Gender and Communication: Analyzing Tweet Length, Sentiment, and Lexical Patterns on X (Twitter)

Annisa Romadloni, Laura Sari

Abstract


This study explores gendered communication patterns on X by examining tweet length, sentiment expression, and lexical choices in 20.050 tweets across 26 variables. Through sentiment analysis using the Bing Lexicon and word frequency analysis, the research investigates how male and female users differ in their digital communication styles. The study also incorporates non-parametric statistical tests, such as the Mann-Whitney U and Wilcoxon rank sum tests, to assess significant differences in tweet length and sentiment scores between genders. Results show that women tend to write shorter, more positive tweets, often reflecting a more personal and relational communication style. In contrast, men’s tweets are generally longer, incorporating more action-oriented language and a broader range of topics. While sentiment analysis revealed a trend of more positive tweets from women, the lack of statistical significance in sentiment differences highlights the complex nature of gendered expression in digital spaces. This research contributes to the understanding of gendered communication on social media and suggests the need for future studies to examine the intersectionality of gender with other social factors.


Keywords


Sentiment Analysis; Gendered Language; Social Media; Gender

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References


A. Reigstad. (2020). Gender Differences in Communication Styles and their Influence on Workplace Communication and the Practice of Public Relations in the United States Gender Differences in Communication Styles and their Influence on Workplace Communication and the Practice of Public Relations in the United States. https://doi.org/10.13140/RG.2.2.30156.28803

Al-Ghalibi, M., Al-Azzawi, A., & Lawonn, K. (2019). NLP based sentiment analysis for Twitter’s opinion mining and visualization. https://doi.org/10.1117/12.2522679

Ang, C. S. (2017). Internet habit strength and online communication: Exploring gender differences. Computers in Human Behavior, 66. https://doi.org/10.1016/j.chb.2016.09.028

Angelakis, A., Inwinkl, P., Berndt, A., Ozturkcan, S., Zelenajova, A., & Rozkopal, V. (2024). Gender differences in leaders’ crisis communication: a sentiment-based analysis of German higher education leaderships’ online posts. Studies in Higher Education, 49(4). https://doi.org/10.1080/03075079.2023.2246505

Bozkurt, M., & Üniversitesi, S. (2021). Online learning communities in covid-19 days: mining twitter data. International Technology and Education Journal, 5(2).

Ceia, V., Mota, T., & Lewis, R. (2022). Style and Rhetoric of Spanish Politics on Twitter. Digital Studies/ Le Champ Numerique, 12(1). https://doi.org/10.16995/dscn.8097

Chai, C., Wu, X., Shen, D., Li, D., & Zhang, K. (2016). Gender differences in the effect of communication on college students’ online decisions. Computers in Human Behavior, 65. https://doi.org/10.1016/j.chb.2016.07.012

Chen, Y., Yang, X., Howman, H., & Filik, R. (2024). Individual differences in emoji comprehension: Gender, age, and culture. PLoS ONE, 19(2 February). https://doi.org/10.1371/journal.pone.0297379

Garcia-Rudolph, A., Laxe, S., Saurí, J., & Guitart, M. B. (2019). Stroke survivors on Twitter: Sentiment and topic analysis from a gender perspective. Journal of Medical Internet Research, 21(8). https://doi.org/10.2196/14077

Haferkamp, N., Eimler, S. C., Papadakis, A. M., & Kruck, J. V. (2012). Men are from mars, women are from venus? Examining gender differences in self-presentation on social networking sites. Cyberpsychology, Behavior, and Social Networking, 15(2). https://doi.org/10.1089/cyber.2011.0151

Himasree, A., Kumar, K. S., & Meenakshi, M. (2023). Automatic Emoji Generation using Inception V3. Proceedings of the 2nd International Conference on Edge Computing and Applications, ICECAA 2023. https://doi.org/10.1109/ICECAA58104.2023.10212164

