Venue: NAACL 2025

# Social Norms in Cinema: A Cross-Cultural Analysis of _Shame_, _Pride_ and

_Prejudice_

 Sumny Rai\({}^{\dagger}\), Khushang Jilesh Zaveri\({}^{\diamond}\), Shreya Havaldar\({}^{\dagger}\), Soumna Nema\({}^{\spadesuit}\),

**Lyle H. Ungar\({}^{\dagger}\) & Sharath Chandra Guntuku\({}^{\dagger}\)**

\({}^{\dagger}\)University of Pennsylvania, \({}^{\diamond}\)Johns Hopkins University, \({}^{\spadesuit}\)Mahindra University

{sunnyrai, sharathy@seas.upenn.edu

###### Abstract

Shame and pride are social emotions expressed across cultures to motivate and regulate people's thoughts, feelings, and behaviors. In this paper, we introduce the first cross-cultural dataset of over \(10k\) shame/pride-related expressions, with underlying social expectations from 5.4K Bollywood and Hollywood movies. We examine _how_ and _why_ shame and pride are expressed across cultures using a blend of psychology-informed language analysis combined with large language models. We find significant cross-cultural differences in shame and pride expression aligning with known cultural tendencies of the USA and India - e.g., in Hollywood, shame-expressions predominantly discuss _self_ whereas shame is expressed toward _others_ in Bollywood. Women are more sanctioned across cultures and for violating similar social expectations.

## 1 Introduction

Shame and pride are social emotions expressed across cultures to motivate and regulate people's thoughts, feelings, behaviors, achievements, and social adaptations Fischer and Tangney (1995); Goetz and Keltner (2007); Fessler (2007); Schaumberg and Skowronek (2022). The expressions and appraisals of shame and pride vary across cultures and is a research question of great significance to understand society and self-regulation Wong and Tsai (2007); Lewis et al. (2010); Wegge et al. (2022). The overarching question we ask in this paper is, _how_ and _why_**shame and pride are expressed across cultures**. Knowing cross-cultural differences in shame/pride-related expressions will help us measure psychological constructs such as shame proneness Tanguey et al. (1992); Cohen et al. (2011) and self-esteem Pyszczynski et al. (2004) reliably from language - enabling effective human-computer interactions in domains such as AI-driven psychotherapy Resendiz and Klinger (2023); Demszky et al. (2020). Examining behaviors evoking shame and pride will reveal culture-specific societal expectations and attitudes (See Table 1) - paramount to building culturally competent AI models for diverse users Talat et al. (2021); Atari et al. (2023).

For our study, we select two top movie industries, i.e., Hollywood and Bollywood1. Hollywood primarily depicts social situations from the USA, an individualist society that values competency and autonomy Triandis (1989, 1988). Bollywood depicts India, a collectivist society where one's sense of self is interwoven with community beliefs. The cultural dichotomy between India and the USA (i.e., _collectivism vs. individualism_) thus presents a rich ground for understanding variations in their beliefs and values. Our approach blends psychology-informed language analysis with state-of-the-art Large Language Model (LLM) to delineate interpretable psychosocial constructs such as _self-focus_ and _morality_ in shame/pride-related expressions and extract implicit social expectations behind them.

Footnote 1: [https://en.wikipedia.org/wiki/Film](https://en.wikipedia.org/wiki/Film) ̇industry

\begin{table}
\begin{tabular}{l} And should we bow before others begging....then \\ to marryour daughters? This shall not happen. \\ Neither will the girls be alive here nor \\ shall...we be sahaned of ourselves. You cannot \\ kill the life which God has given. I won’t let \\ you commit the sin. \\ \(\Rightarrow\)**Not able to marry off their daughters** evokes \\ shame. \\ \hline Sister-in-law! Congrats, sister-in-law! Big \\ brother has started working! \\ Really? \\ Now you will have a place of pride in this family! Yes, please! \\ \(\Rightarrow\)**Employed husband** evokes _pride_ \\ \end{tabular}
\end{table}
Table 1: Excerpts of dialogues expressing explicit social emotions shame and pride indicating actions and social approval.



This paper makes the following research contributions: (a) We develop a cross-cultural dataset of over 10k shame/pride-related dialogues with underlying social expectations2, (b) We demonstrate cross-cultural variations in how and why shame and pride are expressed in movies, and (c) We illustrate that women are more subjected to social sanctions than men across cultures and for violating similar social expectations.

Footnote 2: [https://github.com/Khushangz/Cross-Cultural-Social-Norms-Dataset/](https://github.com/Khushangz/Cross-Cultural-Social-Norms-Dataset/)

To the best of our knowledge, this is the first empirical study analyzing the psychosocial constructs underlying shame/pride-related expressions across cultures and the social expectations behind them.

## 2 Movies Subtitles Corpora

Movies provide culture-specific life-like depictions of social situations Adkins and Castle (2014); Kubrak (2020). The natural conversation style between characters in movies can reveal the social power dynamics (e.g., _boss-employee_, _fatherdaughter_) and gender roles. Another potential alternative is social media posts however, they are (a) skewed to social situations specific to young demographic groups from Western countries, and (b) are second-hand reports that do not reveal fully the social dynamics of how shame/pride are expressed in natural conversations, for instance, between a boss and an employee.

We collected English subtitles for 5,435 Hollywood and Bollywood movies that were released post-1990 by auto-crawling websites that host or link movie subtitles (See Table 2 for data distribution). The subtitles are professionally done translations to preserve the emotions and intended message. Bollywood movie subtitles were collected from www.Bollynook.com and Hollywood movie subtitles were from the publicly available Kaggle movie subtitles dataset3. The year mapping was performed to ensure a similar period for collected movies. The year of release for movies was verified by either parsing subtitle file names having a release year or probing Wikipedia entries.

