Social Intelligence Data Infrastructure: Structuring the Present and Navigating the Future (2024)

Abstract: As Natural Language Processing (NLP) systems become increasingly integrated into human social life, these technologies will need to increasingly rely on social intelligence. Although there are many valuable datasets that benchmark isolated dimensions of social intelligence, there does not yet exist any body of work to join these threads into a cohesive subfield in which researchers can quickly identify research gaps and future directions. Towards this goal, we build a Social AI Data Infrastructure1, which consists of a comprehensive social AI taxonomy and a data library of 480 NLP datasets. Our infrastructure allows us to analyze existing dataset efforts, and also evaluate language models’ performance in different social intelligence aspects. Our analyses demonstrate its utility in enabling a thorough understanding of current data landscape and providing a holistic perspective on potential directions for future dataset development. We show there is a need for multifaceted datasets, increased diversity in language and culture, more longtailed social situations, and more interactive data in future social intelligence data efforts.

heading: 1 Introduction
text: “Data is a precious thing and will last longer than the systems themselves.”
— Tim Berners-Lee
As early as the 1920s, psychologists like Thorndike (1921) and Hunt (1928) considered social intelligence to be a distinct branch of intelligence that underlies all successful human interpersonal relationships. Many researchers now argue that social intelligence is a prerequisite of humanlike Artificial Intelligence (Kihlstrom and Cantor, 2000; Erickson, 2009; Del Tredici et al., 2019; Radfar et al., 2020; Hovy and Yang, 2021; Williams et al., 2022). However, existing work still lacks a
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precise yet holistic definition for social intelligence in AI systems (Silvera et al., 2001).
Prior studies define social intelligence by its cognitive aspect, or the ability to understand others (Barnes and Sternberg, 1989), while others emphasize the behavioral component by defining it as the ability to interact with other people (Hunt, 1928; Ford and Tisak, 1983). Both dimensions are relevant but incomplete, as social intelligence is multifaceted (Marlowe, 1986). Besides the narrow definitions, related empirical efforts, such as dataset collection and model development, also have isolated focuses and only address a single aspect of social intelligence (Fan et al., 2022). Therefore, there exists a pressing need for a holistic and synthesized definition for social intelligence to create an organized space for existing datasets. Without such organization, it is difficult to identify overar-
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ching research questions and emerging trends for future exploration. Besides, although various social intelligence datasets have been proposed, the lack of data organization creates barriers for researchers to gain insights from previous work.
In light of this, we establish Social Intelligence Data Infrastructure, which consists of a comprehensive taxonomy for social intelligence and an organized data library of 480 NLP datasets (see Figure 1) to structure current data efforts and navigate future directions. The taxonomy (§2) formally defines various aspects of social intelligence, to introduce standardization and comprehensiveness to the definition of social intelligence in AI systems. The data library (§3.1) maps crawled datasets to different categories in our taxonomy, to provide structures for existing datasets. The taxonomy and data library can collectively aid researchers to identify existing dataset gaps and guide future dataset development for social intelligence.
Moreover, we demonstrate how the proposed Social Intelligence Data Infrastructure can be applied to gain insights into future dataset development with the following contributions:
• We perform distributional and temporal analysis (§3.3) to highlight overlooked categories and uncover emerging trends.
• We evaluate the zero-shot performance of Large Language Models (LLMs) (§4) on various social intelligence aspects defined in our taxonomy, to shed light on current models’ capabilities and limitations.
• Finally, guided by the analysis and evaluation results, we discuss unfilled gaps and future directions for NLP dataset efforts on social intelligence (§5). We identify a need for multifaceted datasets, better diversity in language and culture, more long-tailed social situations, and more interactive data.

heading: 2 Social AI Taxonomy
text: To introduce a standardized and comprehensive definition of social intelligence, we propose Social AI Taxonomy, to capture diverse dimensions identified in previous work. As shown in Figure 2, different from previous categorization which is thematic with a focus on social understanding (Choi et al., 2023), our taxonomy considers the social interaction component and is hierarchical with three distinct types of social intelligence based on past
literature: (1) cognitive intelligence, (2) situational intelligence, and (3) behavioral intelligence.
As identified in social cognitive theory (Bandura, 2009), these three intelligence types mutually influence each other to shape human behaviour. Prior work (Barnes and Sternberg, 1989; Kosmitzki and John, 1993) shows that these three intelligence types can comprehensively cover different factors and dimensions under social intelligence. Now we detail each intelligence type below.

