An Empirical Study of Gendered Stereotypes in Emotional Attributes for Bangla in Multilingual Large Language Models (2024)

Abstract: The influence of Large Language Models (LLMs) is rapidly growing, automating more jobs over time. Assessing the fairness of LLMs is crucial due to their expanding impact. Studies reveal the reflection of societal norms and biases in LLMs, which creates a risk of propagating societal stereotypes in downstream tasks. Many studies on bias in LLMs focus on gender bias in various NLP applications. However, there’s a gap in research on bias in emotional attributes, despite the close societal link between emotion and gender. This gap is even larger for low-resource languages like Bangla. Historically, women are associated with emotions like empathy, fear, and guilt, while men are linked to anger, bravado, and authority. This pattern reflects societal norms in Bangla-speaking regions. We offer the first thorough investigation of gendered emotion attribution in Bangla for both closed and open source LLMs in this work. Our aim is to elucidate the intricate societal relationship between gender and emotion specifically within the context of Bangla. We have been successful in showing the existence of gender bias in the context of emotions in Bangla through analytical methods and also show how emotion attribution changes on the basis of gendered role selection in LLMs. All of our resources including code and data are made publicly available to support future research on Bangla NLP. 1 Warning: This paper contains explicit stereotypical statements that many may find offensive.

text: Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP), pages 384–398 August 16, 2024 ©2024 Association for Computational Linguistics
Warning: This paper contains explicit stereotypical statements that many may find offensive.

heading: 1 Introduction
text: Humans display a wide range of emotions in their daily lives, which are integral to human intelligence and closely linked to personality and character. Given the diversity of these emotional expressions, it is important to explore whether emotional patterns adhere to gender stereotypes. We define gendered emotional stereotypes as the generalization
1https://github.com/csebuetnlp/BanglaEmotionBias
of expected emotional responses based on a person’s gender in specific situations. Emotions significantly impact how individuals conceptualize themselves and respond to stimuli (Haslam et al., 2011), making bias in this context particularly harmful.
Historically, societal views toward women in Bangla-speaking regions have often been regressive and undervaluing (Jain et al., 2021). Evidence of discrimination in employment and opportunities (Tarannum, 2019) underscores prevalent harmful stereotypes. These stereotypes depict women as inherently vulnerable, overly emotional, and more suited to roles requiring empathy and care (Plant et al., 2000). Conversely, men are perceived as aggressive, resilient, and less capable of handling tasks that necessitate emotional sensitivity and compassion. Such deeply ingrained stereotypes risk being perpetuated by Large Language Models (LLMs). Therefore, it is essential to examine these effects given the growing use of LLMs.
Recent works have shown that persona-based prompting can be utilized to reveal stereotypes in LLMs (Gupta et al., 2024; Deshpande et al., 2023). We utilize the persona presuming capabilities of LLMs to attribute emotions to gendered personas in a specific scenario in order to evaluate the presence of gender stereotypes. To be specific, the model would be assigned a persona and given a scenario to reply with an emotion attribute. In a bias free setup, we would expect the emotions to be uniformly distributed irrespective of gender.
Our contributions in this paper include, (1) the first study that examines gender bias and stereotypes in emotion attribution in state-of-the-art LLMs for Bangla language, (2) a quantitative analysis of around 73K LLM generated responses for over 6K online comments collection for Bangla that covers both male and female personas, and (3) a qualitative analysis of the generated responses and resulting nuances due to instruction variability. Our study suggests the presence of gender stereo-
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types in model responses that could cause harm to a certain demographic group in emotion related NLP tasks.

heading: 2 Bias Statement
text: Various definitions of bias exist across research, as detailed in (Blodgett et al., 2020). In this work, we focus on stereotypical associations between masculine and feminine gender and emotional attributes in LLM responses. If a system consistently associates specific emotions with particular genders, it perpetuates harmful stereotypes, such as women being perceived as experiencing more guilt, shame, or fear, or men as experiencing more anger or pride. This representation poses risk of discrimination on the basis of gender and put obstruction on the natural expression of emotions. Our study aims to illuminate gender-emotion correlations in LLM responses for Bangla language.