Footnote 3: [https://www.Kaggle.com/datasets/adimaman/movie-subtitle-dataset](https://www.Kaggle.com/datasets/adimaman/movie-subtitle-dataset)

### Extracting Shame and Pride related Expressions

The expressions of shame and pride could be explicit (See Table 1) or implicit (e.g. characters' lowered shoulders, avoiding eye contact, etc.). We extracted **explicit mentions of _shame_ and _pride_ to learn about occurrences of shame and pride _as determined by the characters_. We adopted a keyword search-based approach (See Appendix A.1) to identify dialogues along with the previous and the next two lines for situational context (See Table 1). For short dialogues such as monosyllabic responses in spoken conversations, we appended an extra previous and next line to context. One author manually checked the entire dataset of shame and pride-related dialogues to filter out dialogues with conventional phrases such as "_what a shame_", "_it's a shame_", "_proudly presents_".

Detecting **implicit shame/pride-related expressions** in textual discourse is a complex task. Computational systems for detecting social emotions such as shame and guilt underperform significantly compared to basic emotions such as joy due to internal self-evaluation associated with social emotions Demszky et al. (2020); Resendiz and Klinger (2023). We used GPT-4 language model Achiam et al. (2023) to detect implicit expressions of shame in given movie dialogues. We recruited two human annotators to determine the agreement with GPT-4 generated labels (See Appendix A.2 for details). On a randomly sampled 100 situations each from H/Bollywood, the model labeled 20 samples to be expressing shame in Bollywood and 5 samples in Hollywood. However, the agreement between human annotators was low (4 out of 20 samples in Bollywood and 1 out of 5 samples in Hollywood where both annotators agreed with GPT-4). Human annotators across cultures tend to cross-label shame and guilt Troiano et al. (2019) and follow-up discussions with annotators revealed similar tendencies. One such example from Hollywood is

\begin{table}
\begin{tabular}{l|l|l} \hline \hline  & **Hollywood** & **Bollywood** \\ \hline \#Movies & 2697 & 2738 \\ \#Tokens & 20.78M & 22.62M \\ \#Shame & 1221 & 5409 \\ \#Pride & 2805 & 2999 \\ \#Control & 4385 & 8303 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data Distribution. #shame indicates the number of dialogues with the word ”shame” or its derivative form (e.g., ashamed, shameless - See Table A.1). Similarly, #pride indicates the number of dialogues with the word pride or its derivative form (e.g., proud). Control comprises dialogues without words _shame, pride_, or their derivatives.

 

provided below:

"_about feeding the poor, I've never done any of that. God tells us to love everybody. I've hated people... my family, my family, "_

Here, the speaker is probably expressing guilt, shame, or both. Given subjective beliefs and poor agreement with generated outputs, we adopted a high precision-low recall strategy and limited our further analysis to explicit mentions of shame and pride.

We created four sets of dialogues: (a) shame-related dialogues in Bollywood, (b) shame-related dialogues in Hollywood, (c) pride-related dialogues in Bollywood and (d) pride-related dialogues in Hollywood. Cross-cultural differences also exist in speaking styles across cultures and we thus formed a control set of dialogues unrelated to _shame_ and _pride_ for both movie industries to remove the variations in language markers owing to culture-specific speaking styles (See Table 2 for dialogue distribution).

## 3 Approach

Fig. 1 illustrates our study design comprising two key approaches (a) using a psychosocial vocabulary approach to measure how expressions of shame and pride differ and (b) _prompting LLM_ approach to extract reasons behind them and how they differ between India and the U.S.A.

### Vocabulary Approach

Linguistic Inquiry of Word Count (LIWC) [5] is a corpus analysis tool to identify psychological constructs such as "self-focus" and "morality", and therefore, is widely used for examining social behaviors such as self-regulation and conformity.

To understand cross-cultural linguistic variations in the manifestation of shame and pride, we computed the normalized distribution of psychosocial categories in LIWC from the dialogues and examined their correlation with shame and pride compared to the control set. The search keywords used for building social emotions corpus (See Table A1) were removed from the LIWC dictionary to prevent overestimation of shame- (e.g., negative emotion) and pride-related categories (e.g., achievement).

### Prompting Approach

Identifying why shame/pride is expressed in discourse requires multicultural world knowledge. LLMs such as GPT-4 exhibit a superlative pragmatic understanding of the world around us and are increasingly used for extracting implicit meanings and beliefs [60, 79]. We used GPT-4 chat in a two-shot setting ( See Tables A2 and A3 for prompts) to identify:

* who is \(<\)being shared/feeling proud\(>\) in the given movie discourse, and what is their gender? and,
* What is the reason behind \(<\)the feeling of shame/pride\(>\)?

Since there are at least two characters in a discourse, the first question orients the LLM to focus on the _person experiencing the social sanction or approval_ and then identify their gender. The output for the second question serves as the implicit _social expectation_ that led to the expression of shame/pride in the culture. Asking for "reason" leading to the expression of social emotion encourages LLMs to retrieve expectations from the provided context, mitigating potential Anglocentric tendencies [8].

#### Thematic Analysis of Reasons Behind Shame/Pride-related Expressions

To capture overarching themes in social expectations that led to shame/pride-related expressions in Indian and American societies, we performed agglomerative clustering after embedding unique

Figure 1: An overview of our approach comprising two key steps (a) Vocabulary approach and (b) Prompting a pre-trained LLM.

 

shame and pride-related reasons using SBERT embeddings (Reimers and Gurevych, 2019). Unlike movie dialogues, reasons extracted from GPT-4 chat are short phrases (See Table 4) and are devoid of culture-specific language style markers (i.e., Indian English vs American English) thus, no control for culture-specific language is needed.