heading: 2.1 Cognitive Intelligence
text: Definition. Cognitive intelligence refers to the use of verbal and nonverbal cues (Hunt, 1928) to understand others’ mental states (Barnes and Sternberg, 1989). These include cold cognition about intents and beliefs, as well as hot cognition about emotions (Roiser and Sahakian, 2013), so our taxonomy decomposes cognitive intelligence into knowledge about intents, beliefs, and emotions.
Significance. Cognitive intelligence includes the prerequisites for effective communication (Apperly, 2010) and many concrete NLP tasks (Langley et al., 2022). Task-oriented dialogue requires intent recognition (Jayarao and Srivastava, 2018; Wu et al., 2023), and mental health support demands an understanding of emotions (Peng, 2021; Singh and Srivastava, 2023). Broadly, Theory of Mind is a fundamental module in both both human (Premack and Woodruff, 1978) and artificial social intelligence (Rusch et al., 2020) that underlies downstream skills like stance awareness (see the left pillar of Figure 2).

heading: 2.2 Situational Intelligence
text: Definition. Situational intelligence refers to an awareness of the social context (Derks et al., 2007) and how this context informs the other pillars of cognition and behavior (reciprocal interactions in Figure 2). The literature tells us that social context includes: the social event itself (Sap et al., 2019b), as well as social and moral norms (Ziems et al., 2023a), culture and speaker information (Tigunova et al., 2021), all included in the Taxonomy.
Significance. Situational intelligence makes use of social context as the glue to bind the cognitive intelligence of mental states (§2.1) with a set of appropriate behaviors (§2.3), and serves as the foundation for decision making (Endsley, 1990). Given its centrality and its clear manifestations in decision making, situational intelligence has been a standard locus for social intelligence tests (Lievens
and Chan, 2017; Hunt, 1928). The social context spans not only interpersonal factors like tie strength (Sap et al., 2019a; Cristani et al., 2011), but also cultural differences like those between high and low-context cultures (Hall and Hall, 1987; DeVito, 2016), and navigating these differences is essential for cross-domain and cross-cultural communication (Wawra, 2013). Studies have shown that incorporating these factors can lead to significant performance improvements in NLP systems (Rahimi et al., 2018; Wu et al., 2021).

heading: 2.3 Behavioral Intelligence
text: Definition. Behavioral intelligence refers to skills of successfully communicating and acting in a manner to attain social goals (Ford and Tisak, 1983) through (1) information sharing (Zhang et al., 2020), (2) social influence (Turner, 1991; Cialdini and Goldstein, 2004; Chawla et al., 2023; Weinstein, 1969), or (3) maintaining interpersonal relationships (Vernon, 1933; Moss and Hunt, 1927) via open-domain conversations (Huang et al., 2020). Our Taxonomy is organized around these three foci.
Significance. Behavioral intelligence has direct ramifications for human-human and humanAI interactions (see the right pillar of Figure 2). Task-oriented dialogue systems (Zhang et al., 2020) and collaborative AI partners (Bara et al., 2021) depend on successful information sharing, while other applications require engaging and personalized open-domain chit-chat (Zhang et al., 2018a). The capacity for social influence becomes relevant in human-AI teams (Bansal et al., 2021), where such skills can make use of advances in explainable AI systems (Angelov et al., 2021). Across these
diverse applications, systems need to be equipped with social-behavioral skills like empathy (Rashkin et al., 2018), persuasion (Hunter et al., 2019), and transparency to build trust (Liao and Sundar, 2022).