heading: 3 Related Work
text: Since historical times, the relationship between gender and emotions has endured across linguistic and geographic barriers, deeply ingrained in society perceptions. Numerous academic studies have investigated the historical foundations of gendered emotional stereotypes, demonstrating their persistent existence across diverse historical periods and cultural contexts (e.g., Butler (1999); Fischer and Manstead (2000)).
Gender bias in language models has been extensively explored, initially focusing on static embeddings (e.g. Bolukbasi et al. (2016), Caliskan et al. (2017)) before shifting to contextual word embeddings (e.g. May et al. (2019); Guo and Caliskan (2021)) with the rise of transformer-based language models. These works provide the baseline results and introduce popular bias measuring techniques. The work of Kurita et al. (2019) stands out as one of the first to consider model response analysis for bias measurement. Efforts to measure gender stereotypes in Natural Language Generation tasks yield notable results as well (Sheng et al., 2019; Huang et al., 2021; Lucy and Bamman, 2021). Benchmarks such as WinoBias (Zhao et al., 2018) and Winogender (Rudinger et al., 2018) have been used to measure gender biases in LMs.
Studies on gender bias and stereotypes in LLMs were studied in detail in Kotek et al. (2023); Dong et al. (2024); Zhao et al. (2024). The techniques used here mainly comprise of template-based prob-
ing and token prediction based analysis. Similar efforts along with de-biasing techniques were discussed in Ranaldi et al. (2023); Gallegos et al. (2024). Notably, del Arco et al. (2024) provides compelling evidence supporting the presence of gendered emotions in LLMs.
The research in Bangla NLP had gained pace over the past years. Works of Bhattacharjee et al. (2022); Hasan et al. (2021, 2020); Akil et al. (2022) has contributed to the development of Bangla in the domains of Natural Language Understanding and dataset enrichment. The first notable work on gender stereotypes in Bangla is done by Sadhu et al. (2024), where they provide baselines for different bias measurement techniques. Early research on emotional attributes in Bangla primarily involved creating emotion datasets and multi-label classification tasks. Examples of such works are Irtiza Tripto and Eunus Ali (2018); Das et al. (2021); Islam et al. (2022). We provide the first study to evaluate gender bias regarding emotional attributes in multilingual LLMs for Bangla.

heading: 4 Data
text: We use the annotated dataset from (Islam et al., 2022). It is a public dataset containing public comments from social media sites covering 12 different domains such as Personal, Politics and Health, labeled for 6 fine-grained emotion categories of the Junto Emotion Wheel (Love, Fear, Anger, Sadness, Surprise, Joy) (see appendix A). We refine the data for our use such that we extract examples that have the two following properties: 1. Expresses an event or statement or description 2. Does not include any statements or examples explicitly mentioning any emotions
For the first point, we eliminated the comments which are very short and have no semantic values (like "ok", "fine" etc.). For the second case, we
eliminated comments that boldly express an emotion (like "I am happy"). In the final dataset we have 6134 examples that we used in LLM prompting. Details about the dataset pre-processing are discussed in Appendix B. The emotion categories and their frequencies are shown in Table 1.

heading: 5 Experimental Setup
text: Our experiment focuses on exploring the capacities of Large Language Models (LLMs) in emotion attribution tasks. In this task, the objective is to identify the primary emotion of a given comment in relation to a specified persona. We adopt a Zero-shot Learning (ZSL) approach for our model setup, meaning no training examples are provided beforehand. This decision aims to prevent any pre-existing bias from influencing the model’s judgments. Through ZSL, we investigate whether LLMs demonstrate gendered emotional stereotypes.

heading: 5.1 Models
text: For our experiment, we provide results for three state-of-the-art LLMs: Llama3 (version: MetaLlama-3-8B-Instruct 2) (AI@Meta, 2024), GPT3.5-Turbo 3 and GPT-4o 4. Since Bangla is a low resource language, not many models could generate the expected response we required. For our experimentation, we tried a few other models as well. They are Mistral-7b-Instruct 5 (Jiang et al., 2023), Llama-2-7b-chat-hf 6 (Touvron et al., 2023) and OdiaGenAI-BanglaLlama 7 (Parida et al., 2023). However, none could produce any presentable result serving our purpose. For instance, some of these models generated repetitive phrases as responses for many different prompts. In some cases,
2meta-llama/Meta-Llama-3-8B-Instruct 3gpt-3-5-turbo 4gpt-4o 5mistralai/Mistral-7B-Instruct-v0.2 6meta-llama/Llama-2-7b-chat-hf 7OdiaGenAI/odiagenAI-bengali-base-model-v1
these LLMs produced responses that were irrelevant to the query. For example, when asked about emotions, the models would sometimes respond repetitively with statements about how it could assist the user. Additionally, regardless of the actual emotional content of the data entries, some models consistently returned the same emotion in most of their responses. Another issue we observed was the model’s tendency to repeat the input query verbatim.