## 4 Results

### How is shame/pride expressed across cultures?

Shame is associated with negative emotions, power, and morality in both movie industries (See Tables A7 and A9) however, significant cultural nuances exist in its manifestation (See Fig. 2). In Bollywood, shame-related expressions are _other-oriented_ as indicated by 2nd person pronouns and social references. Female and sexual categories are correlated with shame exclusively in Bollywood potentially indicating the honor system in collectivist societies (Caffaro et al., 2014). Other psychosocial categories such as _swearing_ and _conflict_ with present focus reveal the role of shame in enforcing conformity. In Hollywood, shame expressions are self-focused with remorse (e.g., _sadness, anxiety_) and have past-focused language. _Illness_ is uniquely correlated with shame in Hollywood indicating social sanctions around incapability.

A similar dichotomy is observed for pride (See Fig. 3). In Bollywood, pride is _achivement/power_ focused and "We"-centered. Pride is associated exclusively with men in Bollywood. Other psychosocial categories such as _moral_ and _politics_ are more strongly associated with pride. In Hollywood, pride is more self-centered and expressed for _family_ more. Male references are more strongly associated with pride than females in Hollywood whereas the female category has an insignificant (\(p>0.05\)) correlation with pride in Bollywood. Pride has a positive undertone in Hollywood whereas in Bollywood, we speculate that it relates to honor (e.g., _protecting family's pride, bringing pride to family_) and does not have a positive undertone.

### How do _social expectations_ behind shame/pride vary across cultures?

Cross-Cultural Shame/Pride DatasetFor the Bollywood set, GPT-4 predicted reasons (also implicit social expectations) for 5321 (98.4%) sham-related dialogues out of 5409, and 2237 (74.6%) pride-related dialogues out of 2999. For the Hollywood set, GPT-4 chat predicted a reason for 1156 (94.6%) shame-related dialogues out of 1221 and 1731 (61.7%) pride-related dialogues out of 2805. Upon manual analysis, we found that pride is also used to express affection, specifically toward close family members such as children. As a result, a lower number of reasons is associated with pride compared to shame. GPT-4 chat predicted a total of **10,445 social expectations** (See Table 3 for distribution). Prompting "reasons" behind shame

Figure 3: Pearson \(r\) for LIWC categories significantly correlated (\(p<0.05\)) with **prior** for Hollywood and Bollywood after Benjamini-Hochberg p-value correction (See Appendix for confidence intervals and p values). Note the contrast, Achievement-related & Wecentered pride in Bollywood vs Social & Self-centered pride in Hollywood. See Table A8 and A10 for the complete set of correlations.

Figure 2: Pearson \(r\) for LIWC categories significantly correlated (\(p<0.05\)) with **share** for Hollywood and Bollywood after Benjamini-Hochberg p-value correction (See Appendix for confidence intervals and p values). Note, \(1^{st}\) person sing pronouns are strongly correlated with Hollywood-shame whereas the correlation with \(2^{nd}\) person pronoun and social references are up to 3 times stronger in Bollywood compared to Hollywood. See Table A7 and A9 for the complete set of correlations.

 

and pride allowed us to capture high specificity in cultural norms (See Tables 4 for shame-related expectations and A6 for pride-related expectations).

#### 4.2.1 Manual Evaluation

Two volunteers manually verified the predicted gender for "the person experiencing shame/pride" and the predicted "reason" for a randomly sampled set of \(100\) dialogues each from Hollywood and Bollywood. An Indian annotator aware of social roles and expectations in Indian society labeled the Bolywood set. Likewise, an American annotator labeled the Hollywood set.

For Bollywood, 8% of predicted gender and 11% of predicted reasons were incorrect where it was 5% and 2% respectively for Hollywood (See Table A5 for more details). There were 20 cases where gender [(15)] and/or reason [(9)] were ambiguous whereas, for Hollywood, there were 11 (gender=10, reason=1) such samples.

#### 4.2.2 Thematic Analysis

Twenty-four clusters for shame-related reasons and fifteen for pride-related reasons were formed using agglomerative clustering ( See Tables A13 and A14 for clustering parameters). The clusters were manually assigned a theme (as depicted on the Y-axis of Fig. 4 and 5) after analyzing the ten closest samples based on cosine distance from the centroid of the cluster (See Table A11 and A12 for examples in each cluster). We computed the relative association for each cluster with Bollywood and Hollywood using eq. 1 and performed the Barnard-Exact Test [(Barnard, 1947)] with the Yates Correction [(Yates, 1934)] to test if the possibility of observing norms related to pre-assigned themes is statistically different across movie industries.

\[{\vec{\Delta}}=\forall_{t_{i}\in themes}\frac{{\cal D}_{boly_{t_{i}}}}{{\cal D }_{boly}}-\frac{{\cal D}_{holy_{i}}}{{\cal D}_{haly}} \tag{1}\]

Here, \({\cal D}_{industry_{t_{i}}}\) represents dialogues to \(industry\in\{holly,body\}\) and \(t_{i}\in themes\) (as depicted on Y-axis in Fig 4 and 5).

Shame-related social normsThemes such as _lack of accountability_ and _poverty_ are more common in Hollywood whereas _inaappropriate sexual behavior_ and _gender roles_ are more prevalent in Bollywood (See Fig. 4). Unsurprisingly, collectivist factors such as non-conformity in _gender_ roles, disrespect, and deviation from _family norms_ are strongly associated with shame in Bollywood whereas individualistic attributes such as _poverty_, causing _harm_ and _incompetence_ evoke shame in Hollywood.

Pride-related social norms_Duty_, _doing the "right" thing_, and, _self-identity_ are associated with pride in Hollywood whereas _Ethnolinguistic identity_, and _son's achievements_ are associated with pride in Bollywood (See Fig. 5).