heading: 2.4 Challenges in Measuring Intelligence
text: The three pillars of social intelligence are not mutually exclusive, nor are they readily isolated in social life, since they coordinate through reciprocal interactions (Figure 2). A situationally intelligent agent can better express cognitive intelligence, using cues from the social context to infer the mental states of others. The converse is also true that, by considering others’ mental states, one can understand her role in shaping the social situation. Both intelligence can facilitate effective social actions.
Because of its dynamic nature, social intelligence may be outside the scope of what AI engineers can benchmark with any single static dataset. This is especially true as situational and behavioral pillars themselves are not static, but refer rather to an agent’s ability to adapt into a social equilibrium. In our analysis and discussion, we will consider the degree to which benchmarks can reflect this dynamic nature, and whether existing datasets measure more than one type of social intelligence. For example, the COBRACORPUS (Zhou et al., 2023b) requires cognitive and situational intelligence to reason about offensive intents in different social contexts. Finally, we make suggestions for the design of data resources and the future of social AI.

heading: 3 Current Social NLP Data Landscape
text: With what granularity can existing data resources help researchers train and evaluate the core pillars
of social intelligence in AI systems? How holistic is the landscape, and how sufficiently integrated are these pillars in the literature? To answer these questions, we leverage the Social AI Taxonomy to categorize existing NLP publications into a library of relevant datasets.

heading: 3.1 Data Library Construction
text: We use ACL Anthology data2 crawled by Rohatgi et al. and Held et al. (2023), and set our time scope from year 2001 January to 2023 October as there are few datasets before 2001. We automatically collect social intelligence dataset papers by filtering titles and abstracts with keywords related to both (a) social intelligence and (b) dataset development (see Appendix D). The smaller size of this filtered pool allows us to manually curate papers, removing any surveys or irrelevant works on model development, annotation schemes, or annotation tools, which results in a curated set of 480 papers. For these papers, we scraped useful metadata, like title, url and publication year. We discuss the technically infeasibility of an exhaustive library in §6.

heading: 3.2 Metadata Annotation
text: We map the papers in our data library to the Social AI Taxonomy. Two authors reviewed the content in the paper and annotated them with type of intelligence and §2 subcategory (Cohen κ = 0.86 cognitive; 0.80 situational; 0.87 behavioral). The annotation is based on the main focus of the dataset. For example, if a work collects interactive dialogues solely for intent recognition purpose, we will classify it as cognitive intelligence instead of behavioral intelligence. Constructing the data library 3 illustrates how our theoretical taxonomy can be practically useful to organize datasets focusing on different aspects of social intelligence. On top of that, we also annotate other important attributes for each dataset by reviewing the paper contents: we annotate NLP Task, Data Source (where the data was collected from), Annotation Strategy (how the labels were obtained), Generation Method (if the text comes from human, AI or both), Data Format (e.g. tweet, news article, dialogue etc.), Language, Modality, and Public Availability of the data.
2https://aclanthology.org/ 3Data library

heading: 3.3 Social NLP Data Landscape Highlights
text: By visualizing the distribution and temporal trend of the datasets in the data library, we obtain insights about the past and current NLP paradigm for dataset development on social intelligence. We discuss key results in this section and put more detailed analyses in Appendix A.
Topic Distribution Figure 3 shows most of the social NLP datasets focus on the cognitive aspect of social intelligence (64.2%), followed by behavioral aspect (22.7%), and least of all, its situational aspect (3.8%). Only a small set of datasets (9.4%) span multiple intelligence types. We also visualize a detailed breakdown of different factors within each type. For cognitive and behavioral intelligence, papers are balanced across the respective subcategories. For situational intelligence, most datasets measure knowledge of speakers involved in the dialogue such as their demographics and social relations, and there are very few datasets on moral values and social norms.
Temporal Topic Shift From Figure 4, we can better understand the temporal variation and shift of focus over time for each type of intelligence. We can see the onset of study on situational intelligence is later (2008) than the other two types (2001). For all three intelligence, the task of focus has become more specific and nuanced over the years. For example, early work on cognitive intelligence focused on general dialogue act classification
but recent studies are about more nuanced and challenging intents beyond literal meaning like sarcasm and irony understanding (Alnajjar and Hämäläinen, 2021; Frenda et al., 2023). Literature on behavioral intelligence began with tasks to identify effective or powerful written communication (Tan and Lee, 2014), and moved to more specific tasks like highquality persuasive arguments for particular forms of negotiation (Ng et al., 2020; Chawla et al., 2021).
Interactive vs Static Data We classify the data formats of surveyed datasets into two broad categories – interactive and static. Interactive data are those with information exchange like social media threads and daily conversations. Static data include (1) self-contained and topically focused texts written for a general audience (e.g. news and books) and (2) those that are part of information exchange but have no prior or subsequent context (e.g. a single post on Twitter or an utterance in a conver-
sation). The difference in data format can affect the ability for language model to acquire social intelligence (Sap et al., 2022). Figure 5 shows that for cognitive aspects in particular, but not for other pillars, there are significantly more static datasets than interactive ones, which quantitatively confirms the trend observed by Sap et al. (2022). Moreover, within the interactive datasets, the proportion of multi-party modeling is small (18.4%).
Use of AI There are increasing number of works adopting AI for generating and annotating datasets related to social intelligence (before 2015: 3; after 2015: 32). We find that degree of adoption of AI for generation is higher than annotation. In recent work, researchers outsource generation completely by generating contents purely using AI (Zhou et al., 2023b) or simulating conversations between AI and AI (Lee et al., 2022). On the other hand, use of AI in annotating social intelligence data still remains in a hybrid stage (Jo et al., 2020) and AI usually plays a part in annotating simpler high-level components like themes (Maës et al., 2023).