heading: 5.2 Prompting
text: Assigning Persona: We begin by assigning a persona to an LLM as a task prompt. The rationale for employing persona-based prompts to explore gendered stereotypes in emotional experiences aligns with the framework proposed by Gupta et al. (2024). Utilizing two distinct instruction templates, as depicted in Figure 1, each model receives four prompts for every comment (two personas times two templates). As this is the first work of such kind in Bangla, we focus our investigation solely to the most prevalent binary genders: male and female.
Instruction Templates: The two instruction templates illustrated in Figure 1 differ in one aspect: in I1, we impose constraints on the emotional attribute outputs expected from the model, while I2 does not have such constraints. In I1, we direct the model to produce outputs among eight emotions, encompassing the six emotions delineated by Ekman (1992), along with GUILT and SHAME as additional categories, aimed at achieving a more nuanced classification. Conversely, in I2, we allow the model unrestricted freedom in generating responses, enabling us to observe the full spectrum of attributes it may produce. This setup is designed to explore the model’s inherent capabilities and discern the range of options it assigns autonomously.
Prompt Example: We provide the prompt template along with a sample that we used for model inference in Figure 2. As previously mentioned,
we prompt each model four times per data example. To create a prompt, we take one persona and one template from the instruction templates and add a single data instance from the dataset inside the input.

heading: 5.3 Evaluation Setup
text: Each of the 6,134 comments in our dataset prompts all three models four times in a Zero-Shot Learning (ZSL) setup, resulting in a dataset of 73,608 (6,134 comments × 2 persona × 2 templates × 3 LLM) emotion attributes (36,804 data per gender category). To reduce randomness, we set the temperature very low and restrict the maximum re-
sponse length to 128. It is important to note that all the responses were not single word and we could see some grammatical variations. Even there were some responses that does not exist in the Bangla vocabulary. Therefore, we employed various techniques including human reviewing, string matching and LLM prompting for response modification. We provide statistics for response data and examples of the filtering process along with the techniques implemented for post-processing in the Appendix C. After filtering, we are left with 72,936 responses in total (Table 3 of Appendix C).

heading: 6 Results and Evaluation

heading: 6.1 Analysis of Emotion Attribution Across Genders
text: The results of the LLMs are aggregated based on the frequency of the eight most common emotion attributes, as illustrated in Figure 3. Notable contrasts in the distribution of certain attributes are evident.
Prompt Template I1: Although the choices for the LLMs were constrained in this template, the models still produced results outside the designated attributes. For example, although GUILT was included in the instruction template, but we see PRIDE in the top eight attributes along with other emotions in the template. The attributes of SADNESS and SHAME are significantly more frequently associated with women (4,086 instances and 1,685 instances) compared to men (2,346 instances and 730 instances); reflecting a prevalent stereotype regarding female emotional expression. Conversely, men are more frequently attributed with emotions such as SURPRISE (3,881 instances compared to 2,108 for women), ANGER (862 instances compared to 273 for women), PRIDE (257 instances compared to 162 for women), and FEAR (840 instances compared to 545 for women). However, the emotion DISGUST is almost equivalently attributed to both women and men (5,395 times vs 5,252 times).
Prompt Template I2: Here we see some notable shifts in the distribution of some attributes, compared to template I1. Particularly significant, SURPRISE is attributed to women 2,803 times compared to 2,300 times for men, which is a stark contrast to the distribution observed in template I1.
Similar stereotypical patterns persist for ANGER (1,516 instances for men compared to 1,057 for women), DISGUST (1,163 instances for men compared to 816 for women), and PRIDE (707 instances for men compared to 550 for women). The attribute of SADNESS remains predominantly associated with women (1,426 instances compared to 1,307 for men). Interestingly, in this template, FEAR is attributed to women more frequently than men (460 instances compared to 120 for men). In addition, both genders are almost equally attributed to ENTHUSIASM.
Furthermore, the emotion JOY is attributed almost equally to both men and women across both templates. Statistical significance of the results was established using a p-test, confirming significance
at a margin of p < 0.05 (see Appendix D). Key Take-away: The models attributed submissive emotions such as SHAME, SADNESS and FEAR to women and authoritative emotions ANGER and PRIDE to men representing gender-based emotional conditioning.

