Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Tasks (2023)

Abstract: Dialogue systems capable of social influence such as persuasion, negotiation, and therapy, are essential for extending the use of technology to numerous realistic scenarios. However, existing research primarily focuses on either task-oriented or open-domain scenarios, a categorization that has been inadequate for capturing influence skills systematically. There exists no formal definition or category for dialogue systems with these skills and data-driven efforts in this direction are highly limited. In this work, we formally define and introduce the category of social influence dialogue systems that influence users’ cognitive and emotional responses, leading to changes in thoughts, opinions, and behaviors through natural conversations. We present a survey of various tasks, datasets, and methods, compiling the progress across seven diverse domains. We discuss the commonalities and differences between the examined systems, identify limitations, and recommend future directions. This study serves as a comprehensive reference for social influence dialogue systems to inspire more dedicated research and discussion in this emerging area.

## introduction consider a human user who signs up to interact with a persuasive dialogue system that motivates for engaging in physical exercise. the system: 1) uses social cues like small talk and empathy, useful for providing continued support, and 2) employs persuasive strategies to convince the user who, at least in the short-term, is reluctant to indulge in exercise. does such a system fit the definition of a task-oriented dialogue system that are traditionally designed to assist users in completing their tasks such as restaurant or flight booking @xcite )? although the system is task-oriented or goal-oriented per se, the task here goes beyond the traditional definition of assisting users, given the * equal contribution, * * co-supervised project possible misalignment between the goals of the system and the user. clearly, this system is also not open-domain @xcite . although conversations involve social open-ended interactions, there is still a concrete goal of persuading the user towards a healthier habit. scenarios similar to above are ubiquitous in everyday life, including games @xcite , social platforms @xcite , and therapeutic interactions @xcite . dialogue systems for these applications require a core function in human communication, that is, social influence @xcite , which involves influencing users' cognitive and emotional responses, leading to changes in thoughts, opinions, and behaviors through natural conversations. this goes beyond what is captured by traditional task definitions in the dialogue community which primarily focus on task completion and social companionship. despite numerous independent efforts in identifying and analyzing various social influence scenarios, there is a lack of common understanding around social influence in ai research which inhibits a systematic study in this space. further, data-driven efforts for dialogue systems in this space are highly limited. to this end, we introduce the category of social influence dialogue systems (section 2), providing a comprehensive literature review and discussing future directions. developing these systems holds importance in ai research for multiple reasons. tackling these tasks not only involves ai but also aspects of game theory, communication, linguistics, and social psychology, making them an ideal testbed for interdisciplinary ai research. most importantly, they reflect ai's general ability to consider their partners' inputs, tailor the communication strategies, personalize the responses, and lead the conversation actively. we design a taxonomy for existing social influ-ence dialogue datasets, studying their task structure (symmetric vs asymmetric) and context (local vs global). we also organize them by their domains: games, multi-issue bargaining, social good, e-commerce, therapy and support, argumentation, conversational recommendations, and miscellaneous tasks (section 3). we further design a taxonomy of existing methods, assisting readers to comprehend the progress and reflect on future directions. we organize them based on the system strategy, language generation, partner model, architecture, learning process, and the use of pretrained language models (section 4). finally, we identify key challenges and provide recommendations for future work (section 5). over the years, research in task-oriented and open-domain dialogues has benefited from a myriad of survey efforts @xcite @xcite . we instead focus on dialogue systems with social influence capabilities and present a thorough review across various domains. we hope that our work serves as a timely entry point for interested researchers to take this area further, inspiring dedicated effort and discussion on social influence in the dialogue community. ## social influence across diverse application areas we now illustrate social influence across numerous domains and application areas. in total, we curated 22 datasets from prior work that capture social influence in various forms, spanning 12 publication venues, 4 languages, and 7 application domains (see appendix a for details