heading: 4 Model Performance
text: Now we use our Social AI Data Infrastructure to evaluate current LLMs’ performance on social intelligence and gain insights on models’ strengths and limitations, shedding light on aspects which future social intelligence datasets need to address.

heading: 4.1 Experimental Setup
text: Dataset selection. For each of our taxonomic categories, we select two representative datasets, one simple and the other more challenging. Challenges
arise from factors like nuancedness (e.g., sarcastic intents), task granularity (e.g., emotion detection with more fine-grained classes), data scarcity (e.g., stance detection in the economic domain), long-tail data distributions (e.g., moral exception), and challenging data formats (e.g., persuasion in a dialogue setting) (see Appendix E). We select open-sourced and widely-used datasets with high citations for each category. Testing the model on the entire dataset can be computationally expensive and time-consuming so we adopt class-stratified sampling of 100 to 150 instances from the original test set (if available). We evaluate LLMs’ zero-shot performance on sampled instances, and follow the recommended practice of prompting as described by Ziems et al. (2023c). Metrics. For classification tasks, we choose F1 as the metrics. For generation tasks, we present both the original human response and the LLM response to human annotators, and calculate the preference percentage. As such, we can better understand current social capability of language models, in both absolute terms against the ground truth and relative terms compared to human.
Human performance. For classification, we report the best and average F1 scores for responses from three Amazon Mechanical Turk workers per test instance. For generation tasks in which we compute preferences, we define the average human performance as 50%, without defining the best human performance.

heading: 4.2 Result Analysis
text: Main Results. From Table 1, we can see that LLM performs better on simpler datasets than more nuanced ones. For example, compared to straightforward query intent recognition (95.0 F1), the best performing LLM (GPT-4) struggles more with identifying the intended sarcasm (67.3 F1) when people convey an opposite meaning from what they literally said. Moreover, uncommon tasks with fewer datasets are more challenging, such as stance detection in the economic domain (most stance detection data is for political domain (Küçük and Can, 2020)), moral exceptions, language inference under different cultures, and so on. With more fine-grained definitions on labels, LLMs have better performance in classification as seen from a
higher F1 on GoEmotions than SemEvalT1 with more emotion classes defined. Additionally, more social context in the data can also result in better performance: for instance, they achieve a higher F1 on the CICERO dataset with both social situation description and dialogue data, than the SocialIQa dataset with only a simple description.
Performance Comparison with Humans. Table 1 shows that, for every task there exists at least one LLM surpassing the average human performance. However, LLMs perform worse than best human performance on most tasks on cognitive and situational intelligence. The gap between LLMs (e.g. GPT-4) and the best human performance is higher for more nuanced tasks (iSarcasm: 23.6 vs. SNIPS: 2.5), task in scarce domains (WTWT: 25.9 vs. SemEvalT6: 7.5) and more long-tailed situations (MoralExceptQA: 43.2 vs. NormBank: 10.9). On the other hand, LLMs exceed average human performance on behavioral intelligence tasks with percentage preferred more than 50% on all tasks. However, percentage preferred for LLMs (e.g. Claude) is lower in more dynamic and interactive situations (e.g. applying persuasion in dialogue: 74.3 vs. writing persuasive arguments: 93.6) with more constraints (e.g. with persona constraints: 64.0 vs. without persona constraints: 74.7) given. More qualitative analysis about human and LLMs performance is provided in Appendix C.
Multiple intelligence. LLMs in real-life social applications usually require multiple intelligence (e.g. interpreting intents under different cultural backgrounds) but they are still lacking in performance (CulturalNLI: 65.0). Table 1 shows they perform well for individual modules, so systems can utilize LLMs for individual modules which LLMs do exceptionally well in and combine them organically to build a strong holistic system (e.g. combine emotion recognition and positive reframing components for a counseling system).

