Sentiment and sarcasm: Analyzing gender bias in sports through social media with deep learning (2024)

Abstract: Gender bias continues to be a pervasive issue, especially in public discourse surrounding high-profile events like the Olympics. Social media platforms, particularly Twitter, have become a central space for discussing such biases, making it crucial to analyze these conversations to better understand public attitudes. Sentiment analysis plays a key role in this effort by determining how people feel about gender bias. However, sarcasm often complicates sentiment analysis by distorting the true sentiment of a tweet, as sarcastic expressions can mask negative or positive sentiments. To address this, the study introduces a novel framework called SENSA (SENtiment and Sarcasm Analysis), designed to detect both sentiment and sarcasm in tweets related to gender bias. The framework leverages the R2B-CNN model for robust sarcasm and sentiment classification. Using approximately 5,000 tweets related to gender bias from 2010 to August 30, 2024, SENSA applies advanced sarcasm detection to account for shifts in sentiment caused by sarcastic remarks. The R2B-CNN model demonstrates a high accuracy of 92.32% along with achieving 92.75% precision and 92.53% F1-score for sarcasm detection and a 93.67% accuracy, 92.33% precision and 92.33% F1-score for sentiment classification. SENSA provides a comprehensive understanding of gender bias discussions on social media by capturing both sentiment and sarcasm to reveal deeper insights into public perceptions.

## introduction gender bias refers to the unequal treatment or perception of individuals based on their biological gender, often resulting in unfair advantages or disadvantages @xcite . people encounter this bias in various aspects of daily life, from being stereotyped to facing real inequality and discrimination. for example, nursing is often viewed as a female-dominated field, and men who pursue this profession may be labeled as "not man enough". conversely, mechanical engineering is typically male-dominated, and women in the field may face negative stereotypes @xcite . such biases can manifest in trivial comments like calling someone a "girl" for not performing well in sports or in more serious issues such as wage inequality. the effects of gender bias can be deeply personal, impacting individuals' self-worth and opportunities. it can result in people being unable to advance in their careers or have their achievements ignored simply because of their gender @xcite . this bias extends beyond just male and female, transgender and non-binary individuals also face these challenges. the consequences of gender bias are damaging to society as it fosters an environment of inequality and inferiority, leading to lost opportunities and creating unnecessary divisions. social media platforms, especially twitter, amplify these issues, providing a space for people to express their thoughts, often anonymously @xcite . gender bias discussions often come to light on social media because people feel more comfortable addressing such sensitive issues behind a screen. this makes social media an essential platform for gathering data on gender bias, as users frequently express opinions and frustrations about these topics. sarcasm also plays a significant role in online discourse about gender bias. sarcasm, where the literal meaning of words contrasts with the intended message, often complicates the sentiment of a statement @xcite . for example, a tweet like "oh great, another woman in engineering!" might seem neutral or positive, but the underlying sarcasm conveys negativity. twitter is rife with sarcastic remarks, especially around social issues like gender bias, making it an excellent platform for analyzing public sentiment. the 2024 paris olympics highlighted gender bi-ases, particularly around the misgendering of contestants, following the incident involving a boxer imane khalef (indiatoday, 2024), which sparked debates on social media. many users voiced differing opinions, with some supporting the athlete and others questioning her gender identity. this situation highlights the need to analyze how social media conversations reveal attitudes toward gender bias, motivating the study to explore these dynamics in depth. the objective of this study is to explore public sentiment and the role of sarcasm in these discussions, particularly in the context of high-profile events like the olympics. to address this, a novel framework, sensa (sentiment and sarcasm analysis), is proposed, incorporating the r2b-cnn model for sarcasm and sentiment analysis. this framework effectively analyzes tweets related to gender bias, focusing on sarcasm and sentiment to uncover underlying public attitudes. the analysis provides insights into how gender biases are discussed and perceived, contributing to a deeper understanding of public opinion and supporting efforts toward more inclusive and equal treatment across all genders. the contributions of this study are: the paper is structured as follows: section 2, reviews the literature, section 3 explains the methodology of this study, section 4 discusses the results, and section 5 concludes the study. ## literature survey in recent years, several studies have explored the analysis of gender bias and sentiment across various domains on social media platforms. the study @xcite examines interactions between indian politicians and journalists on twitter, with a focus on gender-based differences. using a gender dataset @xcite , different politician accounts are identified and cross-matched with the myneta platform. the interaction data is categorized based on gender combinations between politicians and journalists. findings indicated a significant gender bias, with male politicians receiving more mentions and their tweets gaining more popularity compared to female politicians. the study further noted that male politicians consistently garnered more attention in terms of follower count, although no significant emotional differences are found across gender categories through emotion analysis. in the study @xcite , they talk largely about gender bias in political interactions on digital platforms by considering how politicians present themselves on twitter and how they are approached by others. the study @xcite also revealed that gender stereotypes lead to changes in the assessment of politicians, their communication and party support. the authors analyzed gender differences in twitter communication with politicians, using sentiment analysis and topic modelling on the twitter data @xcite . a sentiment measure based