heading: 6.2 Unique Emotional Attributions to Gender
text: Table 2 presents the unique emotional responses generated by LLMs for male and female personas. The specific emotions attributed to each gender are significant as they shape and reinforce genderspecific characteristics and stereotypes. For instance, emotions such as Anger, Frustration and Disappointment highlight one’s agency, independence and self-worth and also suggest an association with aggression and dominance (Cherry and Flanagan, 2017). On the other hand, attributions of emotions such as Fear, Sadness and Hurt suggest vulnerability and sensitivity (Gotlib, 2017). These patterns reflect and perpetuate societal stereotypes about gender roles and emotional expression.
In Table 2, we notice emotions such as revengeful, furious, disbelief , excitement, restlessness and resistant are uniquely attributed to men, reflecting on the angry men stereotype and suggest dominance or aggression. Conversely, emotions such as hurt, anxious, unrest, adversity, shyness, desperation and intolerant are uniquely attributed to women, aligning with the stereotype of women as sad and helpless.
To further analyze these biases, we plotted the GloVe embeddings of these gender-specific unique words. The result, presented in Appendix E, show that words attributed to men and women form distinct semantic clusters. This clustering suggests that LLMs encode and propagate gender biases in their internal representations.
Key Take-away: LLMs exhibit distinct emotional attributions to gender personas, reinforcing gender-specific stereotypes by associating men with dominance and aggression and women with vulnerability and sensitivity.

heading: 6.3 Shift in Emotion Attribution
text: We examined the differences in emotion attributions between men and women to identify noticeable patterns. Specifically, we address the question: "What are the most frequent words attributed to the other gender in cases where certain words are most frequently produced for one gender?". We perform a quantitative analysis with the top emo-
tion words for each gender in each model (Prompt Template I2) and report the four most frequent emotion words for the opposite gender. We focus on the qualitative analysis in this section and provide detailed results in Appendix F. From Figure 3, we picked the three most contrasting emotion words for each gender and illustrated the shift in emotion words corresponding to each gender in Figure 4.
Our findings show that while some patterns are not always conclusive, certain trends are evident. For instance, in Figure 4, Surprise is predominantly attributed to women when Anger is attributed to men. Specifically, for the prompts that LLMs assign Anger to male, 39.16% of the times same response is given to female personas and Surprise is attributed 27.43% of the times (calculated from Table 6). According to the Junto Emotion Wheel (Appendix A), Anger and Surprise are emotionally distant. Similarly, when Disgust is attributed to male, we calculate that female personas get the same response for 40.76% of the times, whereas Sadness and Surprise for 6.88% and 9.54% of the times respectively. Likewise, for female responses labeled as Sadness, the predominant male response is Disgust. When the prompt elicits Sadness in women, the same prompt elicits Sadness 62.9% of the time in men and Disgust 5.98% of the time. Disgust denotes a spiteful reaction, while Sadness conveys submissiveness (Gotlib, 2017).
Additionally, we observed several instances where the responses are similar across genders. For example, the top responses for men are Pride, Enthusiam, and Satisfaction when the response is Joy for women (aggregated result calculated from Table 6). These three emotions are higher-
level derivatives of Joy on the Junto Emotion Wheel. We suggest that a more in-depth qualitative research approach could further explore these findings, which we leave for future research.

heading: 7 Conclusion
text: In this study, we examined gender stereotypes in emotion attributes across three state-of-the-art multilingual LLMs (both open and closed source), which is the first study of this kind for the Bangla language. Our analysis was conducted on a dataset of over 6,000 online comments, generating completions for male and female personas without losing generality of the research topic. Our quantitative analysis reveals that the models consistently exhibit gendered emotion attributions. A subsequent qualitative analysis suggests these variations are influenced by prevalent gender stereotypes, aligning with findings from psychology and gender studies on gender-based emotional stereotypes.
These findings raise concerns about the direct application of LLMs in emotion-related NLP tasks, especially considering their potential to reinforce harmful stereotypes. Additionally, it is important to note that the models used in this study were not fine-tuned for Bangla-specific tasks, particularly the open-source model. Therefore, it is crucial to implement de-biasing measures during the finetuning process for Bangla language tasks.
We advocate for further research in this area, specifically focused on the Bangla language, and the development of frameworks for bias benchmarking to ensure more equitable and accurate NLP applications.
Limitations
Our study utilized the closed-source models GPT3.5 Turbo and GPT-4o, which presents reproducibility challenges. Closed models can be updated at any time, potentially altering responses irrespective of temperature or top-p settings. In addition, we attempted to conduct experiments using other state-of-the-art models and models fine-tuned for the Bangla language. However, these efforts were hindered by frequent hallucinations and an inability to produce coherent and presentable results. This issue highlights a broader challenge: the current limitations of LLMs in processing Bangla, a low-resource language. The insufficient linguistic capabilities of these models for Bangla reflect a need for more focused development and training on Bangla-specific datasets.
We also acknowledge that our results may vary with different prompt templates and datasets, constraining the generalizability of our findings. Stereotypes are likely to differ based on the context of the input and instructions. Despite these limitations, we believe our study provides essential groundwork for further exploration of gender bias and social stereotypes in the Bangla language.
Ethical Considerations
Our study focuses on binary gender due to data constraints and existing literature frameworks. We acknowledge the existence of non-binary identities and recommend future research to explore these dimensions for a more inclusive analysis.
We acknowledge the inclusion of comments in our dataset that many may find offensive. Since these data are all produced from social media comments, we did not exclude them to reflect realworld social media interactions accurately. This approach ensures our findings are realistic and relevant, highlighting the need for LLMs to effectively handle harmful content. Addressing such language is crucial for developing AI that promotes safer and more respectful online environments.