heading: 5 Recommendations for the Future
text: From analyzing current data landscape (§3.3) and evaluating LLMs’ performance (§4), we unveil the most challenging aspects of social intelligence that remain unaddressed by existing data resources or model capabilities. Guided by insights from our results, we discuss possible future directions for dataset development below.

heading: 5.1 Recommendations for Data Content
text: Future datasets should focus more on specific, nuanced, and long-tailed social situations. The LLMs we evaluated fell short on nuanced tasks like sarcasm and moral exceptions. As human expressions are diverse and subtle, and vary with complex linguistic contexts (Cruse, 2004), it is crucial to model the contextual complexity to address the challenge of ambiguity in language. Long, multi-party interactions that go beyond conventional dialog or small groups are also critical for developing more sophisticated social intelligent systems that account for variation in discourse structure and potential conflicts between individual and group objectives, which produce novel social equilibria.
As shown in Figure 3, most datasets focus on just a single intelligence type. However, different intelligence types are not isolated but rather reciprocal in real-world applications (Fan et al., 2022). Thus, there should be more multifaceted datasets encompassing multiple intelligence types to promote holistic benchmarking.
Moreover, there is a need for better coverage on language, culture, countries, user groups, and domain, as suggested by Figure 7 and LLMs’ worse performance on stance detection in economic domain and culturally aware language inference. This will help models better generalize across populations and social contexts. We need large and diverse datasets that can reflect the linguistic and cultural diversity of different user groups, which ensures that the models recognize and respond to social cue variations specific to different communities. Consider standard languages or mainstream user groups, versus low-resource languages and dialects, and vulnerable populations such as older adults or people with cognitive impairments.

heading: 5.2 Recommendations for Data Structure
text: There is a need for higher interactivity in both data and evaluation. The field has an abundance of static resources and fewer interactive datasets. Our results show that dialogue context improves performance, and others have also argued that without interactivity, language models may be unable to fully develop key pillars of social intelligence, including Theory of Mind Sap et al. (2022); Bender and Koller (2020). In this interactive settings, there will be an opportunity for a reduced focus on performance metrics like accuracy, and an increased focus on explainability, with socially intelligent AI
systems that understand and can explain the factors that underlie their behaviors.
There can be significant shifts in people’s values, beliefs and perspectives over time with societal changes like social movements, generational shifts and globalization. As a result, what social intelligence entails is constantly evolving. Thus, data should undergo dynamic evolution to accurately capture social intelligence over different timeframes. Researchers can also consider a dynamic and flexible framework to allow future customization and extension (Zhou et al., 2023a).
Additionally, humans communicate using various modalities beyond language, such as gestures and facial expressions. Future datasets are encouraged to incorporate multiple modalities to help AI systems develop a more accurate and well-rounded understanding of social contexts and social cues, leading to increased social intelligence.