Hu, L. (2024). Mobilization, self-expression or argument? A computational method for identifying language styles in political discussion on Twitter. Online Information Review, 48(4). https://doi.org/10.1108/OIR-10-2022-0545

Jackson, L. A., Ervin, K. S., Gardner, P. D., & Schmitt, N. (2001). Gender and the internet: Women communicating and men searching. Sex Roles, 44(5–6). https://doi.org/10.1023/A:1010937901821

Jones, J. S., Tapp, S. R., Murray, S. R., Palumbo, R. J., Strange, A. T., & Ritschel-Trifilo, P. (2018). Gender Differences in Online Communication and the Impact of Faculty Gender. Academy of Business Research Journal, 1.

Merchant, K. (2012). How men and women differ: Gender differences in communication styles, influence tactics, and leadership styles. CMC Senior Theses.

Nachar, N. (2008). The Mann-Whitney U: A Test for Assessing Whether Two Independent Samples Come from the Same Distribution. Tutorials in Quantitative Methods for Psychology, 4(1). https://doi.org/10.20982/tqmp.04.1.p013

Noguti, V., Singh, S., & Waller, D. S. (2018). Gender differences in motivations to use social networking sites. In Social Media Marketing: Breakthroughs in Research and Practice. https://doi.org/10.4018/978-1-5225-5637-4.ch034

Omnicore Agency. (2022). Twitter by the Numbers: Stats, Demographics & Fun Facts. Omnicore Agency, 100.

Omuya, E. O., Okeyo, G., & Kimwele, M. (2023). Sentiment analysis on social media tweets using dimensionality reduction and natural language processing. Engineering Reports, 5(3). https://doi.org/10.1002/eng2.12579

Park, C. W., & Seo, D. R. (2018). Sentiment analysis of Twitter corpus related to artificial intelligence assistants. 2018 5th International Conference on Industrial Engineering and Applications, ICIEA 2018. https://doi.org/10.1109/IEA.2018.8387151

Putra, I. G. P. D. D. (2022). Gender Communication Difference Between Students in Online Learning. The Art of Teaching English as a Foreign Language, 3(1). https://doi.org/10.36663/tatefl.v3i1.143

Rajput, N. K., Grover, B. A., Rathi, V. K., & Bansal, R. (2020). Word frequency and sentiment analysis of twitter messages during Coronavirus pandemic. http://arxiv.org/abs/2004.03925

Rodríguez-Ibánez, M., Casánez-Ventura, A., Castejón-Mateos, F., & Cuenca-Jiménez, P. M. (2023). A review on sentiment analysis from social media platforms. In Expert Systems with Applications (Vol. 223). https://doi.org/10.1016/j.eswa.2023.119862

Samuel, J., Rahman, M. M., Ali, G. G. M. N., Samuel, Y., Pelaez, A., Chong, P. H. J., & Yakubov, M. (2020). Feeling Positive about Reopening? New Normal Scenarios from COVID-19 US Reopen Sentiment Analytics. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2020.3013933

Shields, S. A. (2013). Gender and Emotion: What We Think We Know, What We Need to Know, and Why It Matters. Psychology of Women Quarterly, 37(4). https://doi.org/10.1177/0361684313502312

Stone, J. A., & Can, S. H. (2021). Gendered language differences in public communication? The case of municipal tweets. International Journal of Information Management Data Insights, 1(2). https://doi.org/10.1016/j.jjimei.2021.100034

Walther, J. B. (2023). Online hate: A prosocial explanation of antisocial behavior and affordances of social media. In Emotions in the Digital World: Exploring Affective Experience and Expression in Online Interactions. https://doi.org/10.1093/oso/9780197520536.003.0015

Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7). https://doi.org/10.1007/s10462-022-10144-1

Wong, Y. J., & Rochlen, A. B. (2008). Re-envisioning men’s emotional lives: Stereotypes, struggles, and strengths. In Positive psychology: Exploring the best in people, Vol 2: Capitalizing on emotional experiences.




DOI: https://doi.org/10.31004/jele.v10i4.883

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