heading: Acknowledgements
text: We would like to thank Abhik Bhattacharjee for his guidance and valuable insights to this study.

heading: A Junto Wheel of Emotion
text: The Junto Emotion Wheel is a tool designed to help people understand and articulate their emotions by categorizing them into layers of increasing specificity. The innermost layer features broad emotions like Joy, Sadness, Love, Surprise, Anger, and Fear. Moving outward, these are broken down into more specific emotions, such as from Anger to Exasperated to Frustrated. We present the emotion wheel in Figure 5.
This tool highlights the interconnectedness of emotions, showing how they can blend and influence each other. It’s widely used in psychology, counseling, education, and AI to improve emotional literacy and enhance emotion recognition systems.

heading: B Dataset Pre-Processing
text: The dataset processing pipeline involved several important steps to prepare the dataset for our use.
We began by combining the three separate datasets in Islam et al. (2022) — test, train, and validation — into a single, unified dataset of around 22K data points. Next, we applied a length-based filter to discard texts that were either too short or too long (word length 8 to 18) to maintain a balance of concise yet informative data entries. We discard the entries that contain explicit mention of emotion through string matching from a emotion list we gathered for Bangla. We performed a final cleaning step by trimming white-spaces and removing
duplicate entries. Finally we shuffled the dataset to randomize order, ensuring unbiased analysis.

heading: C Generated Data Modification
text: We provide a statistics on the number of data generated for different LLMs in different system instruction settings in Table 3. In the table, we show the number of raw responses and the final dataset we obtain after the data cleaning and modification.
Table 4 details the major modifications made to the responses and the rationale behind them. We employed various techniques for data post processing utlizing both human annotators and LLMs.
We extracted only the core emotion words from longer phrases generated by the LLMs, using String Matching Technique. This method involved scanning the responses for keywords associated with specific emotions. By identifying these keywords as well as discarding formal or filler language (e.g., "the answer to your question is _"), we were able to extract the main emotion conveyed by the response. We also excluded responses lacking emotion-related words or those not present in the Bangla vocabulary to ensure relevance.
Furthermore, we implemented Root Word Finding / Stemming to account for variations in word forms due to suffixes or other morphological changes. This adjustment allowed us to reduce words to their base or root form, ensuring that different variations of a word (e.g., "happiness" and "happy") were recognized as the same emotion. Additionally, we converted verbs to their nominal forms where necessary to maintain consistency in emotional attribution. Punctuation marks and emojis were removed to standardize the responses across the dataset.
For sentences that did not explicitly mention an emotion word but implicitly expressed an emotion, we utilized ChatGPT-3.5-Turbo to generate the core emotion. We provided a prompt designed to elicit the main emotion conveyed by a sentence. In response, ChatGPT-3.5-Turbo identified the primary emotion, analyzing the context and underlying sentiment. We also corrected spelling errors for words that closely resembled Bangla words and made grammatical adjustments when emotions were implicitly expressed to ensure the uniformity and accuracy of the dataset.
Examples of these modifications are presented in Table 4. To avoid confirmation bias, when rejecting a single gender response, we also rejected the
corresponding response from the other gender.

heading: D Statistical Significance of Generated Data
text: The LLM responses that we base our study on are based on two different system prompt instruction
settings. Our claim of the existence of gender bias in the response depends if the difference in the emotion counts for men and women are statistically significant. Thus we provide a χ2 test on the generated emotion frequencies for categories Man and Woman. We present our results in table 5.

heading: E Semantic Clustering of Gender-Specific Emotion Words
text: To further analyze the gender biases observed in the main study, we plotted the GloVe embeddings of the unique emotion words attributed specifically to men and women. We created the GloVe embed-
dings using the dataset of Bangla2B+ used to train BanglaBERT (Bhattacharjee et al., 2022). These embeddings were visualized using t-SNE, a technique for dimensionality reduction that helps to illustrate the semantic relationships between words.
The resulting scatter plot, shown in Figure 6, reveals distinct clusters for the words attributed to
men and women. We provide a convex hull bound for the observable clusters. This separation suggests that the language models (LLMs) encode and propagate gender-specific biases in their internal semantic representations.