heading: 5.3 Recommendations for Data Collection
text: Historically, social AI datasets have drawn heavily on randomly sourced crowdworkers who annotate datasets that have been scraped from social media or other online sources. There are at least three reasons why this paradigm will need to be replaced.
The first concern is the issue of representation. A random sample of crowdworkers may not contain a fair representation of diverse viewpoints from a wide variety of sociodemographic backgrounds. Similar biases appear in randomly sampled social media data. Representation should extend beyond nationality to include diverse local regions, vulnerable populations, and people of different ages and genders. Both annotation and evaluation criteria should be designed in a way that accounts for sociolinguistic variation and considers diverging perspectives. Relatedly, crowdworkers may not be equipped to consistently identify subtle social cues. For this reason, we support increasingly interdisciplinary, expert annotation efforts in which domain experts such as linguists, psychologists, anthropologists, and sociologists work to annotate high-quality social AI data resources.
Second, by passively observing decontextualized data, annotators may be unable to fully understand the social context behind any observed behaviors. There may be frequent misalignment between the behaviors expressed in random internet data and the lived experiences of the annotators. This motivates a more active paradigm of dataset construction in which annotators participate in the social interac-
tion, and are thus de facto experts on its situational context, any operational norms and cultural expectations that govern their behavior, as well as their own cognitive factors like personally motivating beliefs, intents, and emotions. As an added benefit, such active construction will naturally produce data with a high degree of interactivity (see §5.2).
Third, we encourage the field to closely consider how to effectively leverage LLMs to create humanin-the-loop collaborative datasets, which applies both to the active generation of data previously mentioned and co-annotation of other social constructs (Li et al., 2023). Note that this differs from using LLMs to simulate synthetic social interaction data. In fact, we argue that it is still unclear whether simulation can produce high quality data with practical validity, since recent studies have shown caricatures and stereotypes in LLM-based simulations (Durmus et al., 2023; Cheng et al., 2023).
Last but not least, we call for the development of annotation tools to facilitate the collection, visualization and annotation of different constructs in social intelligence, to allow for easy plug-in to existing crowdsourcing platforms and to support reproducible data collection.

heading: 5.4 Recommendations for Data Ethics
text: Social AI datasets must be designed with ethical considerations, such as fairness, transparency, and privacy, to avoid perpetuating stereotypes or biases, and to respect user privacy. We envision that social AI dataset construction takes a community-centric approach where domain users co-design the tasks and data collection efforts with researchers (i.e., tasks of the community, by the community, and for the community), in addition to interdisciplinary collaboration among research fields. This process will also benefit from protocols, compliance guides, culture- or country specific data use agreements to address any legal and ethical issues for creating and maintaining social AI datasets.

heading: 6 Conclusions
text: We introduce Social AI Data infrastructure with a theoretically grounded taxonomy and a data library of 480 NLP datasets, which facilitates standardization of the social intelligence concept in AI systems and organization of previous NLP datasets. We also conduct comprehensive analysis on the data library and evaluate LLMs’ performance, offering insights on the current data landscape and future
dataset development to advance social intelligence in NLP systems. It enables curation of high-quality datasets and holistic development of social intelligence in the NLP field.
Limitations
Although we try to be comprehensive, the datasets in our data library are not exhaustive as it is practically impossible to capture all datasets on socially intelligence. Moreover, we only crawled datasets on ACL Anthology, which is not representative of the whole academic space. As such, our analysis is more on relative comparison in the NLP domain rather than interpretation of the absolute figure. We encourage future work to further extend and contribute to our initial data library. Moreover, since LLMs have been trained on a large number of data, there may be data leakage issue where LLMs have seen some datasets in our experiment, making the performance reported higher than their actual capability. On top of that, since our work focuses more on obtaining insights about future dataset design (data aspect) instead of testing LLMs’ social capability comprehensively (model aspect), we only select one simple and one nuanced dataset for each category for comparison purposes. Future work could leverage upon our infrastructure to design a comprehensive evaluation set for social intelligence to get insights on how models perform along each dimension of social intelligence.
Ethical Statement
This study has been approved by the Institutional Review Board (IRB) at the researchers’ institution, and we obtained participant consent with a standard institutional consent form. One ethical concern is that models will become more capable of undesirable outcomes like persuasive misinformation or psychological manipulation as they become more socially intelligent. There may also be concerns that skilled anthropomorphic models will come to replace humans. These can not only lead to loss of trust in users (Mori et al., 2012) but also harm users’ well-being (Salles et al., 2020). Our work proposes a standard concept and analysis the landscape and these risks are beyond the scope, but we acknowledge their presence and encourage future social AI data and systems to have clearer guidelines on the capabilities and limitations of AI systems to prevent deceptive and manipulative behaviours when advancing social intelligence.