#### 4.2.3 How is shame/pride attributed across genders?

For the Bollywood set, GPT-4 predicted 1541 targets as female and 3949 as male. For the Hollywood set, GPT-4 predicted 482 targets as female and 1367 as male. Across all combinations (shame vs pride x Bollywood vs. Hollywood in Table 3), there are more male targets than females in line

\begin{table}
\begin{tabular}{c|c|c|c|c}  & \multicolumn{2}{c|}{Bollywood} & \multicolumn{2}{c}{Hollywood} \\ \hline  & shame & pride & shame & pride \\ \hline male & 2690 & 1259 & 591 & 776 \\ female & 1215 & 326 & 246 & 236 \\ Not clear & 1416 & 652 & 319 & 719 \\ \hline total & 5321 & 2237 & 1156 & 1731 \\ \hline \end{tabular}
\end{table}
Table 3: Reason and Gender distribution (with duplicates) for dialogues for which GPT-4 predicted _male_ or _female_. The duplicate reasons are not removed as their frequency reflects their prevalence and is useful for estimating gender association.

 

with the findings of Geena Davis Inclusion Quotient4.

Footnote 3: [https://about.google/intl/ALL_us/main/gender-equality-films/](https://about.google/intl/ALL_us/main/gender-equality-films/)

We computed the gender-wise attribution to "shame" and "pride" in movie dialogues using eq. 2. A positive score indicates a higher association of gender groups with pride, whereas a negative score reflects a higher association with shame. A null score indicates no preference.

\[\vec{\Delta}_{g}=\forall_{g\in\{male,female\}}\frac{\mathcal{D}_{pridge}- \mathcal{D}_{share_{g}}}{\mathcal{D}_{g}} \tag{2}\]

Here, \(\mathcal{D}_{pridge}\) represents movie dialogues having pride-related words where the target of pride is \(g\in\{male,female\}\), \(\mathcal{D}_{shame_{g}}\) represents movie dialogues having shame-related word where the target of shame is \(g\in\{male,female\}\) and \(\mathcal{D}_{g}\) represents the number of movie dialogues spoken by \(g\). As depicted in Fig. 6, Hollywood movies are pride-oriented, whereas Bollywood movies are shame-oriented. Females are attributed more shame, and the difference (male-female) in the expression of pride and shame is \(0.16\) for Hollywood and \(0.21\) for Bollywood.

Social expectations evoking shame are similar across movie industries (See Fig. 7 and 8). Sexuality (e.g., _promiscuity, immodesty_) is the dominant theme for sanctioning women in both movie industries whereas males are shamed for _incompetency_. Women across movie industries express more pride in _family roles_. Men-pride in Bollywood is centered on _justice, winning_ and _bravery_ whereas it is _duty_, _self-identity_ and _winning_ in Hollywood (See Fig. A1 for gender differences in pride).

## 5 Discussion

We release a cross-cultural dataset of shame- and pride-related expressions with implicit reasons behind them. To the best of our knowledge, the only related corpus is the GoEmotions dataset (Demszky et al., 2020) comprising 817 samples for embarrassment and 452 samples for pride. Our dataset

Figure 4: Relative association (\(\Delta\)) of Bollywood and Hollywood to themes obtained from agglomerative clustering performed on shame-related norms. \(*\) indicates significant difference i.e., \(p<0.05\). See Table A15 for statistics and p-value.

Figure 5: Relative association (\(\Delta\)) of Bollywood and Hollywood to themes obtained from agglomerative clustering performed on pride-related norms. \(*\) indicates significant difference i.e., \(p<0.05\). See Table A16 for statistics and p-value.

Figure 6: Relative association (\(\Delta\)) of social emotions attributed to male and female. A higher positive score indicates a stronger association of gender with pride.

 

can help develop computational models for detecting shame/pride expressions and recognizing how appraisals of such emotions vary - enabling culturally cognisant human-computer conversations for diverse users Kim et al. (2011) and cross-cultural intent translation5.

Footnote 5: DARPA Computational Cultural Understanding: [https://www.darpa.mil/program/computational-cultural-understanding](https://www.darpa.mil/program/computational-cultural-understanding)

Shame is a highly undesirable emotion emphasizing incompetency and failures in the U.S. and used sparingly Cohen (2003); Boiger et al. (2013) whereas Eastern cultures use shame more frequently and it is known to have a desirable affect Lim (2016). Notably, we also observe that shame is 4.5 times more common in Bollywood compared to Hollywood. We also observe that shame is self-focused (reflecting internal shame) in Hollywood (See Table 2 and Fig. 2 for frequency distribution and psychosocial constructs). In contrast, shame is interdependent in collectivist communities Wong and Tsai (2007). The empirical analyses reveal other-focused (also, the public nature of shame) in Bollywood movies (See Fig. 3). Moreover, the contrasting tenses coupled with varying emotions, i.e., past-focus + sadness in Hollywood vs. present-focus + conflict in Bollywood, reflect their varying goals, i.e., remorse for past failures/losses vs enforcing conformity Wong and Tsai (2007). Pride-related discourse in Hollywood is duty and achievement-focused, in line with prior findings that the significance of "success" grows as a society becomes more individualistic Cohen (2003). In Bollywood, pride is collective and male-focused (_we, achievement, male-focused_) Khadilkar et al. (2022). It is also interesting to note linguistic markers such as _determiner_ (used with objects/nouns) in Bollywood indicate more materialistic pride, compared to personal pronouns and social references in Hollywood which reflect more personal pride.

Eliciting reasons leading to shame and pride expressions revealed social expectations across cultures. We observed high specificity in extracted reasons revealing cultural subtleties (e.g., _desire for a son_ in Bollywood vs. _returning calls after date night_ in Hollywood). To tease apart cultural differences, we first considered mapping social expectations/generated reasons to Schwartz's Theory of Values Schwartz (2012). However, social expectations were found to have contrasting values depending on the culture. Consider "refusing to marry", an instance of non-conformity in Indian society, whereas it is an instance of self-direction in a Western context. We thus performed hierarchical clustering and empirically picked the distance after manually analyzing the quality and granularity of clusters. The situations connected to shame and

Figure 8: Gender differences in shame-related themes in **Hollywood** movies. \(*\) indicates significant difference i.e., \(p<0.05\).