heading: F Emotion Shift Per Gender Data Statistics for Prompt Template I2
text: This section presents a quantitative analysis of the shift in emotional responses generated by LLMs when the assigned persona is changed. We focus on the system instruction template I2, as illustrated in Table 5, to highlight the shifts in gender-specific responses. The table lists the top emotion word occurrences (with English translations) for one gender and the percentage of cases where the same response is generated for the opposite gender using the same data points. Additionally, we include the top responses for the opposite gender, their corresponding occurrences (in brackets), and English
translations, listed sequentially on the next line. For instance, in the case of GPT-4o, the emotion joy appears 1966 times for the male persona responses (table 6a). Among these 1966 instances, 1624 (82.6%) also generated the same response for the female persona. Furthermore, the top responses generated for the female persona for the same inputs were Surprise (64), Insult (32), Melancholy (27), and Enthusiasm (24).
Template I2 Template I2 Model ChatGPT-4o Model ChatGPT-4o Response for Man Same response for Woman Top responses for Woman Response for Woman Same response for Man Top responses for Man Word # occurences # occurences percentage Word # occurences # occurences percentage আন 1966 1624 82.60% িব য়(64), অপমান(32), উদাস(27), উৎসাহ(24) আন 1752 1624 92.69% গব(45), িব য়(23), স ি (7), কৃত তা(7) (joy) Surprise, Insult, Melancholy, Enthusiasm (joy) Pride, Surprise, Satisfaction, Gratitude দঃুখ 787 551 64.08% িবষ তা(74), াভ(48), িবরি (35), হতাশা(23) দঃুখ 714 551 77.17% াভ(51), িবষ তা(36), অপমান(17), িবরি (16) (sadness) Depression, Agitation, Disgust, Disappointment (sadness) Agitation, Depression, Insult, Disgust াভ 590 463 78.47% দঃুখ(51), িবরি (25), অপমান(17), িব য়(10) াভ 622 463 74.44% দঃুখ(48), অপমান(42), িবরি (25), হতাশা(20) (agitation) Sadness, DIsgust, Insult, Surprise (agitation) Sadness, Insult, Disgust, DIsappointment িব য় 341 190 56.79% অপমান(33), আন (23), িবরি (17), অ ি (12) িব য় 405 190 46.91% আন (64), হািস(24), কৗতূহল(17), হতাশা(14) (surprise) Insult, Joy, Disgust, Discomfort (surprise) Joy, Laughter, Curiosity, DIsappointment হতাশা 316 218 68.99% াভ(20), িবরি (19), অপমান(15), িব য়(14) অপমান 399 202 50.63% িব য়(33), আন (32), হািস(24), িবরি (23) (disappointment) Agitation, Disgust, Insult, Surprise (insult) Surprise, Joy, Laughter, DIsgust গব 285 194 68.07% আন (45), অনেু রণা(5), অস ি (4), অব া(4) হতাশা 311 218 70.10% দঃুখ(23), হািস(14), িবরি (10), াভ(9) (pride) Joy, Insipiration, Surprise, Displeasure (disappointment) Sadness, Laughter, Disgust, Agitation অপমান 284 202 71.13% াভ(42), দঃুখ(17), িবরি (10), িব য়(6) িবষ তা 286 172 60.14% দঃুখ(83), িব য়(11), আন (5), হতাশা(4) (insult) Agitation, Sadness, Disgust, Surprise (depression) Sadness, Surprise, Joy, Disappointment িবষ তা 239 172 71.97% দঃুখ(36), িবরি (9), িব য়(7), আেবগ বণতা(5) িবরি 248 94 37.90% দঃুখ(36), াভ(25), হতাশা(19), িব য়(17) (depression) Sadness, DIsgust, Surprise, Passion (disgust) Sadness, Agitation, Disappointment, Surprise িবরি 200 94 47.00% াভ(25), অপমান(23), দঃুখ(16), িব য়(12) গব 207 194 93.72% আন (9), স ান(1), অপমান(1), িব য়(1) (disgust) Agitation, Insult, Sadness, Surprise (pride) Joy, Respect, Insult, Surprise হািস 173 80 46.25% িব য়(24), অপমান(24), হতাশা(14), িবরি (10) হািস 117 80 68.38% আন (22), হতাশা(4), িব য়(3), িব াি (3) (Laughter) Surprise, Insult, Disappointment, Disgust (Laughter) Joy, Disappointment, Surprise, Confusion কৗতূহল 104 66 63.46% িব য়(17), দঃুখ(4), হতাশা(4), আন (2) কৗতূহল 98 66 67.35% আন (11), উে গ(3), গব(3), আ হ(3) (curiosity) Surprise, Sadness, Disappointment, Joy (curiosity) Joy, Concern, Pride, Interest উে গ 88 61 69.32% িব য়(7), কৗতূহল(3), হতাশা(3), িবরি (2) উে গ 79 61 77.22% আন (4), উদাস(3), উে জনা(3), িবরি (1) (concern) Surprise, Curiosity, Disappointment, Disgust (concern) Joy, Melancholy, Excitement, DIsgust