heading: Acknowledgements
text: We are thankful to the members from SALT Lab for their helpful feedback. Minzhi Li is supported by the A*STAR Computing and Information Science (ACIS) Scholarship. Caleb Ziems is supported by the NSF Graduate Research Fellowship under Grant No. DGE-2039655. This work was partially sponsored by NSF IIS-2247357 and IIS-2308994.

heading: A Other Analysis
text: Perspective-Taking Perspective-taking is the act of considering an alternative point of view for the same situation, which is one aspect of social intelligence (Kosmitzki and John, 1993). Some work starts to pay more attention to two different perspectives which are intended and perceived point of views. For example, Oprea and Magdy (2019) points out the difference between intended and perceived sarcasm from the perspectives of the author and audience, which is often overlooked in previous work. Thus, they asked for self-reported annotations to capture the intended sarcasm. The same also holds for other factors like emotion, which can be intended emotion by the author (Kleinberg et al., 2020) or aroused emotion among the audience (Gambino et al., 2018).
Distribution of Data Sources Most datasets in our data library use social media as sources of data (see Figure 6). The prevalence of data collection from social media has experienced a significant surge from 2010. This might be due to an increase in the use of Twitter data (Baeth, 2019). In the meantime, relative proportions of traditional media like news and websites has experienced a decrease since then.
The second popular data source is previously built data resources. New datasets leverage and extend upon previous ones in cases like translating to low-resource language (Ramaneswaran et al., 2022), introducing new evaluation criteria (Peng et al., 2020) and adding new layers of annotation (Tigunova et al., 2021).
Language and Culture Representation Datasets surveyed in our data library covered up to 49 different types of languages. Figure 7 shows that majority of study (62.5%) uses English data to explore social intelligence and the number of such work is much higher than those in other languages. Moreover, there are more recent research efforts on code-mixing datasets about social intelligence, suggesting an increased representation of multilingual community (Chakravarthi et al., 2020a,b; Shetty, 2023).
Additionally, most datasets in the data library (97.9%) has unspecified cultural representation. However, the same sentence could have different meanings under different cultural contexts as social interpretations and social interactions vary from culture to culture. Therefore, there is a strong need for more future datasets with generations and annotations from different cultural backgrounds.
Overall, most of datasets contain textual content that is purely human-generated (95.0%) and manually labeled (98.1%). From Figure 8, we can see
there is an increasing trend in adoption of AI for generating and annotating datasets related to social intelligence. We can also see that number of work using AI for generation is more than those for annotation.
Incorporation of Different Modalities Because we only use crawled data from ACL Anthology, the majority datasets on social intelligence we surveyed are only in textual format. However, different modalities like image, audio and video can enhance learning of social intelligence with enriched social information embedded in other modalities.
More Open-sourced Community Figure 10 shows a promising trend where proportion of social intelligence datasets that are made available increases over the years. From 2014 onwards, the proportion of available has experienced a surge and there are more publicly available social intelligence datasets than unavailable ones from 2016 onwards. Unavailable social intelligence datasets usually contain sensitive information such as mental illness (Santos et al., 2020; Tasnim et al., 2023) and sexual orientation (Vásquez et al., 2023). Additional measures like anonymizing, dara encryption
and access control should be in place to protect data confidentiality while ensuring future work could have secure ways to use these data to advance research in sensitive domains.

heading: B LLM Inference
text: Model We run inferences using Claude-v2, GPT4-1106 and Llama2-13b models.
Prompt We follow the recommended practice of prompting as described by Ziems et al. (2023c). We design prompts in MCQ format, give instructions after the context and clarify specific social concepts when necessary.
Temperature Setting We set temperature to 0 for classification tasks to ensure consistency and 0.7 for generation tasks to allow some diversity.