Figure 7: Gender differences in shame-related themes in **Bollywood** movies. \(*\) indicates significant difference i.e., \(p<0.05\).

 

proide in the U.S. society (i.e., _lacking accountability, poverty, harm_) vs Indian society (e.g., _inappropriate sexual behavior, gender roles, betrayal_ - see Fig 4 and 5) align with known cultural tendencies (i.e., individualist vs collectivist) of both nations Triandis [198].

The vision of safe and accountable AI is centered on LLMs' moral and value alignment. The dominant approach for norm discovery involves prompting LLMs, sometimes coupled with a verification step such as an entailment test or identifying underlying emotion (negative emotion \(\rightarrow\) norm violation) Jiang et al. [2021]; Fung et al. [2022]; CHwang et al. [2023]. However, a majority of social situations and the human annotators employed to label those situations reflect English beliefs and ethics. Our approach for extracting culture-specific social expectations and attitudes using social emotions overcomes this limitation. While the clustering was performed on the reasons extracted from movie conversations, the cluster themes are similar to patterns seen during LIWC analysis, validating our approach for overcoming Anglocentric bias in LLMs for norm discovery.

Lastly, we examined how social expectations associated with shame/pride are attributed to men and women across cultures influencing their social behaviors. For example, women are less assertive across cultures to avoid negative attribution Amanatullah and Morris [2010]; Ferguson and Eyre [2000] and ask for less during negotiation than men Arnold McAuliffe [2021]. We demonstrated that shame, a negative self-conscious emotion expressing devaluation, is targeted toward women more than men, and pride, a social emotion endorsing social value, is more used for men than women. The similarity in social expectations (e.g. regulating sexual behaviors of women vs shaming incompetency in men) across cultures is surprising. We thus feel that it is important to characterize social biases in the training data before their use for aligning LLMs.

## 6 Conclusion

We introduced a multi-cultural dataset of shame/pride-related expressions and the underlying social expectations. This study demonstrates (a) cross-cultural linguistic differences in shame and pride-related expression, offering insights into their functions across cultures, (b) cultural dichotomy in social expectations, and (c) more "social sanctions" and fewer "endorsements" to women in movies. Future work can utilize our dataset for culturally aligned LLMs and build social emotion perception and appraisal in human-computer interactions. Additionally, our analysis of shame/pride is the first of its kind; we hope future NLP researchers will build upon this work to investigate social expectations in LLMs from a multicultural, social emotion-based perspective.

### Social Impact and Ethics Statement

Social norms discovery is a crucial component of social and behavioral change programs6 promoting equity, social justice, and well-being Mauduy et al. [2022]; Bonan et al. [2020]. Further work explores style as a product of norm differences Havaldar et al. [2023a]. Social psychology investigates social norms (descriptive vs injunctive) to design experiments for understanding behaviors such as self-regulation, persuasion Cialdini et al. [1990] and decision-making Gavrilets [2020]; Bhanot [2021] to promote collective-level change in societies.

Footnote 6: ALIGN-[https://www.alignplatform.org/learning-collaborative](https://www.alignplatform.org/learning-collaborative)

Relatedly, Kimbrough and Vostroknutov [2023] showed people's tendency to choose self-serving social norms using a dictator-recipient setup, emphasizing the need for dedicated research efforts to understand morality and belief distortion in different contexts. The norms and cultural preferences learned from movies that often showcase stereotypical behaviors of society may induce pluralistic ignorance and, more importantly, lead to discrimination and biases in LLMs when used for training. We hope that this paper will encourage scrutiny of source corpora and derived norms before their use for fine-tuning LLMs.

### Limitations

Social norms mutate as society evolves. We acknowledge that our dataset of movies (released post-1990) may reflect social norms that are less characteristic of contemporary society. Moreover, countries like India and America contain a mix of cultures. The captured norms may not reflect the cultural variations, for example, between regions (e.g., East Coast vs West Coast in the U.S.A or North vs South in India). Movies also exaggeratedly depict the world around us (e.g., weddings, 

criminal activities, sexual abuse, etc.), and we caution against stereotyping cultures based on movie-based norms.

The dominant language in Bollywood movies is Hindi and our analysis is based on their English translations which may not always be accurate, especially when the discourse is about concepts native to a culture. LIWC dictionary may also lack complete coverage for such concepts. We did not compare the movie genre and acknowledge that situational/unrealistic norms (e.g., science fiction, comedy, etc.) could exist. Regardless, social norms associated with shame and pride are still relatable and reflect the target audience's beliefs. This study is conducted over a thirty-year long period and the aggregated norms may not truly reflect the current trends.

We acknowledge that the excerpts expressing shame and pride from movie subtitles may have led to the over-representation of certain social situations (e.g., son's achievement and daughter's wedding in India vs duty and competence in America). The prompts used to elicit norms may have induced unwanted bias Cheng et al. (2023); Lucy and Bamman (2021) and it is worth investigating the variations if any, in extracted norms due to different prompt designs.

## Data use and availability

Bollywood subtitles were scraped from a website called www.bollynook.com and Hollywood subtitles are taken from publicly available Kaggle movie subtitles datasets. We will only release dialogue excerpts related to shame and pride. We have around 7.5k such instances in Bollywood and almost 2.8k in Hollywood. Under the interpretation of fair use in research, this makes up a very small portion of dialogues taken from Bollywood (i.e. almost 3 samples (5 lines long) per movie on average) and Hollywood (about 1 sample (5 lines long) per movie on average).

## Acknowledgements

This work was supported in part by the Penn Global Engagement Research Fund. We acknowledge the use of ChatGPT, an AI language model developed by OpenAI, for its assistance with writing.

