is as follows:

The second dataset (the first five rows will be displayed), whose sentiment analysis was based on the BERTurk model, is as follows:

Findings

According to the BERTurk model:

On the other hand, according to the XLM-RoBERTa model:

The following excerpt was classified as ‘negative’ by both models. BERTurk scored the sentence ‘0.868’ and XLM-RoBERTa scored it ‘0.903’.

The following excerpt was classified as ‘positive’ by both models. BERTurk scored the sentence ‘0.954’ and XLM-RoBERTa scored it ‘0.674’.

Jitter Plots

Through the use of jitter plots, the aim was to display the distribution of sentiment scores and sentiment labels within the datasets. Hence, the sentiment scores were represented on the x-axis, while the y-axis depicted the corresponding sentiment labels. The first jitter plot is as follows:

The second jitter plot, which was based on the BERTurk model and whose sentiment scores and labels were represented the same way as the XLM-RoBERTa model, is as follows:

Violin Plots

Violin plots, which are combinations of box plots and kernel density plots, allow us to see how dense the distribution of sentiment scores is in this context. The overall shapes of the violin plots below represent the distributions.

XLM-RoBERTa model:

BERTurk model:

Bar Plots

To display the distribution of the counts, bar plots were utilized. Bar plots allow us to see and analyze how many negative, positive, or neutral classified sentences there are more efficiently

XLM-RoBERTa Model:

BERTurk model:

Literature Review

Turkey supports heteronormativity while marginalizing same-sex sexualities and gender-nonconforming identities (Atalay & Doan, 2019). The influence of the AKP’s (Justice and Development Party) rhetoric has pushed the country towards conservatism, religiosity, and more societal oppression. Ozbay (2015) states that homophobia has been widespread, with specific sexualities considered ‘deviations’ and ‘illnesses’ by public authorities and military organizations, as Selma Aliye Kavaf, Turkish Minister of State responsible for Women and Family Affairs, stated in 2010 that homosexuality was a ‘biological disorder’, an ‘illness’ that should be treated (Amnesty International, 2011, p. 5). After the coup in 1980 and through the 1990s, especially trans individuals were represented within a sexist and homophobic context in the private media channels. Through broadcasting, this period witnessed the cultivation of a national “fear of the queer” ideology (Gurel, 2017). Regardless of the growing visibility of the LGBT community, the depiction was negative, characterizing its members as sinful individuals, outcasts, and even as monsters (Atalay & Doan, 2019).

Conclusion

According to the findings of this study, the tone and framing of the LGBTQ+ community in Turkish media are mainly neutral and negative. After analyzing the results of the sentiment analyses of both models, the XLM-RoBERTa model has displayed a better classification than BERTurk. Even though BERTurk classified more sentences as positive, sentiment scores indicated that the model was not confident enough, meaning that if there was a neutral classification, most of the sentences would be classified as neutral. Hence, it was concluded that the Turkish media avoids describing the LGBTQ+ community positively; instead, words that have neutral or negative connotations are preferred. As a result, it is very likely for media organizations to influence people’s opinions, beliefs, and conceptions towards the LGBTQ+ community negatively, meaning that they do not attempt to change the negativity towards the community and keep making their lives harder. Further sentiment analysis with larger datasets that contain more news articles related to the LGBTQ+ community in Turkey should be conducted. Additionally, various sentiment analysis models should be utilized and the results should be compared to reach a better result.

References