heading: C Details of Human Evaluation
text: For interpretability, we also provide different criteria (see Table 2) for each task and collect human’s free-text explanation on top of an overall judgment. Below is the qualitative analysis for their free-text inputs for each dataset:
Daily Dialogue A better responses is more specific and thoughtful and goes beyond the straightforward question a bit to make a personal anecdote. Some machine generated responses are too artificially friendly and sympathetic.
PersonaChat A response is better if it expresses more information (information content), responds directly to the question (appropriateness, naturalness) and expands on simple facts with personal details (naturalness, engagement).
Positive Reframing A better response is more multifaceted and nuanced by acknowledging all aspects of the input text. It better acknowledges and preserves the original meaning. Some machine generated responses feel forced and rated as worse.
Counsel Chat Better responses are more detailed, address the complexity of the situation and provide more specific and actionable advice.
ArgumentsPairs Better arguments assert claims with evidence, provides specificity and contextualization of evidence and offer multiple complementary ideas that address different sub-concerns.
PersuasionforGood Better responses are more specific and have a clear call to action. They better express emotional (pathos) and logical (logos) appeals. They take a genuine interest in the listener.
In addition, we also collect crowdworkers’ perceptions and comments on whether they think the text is generated by machine or human.
We find that majority of people cannot correctly identify which response is generated by machine and a significant proportion choose the option ‘both generations are produced by human’. Thus, more regulation and transparency on who generates the texts are needed since it is hard for people to distinguish human and machine generated texts.
In general, they perceive more specific and thoughtful answers to be human generated and those brief and incomplete answers to be machine generated. Occasionally, some people hold an opposite view that more detailed and structured responses are from machine. This suggests for generation tasks, LLM has exceeded average human performance (see Table 1) and it has also been acknowledged in human’s perception for a certain proportion of population.

heading: D Keywords

heading: E Dataset Selection Criteria
text: We provide justifications for the choice of one simpler and one challenging dataset used for LLM probing for each subcategory.
Intent SNIPs dataset is about query intent classification while the iSarasm dataset involves identification of sarcastic intents. The latter is more challenging as it demands understanding of contextual complexity and language ambiguity which makes the task more nuanced.
Belief SemEval-Task6 is about stance detection for texts in the political domain, which is the dominant domain in stance detection. WTWT is a more challenging dataset as it consists of data from the economic domain, with limited previous data efforts in the area of stance detection.
Emotion GoEmotions dataset is more challenging as it has more fine-grained emotion classes (28 classes) than SemEval Task 1 (11 classes).
Social Situation CICERO dataset is more challenging as the social situation is implicitly reflected in a dialogue compared to the SocialIQa dataset where the social situation is explicitly described.
Social Norm NormBank contains mostly common scenarios in daily life and MoralExceptQA is more challenging as the situations include cases with exceptions for moral judgment (e.g. permissibility of cutting queue which is usually unacceptable in a really urgent context).
Chitchat PersonaChat is more challenging than DailyDialogue as it has additional persona constraints for dialogue response generation.
Persuasion and Therapy The less challenging datasets (ConvincingArguments and Positive Reframing datasets) contain static settings with singleturn generation whereas the more challenging ones (PersuasionforGood and CounselingChat) are for
interactive multi-turn settings where more contextual information needs to be taken into account during response generation.

heading: F Annotator Qualification
text: For selection criteria of human annotators, we require an accepted number of tasks higher than 500 and an approval rate higher than 98%. Crowdworkers who met this criteria will take a qualification round which contains exactly the same questions in the actual round. Their submissions for the qualification round were manually verified by the authors and qualified workers were given access to the actual round.

heading: Future Work
text: Therefore, there is a strong need for more future datasets with generations and annotations from different cultural backgrounds. Overall, most of datasets contain textual content that is purely human-generated (95. 0%) and manually labeled (98. 1%). From Figure 8, we can see
there is an increasing trend in adoption of AI for generating and annotating datasets related to social intelligence. We can also see that number of work using AI for generation is more than those for annotation. Incorporation of Different Modalities Because we only use crawled data from ACL Anthology, the majority datasets on social intelligence we surveyed are only in textual format. However, different modalities like image, audio and video can enhance learning of social intelligence with enriched social information embedded in other modalities. More Open-sourced Community Figure 10 shows a promising trend where proportion of social intelligence datasets that are made available increases over the years. From 2014 onwards, the proportion of available has experienced a surge and there are more publicly available social intelligence datasets than unavailable ones from 2016 onwards. Unavailable social intelligence datasets usually contain sensitive information such as mental illness (Santos et al. , 2020; Tasnim et al. , 2023) and sexual orientation (Vásquez et al. , 2023). Additional measures like anonymizing, dara encryption
and access control should be in place to protect data confidentiality while ensuring future work could have secure ways to use these data to advance research in sensitive domains