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Appendix

### Search Keywords

Search keywords defined in Table A1 were used to extract shame- and pride-related dialogues from Bollywood and Hollywood movie subtitles.

### Examining Implicit shame

We prompted GPT-4 chat to identify (a) if a given discourse expresses shame, and (b) identify the behavior or action behind shame, for a randomly sampled set of 100 samples without explicit mentions of shame/pride each from Hollywood and Bollywood. We asked two human annotators to evaluate the correctness of GPT-4 chat-generated outputs. See Table A4 for annotation guidelines.

### Prompts

The prompts designed to extract the subject experiencing shame and pride, their gender, and the reason behind the emotions are provided in Tables A2 and A3.

#### a.3.1 Annotation

The annotation guidelines to verify the gender predicted by GPT-4 and the correctness of the reason are provided in Table A4. The annotators for Bollywood set and Hollywood set were Indian and American respectively. Both annotators were female, proficient in English language, and well-versed with social norms. During annotation, if the gender or the reason is unclear, the annotators were asked to label "not 

explicit". We only considered the cases where the gender was predicted to be either male or female. The task is objective and inter-annotator agreement was not computed. The annotators volunteered for the task and were not provided monetary compensation.

### LIWC Correlation Results

Tables A7, A8, A9 and A10 contain positively correlated (\(p<0.05\)) LIWC categories, the most frequent five words for each category, Pearson \(r\) and confidence interval.

 



\begin{table}
\begin{tabular}{l|c|c|c|c} LIWC Categories & Top-5 words & \(r\) & p-value & 95\% CI \\ \hline Negative emotions (EMO\_NEG) & (bad, mad, scared, worry, fear) & 0.330 & 0.000 & [0.315, 0.344] \\ Negative tone (TONE\_NEG) & (lost, kill, wrong, bad, hit) & 0.249 & 0.000 & [0.233, 0.265] \\ POWER & (sir, respect, own, kill, poor) & 0.198 & 0.000 & [0.182, 0.214] \\ EMOTION & (lowe, good, bad, happy, crazy) & 0.168 & 0.000 & [0.152, 0.185] \\ \(2^{nd}\) person pronouns (YOU) & (you, your, you're, yourself, you’ve) & 0.161 & 0.000 & [0.145, 0.178] \\ Social references (SOCRFFS) & (you, your, he, her, him) & 0.148 & 0.000 & [0.132, 0.165] \\ FEELING & (feel, touch, feeling, felt, hard) & 0.133 & 0.000 & [0.117, 0.150] \\ DRIVES & (we, our, us, sir, married) & 0.115 & 0.000 & [0.098, 0.132] \\ SOCAL & (you, your, be, her, him) & 0.110 & 0.000 & [0.094, 0.127] \\ MORAL & (wrong, innocent, duty, decent, excuse) & 0.102 & 0.000 & [0.085, 0.118] \\ AFFECT & (love, good, keep, respect, well) & 0.078 & 0.000 & [0.062, 0.095] \\ NEGATE & (not, don’t, no, aren’t, won’t) & 0.065 & 0.000 & [0.049, 0.082] \\ FEMALE & (her, she, girl, she’s, mom) & 0.060 & 0.000 & [0.043, 0.077] \\ Personal Pronouns (PPRON) & (you, i, me, your, my) & 0.058 & 0.000 & [0.041, 0.075] \\ FAMILY & (son, married, uncle, dad, mom) & 0.055 & 0.000 & [0.039, 0.072] \\ Preposition (PREEP) & (to, of, in, for, on) & 0.053 & 0.000 & [0.037, 0.070] \\ SEXUAL & (chaste, lust, sex, sexy, pimp) & 0.051 & 0.000 & [0.034, 0.067] \\ PRONOUN & (you, i, me, your, my) & 0.040 & 0.000 & [0.023, 0.057] \\ Auxiliary verbs (AUXVERB) & (is, are, have, be, don’t) & 0.029 & 0.001 & [0.013, 0.046] \\ CONFLICT & (kill, killed, accessing, killing, cruel) & 0.024 & 0.007 & [0.007, 0.041] \\ SWEAR & (hell, bloody, idiot, damn, ass) & 0.024 & 0.007 & [0.007, 0.041] \\ Anger (EMO\_ANGER) & (mad, angry, hate, cruel, argue) & 0.022 & 0.015 & [0.005, 0.039] \\ FOCUSPREST & (is, are, don’t, i’m, aren’t) & 0.021 & 0.018 & [0.005, 0.038] \\ \hline \end{tabular}
\end{table}
Table 6: A subset of reasons extracted from movie dialogues expressing pride. A total of 3163 unique reasons (Bollywood-1589, Hollywood-1574) were extracted.

\begin{table}
\begin{tabular}{l|c|c} Gender Evaluation & Incorrect & Ambiguous \\ \hline Bollywood & 8 & 15 (3 Female) \\ Hollywood & 5 & 10 (3 Female) \\ \hline \multicolumn{3}{c}{Social Norms/Reason Evaluation} \\ \hline  & Incorrect & Ambiguous \\ \hline Bollywood & 11 & 9 \\ Hollywood & 2 & 1 \\ \hline \end{tabular}
\end{table}
Table 5: Manual Evaluation of predicted gender and reasons for randomly sampled 100 samples each from Bollywood and Hollywood.