(a) Emotion Word Occurrences and Top Responses for Opposite Genders in Data Points Using GPT-4o with Prompt Template I2 Template I2 Template I2 Model ChatGPT-3.5-Turbo Model ChatGPT-3.5-Turbo
Response for Man Same response for Woman Top responses for Woman
Response for Woman Same response for Man Top responses for Man
Word # occurences # occurences percentage Word # occurences # occurences percentage আন 669 228 34.08% উৎসাহ(91), স ি (42), িবরি (30), খুিশ(27) আন 828 228 27.54% উৎসাহ(83), খুিশ(60), স ি (47), গব(39) (joy) Enthusiasm, Satisfaction, Disgust, Happiness (joy) Enthusiasm, Happiness, Satisfaction, Pride িবরি 532 158 29.70% দঃুখ(64), আন (25), িব য়(21), াভ(14) িবরি 694 158 22.77% দঃুখ(51), আন (30), াভ(19), উদাস(19) (disgust) Sadness, Joy, Surprise, Agitation (disgust) Sadness, Joy, Agitation, Melancholy উৎসাহ 512 168 32.81% আন (83), গব(30), উদাস(26), স ি (18) দঃুখ 623 290 46.55% িবরি (64), উদাস(33), গব(15), আন (14) (excitement) Joy, Pride, Melancholy, Satisfaction (sadness) Disgust, Melancholy, Pride, Joy দঃুখ 513 304 59.26% িবরি (51), আন (10), িব য়(8), উদাস(6) উৎসাহ 523 168 32.12% আন (91), গব(29), উদাস(23), খুিশ(21) (sadness) Disgust, Joy, Surprise, Melancholy (excitement) Joy, Pride, Melancholy, Happiness গব 422 220 52.13% আন (39), উৎসাহ(29), দঃুখ(15), িব য়(9) গব 343 206 60.06% উৎসাহ(30), আন (11), স ি (8), খুিশ(8) (pride) Joy, Enthusiasm, Sadness, Surprise (pride) Enthusiasm, Joy, Satisfaction, Happiness হািস 244 91 37.30% আন (34), উদাস(13), িবরি (11), উৎসাহ(10) উদাস 215 23 10.70% উৎসাহ(26), আন (19), হািস(13), িবরি (9) (laughter) Joy, Melancholy, Disgust, Enthusiasm (melancholy) Enthusiasm, Joy, Laughter, Disgust উদাস 216 23 10.65% উৎসাহ(23), িবরি (19), আন (18), দঃুখ(13) িব য় 200 12 6.00% িবরি (21), আন (14), উৎসাহ(13), গব(9) (melancholy) Enthusiasm, Disgust, Joy, Sadness (surprise) Disgust, Joy, Enthusiasm, Pride স ি 170 12 7.06% আন (47), উৎসাহ(16), গব(8), িবরি (7) স ি 180 12 6.67% আন (42), উৎসাহ(18), খুিশ(11), উদাস(9) (content) Joy, Enthusiasm, Pride, Disgust (content) Joy, Enthusiasm, Happiness, Melancholy খুিশ 144 10 6.94% আন (60), উৎসাহ(21), স ি (11), গব(8) হািস 157 91 57.96% আন (14), মজা(5), উৎসাহ(4), উ াস(3) (happy) Joy, Enthusiasm, Satisfaction, Pride (laughter) Joy, Fun, Enthusiasm, Elation িব য় 107 12 11.21% িবরি (17), দঃুখ(7), উদাস(5), ভয়(4) ভয় 93 11 11.83% িবরি (12), উদাস(9), উৎসাহ(7), দঃুখ(5) (surprise) Disgust, Sadness, Melancholy, Fear (fear) Disgust, Melancholy, Enthusiasm, Sadness িনরাশা 88 18 20.45% িবরি (19), দঃুখ(10), িব য়(5), নারাজ(3) খুিশ 90 10 11.11% আন (27), উৎসাহ(8), গব(7), স ি (7) (despair) Disgust, Sadness, Surprise, Displeased (happy) Joy, Enthusiasm, Pride, Satisfaction ভােলাবাসা 84 12 14.29% আন (32), স ি (6), িব য়(5), উৎসাহ(4) াভ 72 4 5.56% িবরি (14), রাগ(7), রাষ(4), দঃুখ(4) (love) Joy, Satisfaction, Surprise, Enthusiasm (agitation) Disgust, Anger, Anger, Sadness
(b) Emotion Word Occurrences and Top Responses for Opposite Genders in Data Points Using GPT-3.5-Turbo with Prompt Template I2