on the lexicoder sentiment dictionary (lsd) shows how positive or negative words vary across tweets, while a second measure quantifies personal versus professional language using liwc dictionaries. results reveal that female politicians receive more personal and emotional tweets compared to male counterparts, especially in conservative parties. however, sentiment in politicians' tweets largely aligns with party identity rather than gender. structural topic modeling (stm) further confirms significant differences in the communication directed toward male and female politicians. in @xcite , authors analysed gender bias in english songs. the study exploits weat @xcite , sc-weat @xcite and sweat @xcite . authors in @xcite fine-tuned bert classifier on wasabi dataset @xcite for sexist content detection and also analyzed language bias using word embeddings. the study's results revealed significant gender-based biases in song lyrics. male solo artists are found to produce a higher proportion of sexist content compared to female solo artists or groups, with the bert classifier detecting sexist language patterns. the word embedding association tests (weat, sc-weat, and sweat) further confirmed that male artists' while these studies contribute to understanding gender bias in various contexts, a notable research gap exists in the analysis of gender bias in sports, specifically during major events like the olympics. sentiment analysis of tweets related to gender bias, particularly with sarcasm, has been largely overlooked. addressing this gap is crucial to avoid reinforcing harmful stereotypes and fostering a more gender-equal society. to meet this challenge, the novel sensa framework is introduced, integrating advanced sarcasm and sentiment analysis techniques to explore these discussions in depth, particularly in the context of high-profile sporting events. ## methodology the study proposes a novel sensa framework designed to analyze sentiment shifts concerning sarcasm in user tweets related to gender bias. this framework processes tweets by first normalizing them through preprocessing. after preprocessing, the tweets are fed into a sentiment model to determine the expressed sentiment. subsequently, the tweets are passed to a sarcasm detection model to assess the presence of sarcasm. if sarcasm is detected, the sentiment undergoes a shift; for instance, a positive sentiment is adjusted to negative, and a negative sentiment is transformed to positive. finally, the overall sentiment is analyzed after accounting for any shifts caused by sarcasm. the graphical abstract of this proposed framework is presented in figure 1 . ## result and analysis overall, the results highlight that the r2b-cnn model stands out in sarcasm detection, while the r2b-cnn excels in sentiment classification. this comparative analysis underscores the effectiveness of specialized models tailored for specific tasks, enhancing the understanding of public sentiment and sarcasm in discussions of gender bias in the context of high-profile events like the olympics. the roc vs precision-recall curve is presented in figure 3a and 3b . the collected tweets are tested with the proposed model r2b-cnn to see the sarcasm trends in the data. when analyzing the sarcasm trend figure 4 unveils a fascinating trend in sarcasm during the year 2024, particularly highlighting the surge in sarcastic comments as the 2024 paris olympics unfolded. notably, sarcasm reached its peak after july 2024, with august recording a staggering 43 sarcastic tweets. this trend reveals an intriguing phenomenon, as the olympic games progressed, users expressed more sarcastic sentiments, suggesting that the excitement and intensity of the event fueled a wave of irony and critique among spectators, especially towards the end of the games. to be exact, there are 630 sarcastic comments and 4352 non-sarcastic comments. some of the sample texts are classified as sarcastic and nonsarcastic as given in the proposed r2b-cnn sentiment model categorized a total of 651 tweets as positive, while around 1,210 as negative. the analysis also revealed that out of the collected dataset labelled positive or negative after sentiment analysis, 1,657 tweets are classified as non-sarcastic, whereas 204 are identified as sarcastic. this sharp contrast in sentiment is compelling, after accounting for sarcasm, the final counts showed 743 tweets classified as positive and 1,118 as negative. this shift underscores the profound impact of sarcasm on public sentiment on gender bias, particularly in events like the olympics. the graphical representation of this sentiment shift is presented in figure 5 . hence, the analyses of both sarcasm and sentiment shifts reveal a prevailing negative sentiment among users concerning gender bias in the olympics. even when accounting for instances where positive sentiments are initially misclassified as negative due to the sarcastic nature of certain statements, it is evident that the overall sentiment ## conclusion gender bias continues to be a pressing issue in contemporary society, particularly, it disturbs the ecosystem in sports events. the primary objective of this study is to analyze public sentiment and sarcasm in tweets related to gender bias in a sports context. to accomplish this, the novel sensa framework integrates sentiment analysis with sarcasm detection, offering a comprehensive understanding of public discourse. the analysis is performed on 5,000 tweets dating from 2010 to august 30, 2024. the r2b-cnn model demonstrated strong performance in sarcasm detection, with a precision of 92.75%, recall of 92.31%, f1-score of 92.53%, and accuracy of 92.32%. for sentiment analysis, it achieved a precision of 92.33%, recall of 92.32%, f1-score of 92.33%, and accuracy of 93.67%. this showcases the model's effectiveness in accurately identifying sarcasm and adjusting sentiment classifications. the study revealed a prevailing negative sentiment surrounding gender bias discussions, indicating that the majority of users express concern regarding this issue, especially in relation to highprofile sporting events. this analysis on gender bias highlights the significance of understanding sarcasm in shaping public sentiment. future work can involve expanding the dataset to include a wider range of events and utilizing the sensa framework to explore other dimensions of public discourse, such as intersectionality and media representation, further enhancing the understanding of societal attitudes toward gender biases. additionally, incorporating real-time analysis could provide more immediate insights into evolving public sentiments.