\begin{table}
\begin{tabular}{l|c|r|r|r} LIWC Categories & Top-5 words & \(r\) & p-value & 95\% CI \\ \hline Negative emotions (EMO\_NEG) & (sick, pain, fear, bad, afraid) & 0.425 & 0.000 & [0.403, 0.446] \\ Negative tone (TONE\_Neg) & (host, wrong, sick, pain, poor) & 0.355 & 0.000 & [0.331, 0.377] \\ EMOTION & (good, love, sick, pain, bad) & 0.290 & 0.000 & [0.266, 0.314] \\ POWER & (own, sir, poor, killed, war) & 0.263 & 0.000 & [0.238, 0.287] \\ AFFECT & (well, good, love, help, damn) & 0.168 & 0.000 & [0.142, 0.193] \\ DRIVES & (we, us, our, work, we’re) & 0.152 & 0.000 & [0.127, 0.178] \\ FUNCTION & (you, i, the, to, of) & 0.131 & 0.000 & [0.105, 0.157] \\ Personal Pronouns (PPRON) & (you, i, me, i’m, my) & 0.111 & 0.000 & [0.085, 0.137] \\ MORAL & (wrong, excuse, decent, honest, duty) & 0.110 & 0.000 & [0.084, 0.136] \\ \(1^{st}\) person sing. pronouns (I) & (i, me, i'm, my, i’ll) & 0.102 & 0.000 & [0.076, 0.128] \\ Sadness (EMO\_SAD) & (crying, cry, sob, lonely, sad) & 0.096 & 0.000 & [0.070, 0.122] \\ NEGATE & (no, not, don’t, nothing, never) & 0.090 & 0.000 & [0.063, 0.116] \\ Proposition (PREP) & (to, of, in, for, on) & 0.089 & 0.000 & [0.062, 0.115] \\ PRONOUN & (you, i, that, it, me) & 0.088 & 0.000 & [0.062, 0.114] \\ YOU & (you, your, you’re, yourself, you’ve) & 0.080 & 0.000 & [0.054, 0.106] \\ Auxiliary verb (AUXVERB) & (be, i’m, is, was, have) & 0.077 & 0.000 & [0.051, 0.103] \\ FOCUSPAST & (was, did, were, been, didn’t) & 0.069 & 0.000 & [0.043, 0.096] \\ SOCIAL & (youl, your, we, he, you’re) & 0.060 & 0.000 & [0.033, 0.086] \\ Conjunction (CONJ) & (and, so, but, if, when) & 0.056 & 0.000 & [0.030, 0.082] \\ LINGUSTIC & (you, i, the, to, of) & 0.056 & 0.000 & [0.030, 0.082] \\ ALLNONE & (no, all, nothing, never, yes) & 0.055 & 0.000 & [0.029, 0.081] \\ FAMLY & (son, dad, baby, mom, mama) & 0.054 & 0.000 & [0.028, 0.081] \\ Social references (SOCEFS) & (you, your, we, he, you’re) & 0.050 & 0.000 & [0.024, 0.077] \\ Anxiety (EMO\_ANX) & (fear, afraid, worry, terrified, scared) & 0.042 & 0.004 & [0.015, 0.068] \\ ILLNESS & (sick, pain, pains, flu, sickly) & 0.038 & 0.010 & [0.012, 0.064] \\ FEELING & (feel, felt, pain, feeling, hard) & 0.035 & 0.020 & [0.008, 0.061] \\ Anger (EMO\_ANGER) & (hate, hated, mad, angry, hates) & 0.033 & 0.026 & [0.007, 0.059] \\ DIFFER & (not, but, if, didn’t, or) & 0.033 & 0.027 & [0.007, 0.059] \\ Discrepancy (DISCREP) & (should, can, would, can’t, want) & 0.032 & 0.029 & [0.006, 0.059] \\ COGNITION & (no, not, all, know, but) & 0.032 & 0.033 & [0.005, 0.058] \\ \end{tabular}
\end{table}
Table A9: Psychosocial categories positively correlated (p<0.05) with _share_ in **Hollywood**. p-values were corrected using Benjamini-Hochberg correction. The categories are arranged in decreasing order of correlation.




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\begin{table}
\begin{tabular}{p{142.3pt}|p{85.4pt}} \hline \hline Theme & Examples from cluster \\ \hline Stealing Poverty Incompetence & shamelessness and taking money, lying and misusing money, Stealing and bribery not having money, being poor, living in poor conditions not fulfilling responsibilities, not meeting expectations or making a mistake, Failure or perceived incompetence \\ Promiscuity Inappropriate social behavior Disobedence Parent-related Marriage-related Immodesty Informage-related Irmonetely & being promiscuous, Having a love affair, shameless behavior and expressing love not living up to someone’s expectations, being disloyal, Being rude and ungrateful household \\ Homiscuity Inappropriate social behavior Disobedience Parent-related Marriage-related Immodesty Informationage-related Irmonetely & not standing up for his mother, not taking care of his father, not taking responsibility for his son \\ ComorbidRoles Gender Roles & wearing inappropriate clothes, inappropriate behavior nudity in public, behaving indecently in public \\ Exausal Harassment Cowardice Alcoholism Non-conformity Not conforming to gender norms, breaking gender norms, behaving inappropriately according to societal norms & here daughter, going against her husband \\ Illlegal activities Bitrayal Back of accountability Disrespect Disrespect & assaulting a girl, physical assault on women, sexual assault \\ ComorbidRoles Group & lack of courrage, cowardice and inability to stand up for oneself, lack of pride and integrity \\ ComorbidRoles Group & excessive drinking, inappropriate behavior due to alcohol, drinking and irresponsible behavior \\ ComorbidRoles Group & not conforming to gender norms, breaking gender norms, behaving inappropriately according to societal norms \\ Illegal Activities Betrayal Lack of accountability Disrespect & engaging in criminal activities, Being a criminal, committing illegal acts Retrayal Or dishonesty, betrayal and infidelity, Deception/Betrayal \\ ComorbidRoles Group & not taking responsibility and blaming others, wrongdoing without remorse, not acknowledging wrongdoing \\ ComorbidRoles Group & distrespecting others, dissrespecting an authority figure, Disrespectful and inappropriate behavior \\ ComorbidRoles Group & causing harm to others, committing harmful deeds, causing trouble and endangering others \\ ComorbidRoles Group & lying and hiding information, Lying or deceit, Deception/Not being truthful inappropriate sexual behavior inappropriate advances and comments, inappropriate behavior towards a young girl, inappropriate language and behavior \\ Social Etiquette Privacy-related Irregularly & Being humiliated in public, being disrespected and belittled, being mocked and treated sharefully invading personal space, invading someone’s privacy, intrusion of personal space, trespassing/invading personal space Accusation Family norms & \\ ComorbidRoles Group & accused of wrongdoing, being accused of something, being accused of infidelity Shrespecpreting family, causing harm and shame to family, causing difficulty and shame to family \\ ComorbidRoles Group & perceived shamelessness, being perceived as shameless, feeling embarrassed and ashamed \\ \hline \hline \end{tabular}
\end{table}
Table A11: Themes (\(n=26\)) and top-3 examples for clusters obtained after agglomerative clustering of shamelerated dialogues from Bollywood and Hollywood. The cluster _Lack of shame_ was removed since the reason behind shame was not evident. Clusters _Lack of accountability_ and _Inappropriate social behavior_ were merged together (and named “Lack of accountability”) due to overlapping normative expectations.