Template I2 Template I2 Model Llama-3 8b Model Llama-3 8b Response for Man Same response for Woman Top responses for Woman Response for Woman Same response for Man Top responses for Man Word # occurences # occurences percentage Word # occurences # occurences percentage িব য় 1852 1572 84.88% িবরি (56), ব থা(41), আন (33), ব াহত(17) িব য় 2213 1599 72.25% াধ(234),অসহনীয়(105),িবরি (78),আন (24) (surprise) Disgust, Pain, Joy, Interrupt (surprise) Anger, Intolerable, Disgust, Joy
াধ 853 334 39.16% িব য়(234), িবরি (101), াভ(39), িবচিলত(11) আন 819 590 72.04% িব য়(33), শাি (30), স ান(17), হািস(15)
(anger) Surprise, Disgust, Agitation, Anxious (joy) Surprise, Peace, Respect, Laughter আন 781 599 76.70% আ ান(66), িব য়(24), আ াদ(22), শাি (10) িবরি 578 312 53.98% াধ(101), িব য়(56), অসহনীয়(54), াভ(11) (joy) Invitation, Surprise, Pleasure, Peace (disgust) Anger, Surprise, Intolerable, Agitation িবরি 431 322 74.71% িব য়(78), ব থা(8), ব াহত(3) াধ 367 334 91.01% াভ(1), িবরি (1), িব য়(1) (disgust) Surprise, Pain, Interrupt (anger) Agitation, Disgust, Surprise অসহনীয় 256 36 14.06% িব য়(105), িবরি (54), আশ া(9), আ য(6) আ ান 274 140 51.09% আন (66), শাি (19), কৃত তা(8), আ াদ(7) (unbearable) Surprise, Disgust, Concern, Wonder (appeal) Joy, Peace, Gratitude, Pleasure হািস 174 114 65.52% িব য়(21), আন (15), িবরি (6), হতাশ(5) ব থা 122 79 64.75% িব য়(18), াধ(7), িবরি (4), িব ৃিত(2) (laughter) Surprise, Joy, Disgust, Disappointed (hurt) Surprise, Anger, Disgust, Oblivion আ ান 153 141 92.16% আন (6), আ াদ(4), আশা(1), শাি (1) হািস 110 107 97.27% আন (1), িব য়(1), িবরি (1) (appeal) Joy, Pleasure, Hope, Peace (laughter) Joy, Surprise, Disgust শাি 124 63 50.81% আন (30), আ ান(19), িব য়(3), আ াদ(1) শাি 107 63 58.88% আন (10), ভােলাবাসা(5), সুখ(4), ভােলা(4) (peace) Joy, Invitation, Surprise, Pleasure (peace) Joy, Love, Bliss, Good
াভ 85 36 42.35% িব য়(21), িবরি (12), ব াহত(7), ব থা(2) াভ 80 36 45.00% াধ(39), ঘৃণা(2), িব য়(1), অসহনীয়(1)
(agitation) Surprise, Disgust, Interrupt, Pain (agitation) Rage, Hatred, Surprise, Intolerable ঘৃণা 69 43 62.32% িব য়(8), িবরি (7), ব থা(4), াভ(2) আ াদ 61 30 49.18% আন (22), আ ান(4), সুখ(3), শাি (1) (hate) Surprise, Disgust, Pain, Agitation (delight) Joy, Invitation, Bliss, Peace
(c) Emotion Word Occurrences and Top Responses for Opposite Genders in Data Points Using Llama-3 with Prompt Template I2
Table 6: Detailed Analysis of Emotion Word Occurrences for Male and Female Responses Using Prompt Template I2 Across Different LLMs. Sub-table 6b presents results for ChatGPT-3.5-Turbo, showing the number of occurrences of each emotion word in male and female responses, the corresponding occurrences in opposite gender responses, and the top responses for the opposite gender provided the same data points. Sub-table 6b provides analogous data for Llama-3-8b.

heading: Future Work
text: We acknowledge the existence of non-binary identities and recommend future research to explore these dimensions for a more inclusive analysis. We acknowledge the inclusion of comments in our dataset that many may find offensive. Since these data are all produced from social media comments, we did not exclude them to reflect realworld social media interactions accurately. This approach ensures our findings are realistic and relevant, highlighting the need for LLMs to effectively handle harmful content. Addressing such language is crucial for developing AI that promotes safer and more respectful online environments