\begin{table}
\begin{tabular}{l|c|c|c} \hline \hline Theme (Shame-related norms) & Total Samples & Hollywood & Bollywood \\ \hline Lack of Accountability & 802 & 185 & 617 \\ Accusation & 70 & 0 & 70 \\ Alcoholism & 98 & 18 & 80 \\ Betrayal & 262 & 26 & 236 \\ Cowardice & 209 & 51 & 158 \\ Disobedience & 216 & 49 & 167 \\ Disrespect & 246 & 26 & 220 \\ Family norms & 203 & 24 & 179 \\ Gender roles & 221 & 17 & 204 \\ Harm & 225 & 64 & 161 \\ Illegal activities & 253 & 27 & 226 \\ Immodesty & 311 & 58 & 253 \\ Incompetence & 342 & 83 & 259 \\ Lying/Deception & 222 & 53 & 169 \\ Marriage-related & 179 & 25 & 154 \\ Non-conformity & 208 & 28 & 180 \\ Parent-related & 263 & 34 & 229 \\ Poverty & 414 & 108 & 306 \\ Privacy-related & 102 & 14 & 88 \\ Promiscuity & 595 & 89 & 506 \\ Inappropriate Sexual behavior & 407 & 33 & 374 \\ Sexual Harassment & 232 & 24 & 208 \\ Social Etiquette & 417 & 97 & 320 \\ Stealing & 351 & 49 & 302 \\ \hline Total & 6848 & 1182 & 5666 \\ \end{tabular}
\end{table}
Table A13: Distribution of reasons (shame) across manually labeled clusters. A total of twenty-six clusters were generated with distance=5. Duplicates were removed for clustering. One cluster with generic reasons ( such as phrases “lack of shame”) was removed and two clusters with similar reasons (related to accountability) were merged. Finally, 24 clusters were considered. The total is slightly more than numbers in Table 3 since a reason could be mapped to multiple clusters.





\begin{table}
\begin{tabular}{l|c|c|c} \hline \hline Theme & Statistic & p-value \\ \hline Inappropriate Sexual behavior & 5.0380 & 0.00001 \\ Gender roles & 3.8262 & 0.0006 \\ Betrayal & 3.2044 & 0.001 \\ Illegal activities & 2.8258 & 0.005 \\ Disrespect & 2.8284 & 0.005 \\ Sexual Harassment & 2.8358 & 0.005 \\ Promiscuity & 1.5553 & 0.1 \\ Accusation & 3.8411 & 0.0006 \\ Stealing & 1.6798 & 0.09 \\ Parent-related & 1.8961 & 0.06 \\ Family norms & 2.0813 & 0.04 \\ Non-conformity & 1.4724 & 0.1 \\ Marriage-related & 1.1817 & 0.2 \\ Privacy-related & 0.9518 & 0.3 \\ Alcoholism & -0.2920 & 0.8 \\ Immodesty & -0.6634 & 0.5 \\ Disobedience & -2.1438 & 0.03 \\ Lying/Deception & -2.6508 & 0.008 \\ Cowardice & -2.7746 & 0.006 \\ Incompetence & -3.5187 & 0.002 \\ Social Etiquette & -3.3461 & 0.002 \\ Harm & -4.5140 & 0.00009 \\ Poverty & -4.9028 & 0.00002 \\ Lack of accountability & -4.6312 & 0.00007 \\ \hline \hline \end{tabular}
\end{table}
Table A15: Barnard Exact test with Yates correction for testing statistically significant occurrence of shame-related themes in Bollywood and Hollywood.





\begin{table}
\begin{tabular}{l|c|c} \hline \hline Theme & Statistic & p-value \\ \hline Ethnolinguistic Identity & 9.6185 & 0.0000 \\ Son’s Achievements & 5.8025 & 0.0000 \\ Bravery & 5.0368 & 0.0000 \\ Family Honor & 4.7046 & 0.0000 \\ Nation & 5.1183 & 0.0000 \\ Wedding & 5.3898 & 0.0000 \\ Resilience & 1.4959 & 0.1370 \\ Justice & 0.1441 & 0.8963 \\ Family Roles & -0.2560 & 0.8024 \\ Physical Appearance & -0.6420 & 0.5223 \\ Winning & -2.8913 & 0.0038 \\ Achievement & -3.5425 & 0.0004 \\ Self-identity & -4.9482 & 0.0000 \\ Doing the ”right” thing & -10.4936 & 0.0000 \\ Duty & -8.5488 & 0.0000 \\ \hline \hline \end{tabular}
\end{table}
Table A16: Barnard Exact test with Yates correction for testing statistically significant occurrence of pride-related themes in Bollywood and Hollywood.