on the compilation process). in general, the datasets capture the following information about an interaction: the non-conversational context for the participants (e.g. negotiation preferences or other role-specific information), the conversation between them, and outcome assessment. optionally, some datasets also gather participant demographics and personality traits, utterance-level annotations, and subjective evaluations via post-surveys. to understand the structural similarities and differences between these datasets, we design a taxonomy with two primary dimensions: task structure (symmetric vs asymmetric), and context definition (global vs local). task structure captures whether the participant roles are defined in a sym-metric or an asymmetric manner. for instance, a typical multi-issue negotiation is symmetric, in the sense that both parties have their own preferences and goals based on which they actively try to reach a favorable agreement @xcite . on the other hand, a counseling session between a therapist and a patient is asymmetric, where the therapist attempts to emotionally support the patient by employing social influence skills @xcite . context definition relates to whether the input context before each interaction is defined globally or locally. for instance, the persuasionfor-good dataset globally defines the context of persuasion for charity donation, which is kept the same throughout @xcite . on the contrary, in a typical debate, although the rules are defined globally, the conversation topic and arguments are local and can vary for each conversation @xcite . we present this categorization in games: strategy games involve social influence dynamics of trust and deception. diplomacy captures deception in long-lasting relationships, where players forge and break alliances to dominate europe @xcite . catan revolves around the trade of resources for acquiring roads, settlements, and cities @xcite . the players have access to only a subset of resources that they would need, which encourages strategic influence and trade. multi-issue bargaining tasks (mibt): mibt is a tractable closed-domain abstraction of a typical negotiation @xcite . it is based on a fixed set of issues each with a predefined priority for each player, which essentially governs the goals of the players. if the priorities of the players align, this leads to competitive negotiations, where each party attempts to convince their partner with tradeoffs and persuasive arguments. if they don't, this allows cooperative interactions where the negotiators try to find optimal divisions that benefit everyone. dealornodeal @xcite involves negotiations over three issues: books, balls, and hats. other datasets define a more grounded scenario, such as symmetric casino @xcite negotiations between two campsite neighbors and asymmetric jobinterview @xcite name ( citation negotiations between recruiters and applicants. social good: social influence is critical for social good applications. the tactics must be personalized using knowledge that is both relevant and appealing. persuasionforgood @xcite involves asymmetric interactions led by a persuader who attempts to convince the other participant for charity donation by employing a variety of tactics. for instance, logical appeal uses reason and evidence to support the argument, while emotional appeal elicits specific emotions. e-commerce: these tasks are typically asymmetric. a buyer influences the seller towards a reasonable price, while the seller tries to maximize their own profit. an effective system must combine price-related reasoning with language realization. craigslistbargain @xcite involves openended price negotiations with rich influence strategies like embellishments, side offers, emotional appeals, and using world knowledge. another example is customer support interactions in antiscam dataset @xcite , where users defend themselves against attackers who try to steal sensitive personal information with convincing arguments. therapy & support: effective therapy using social influence aids in the treatment of mental disorders, and substance use disorders, along with changing undesirable behaviors like unhealthy diets. a counselor needs to be adaptive, personalized, should understand the core issues, and should facilitate a change in patient's perspective @xcite . in sms counseling, @xcite found that linguistic influence like pushing the conversation in the desired direction is associated with perspective change. similar scenarios were captured in other datasets as well @xcite . @xcite collected the motivational interviewing dataset where the goal is to elicit and explore the patient's own motivations for behavior change. empatheticdialogues (rashkin et al., 2019) captured empathetic support interactions, which has been associated with rapport and better task outcomes @xcite @xcite . argumentation: in addition to factuality and social proof, a convincing argument must also consider the intensity, valence, authoritativeness, and framing @xcite . tan et al. (2016) released the changemyview logs from reddit, involving discussions on numerous controversial topics. other datasets include debate dot org (ddo) debates on diverse topics (durmus and cardie, 2019), congressional proceedings (thomas et al., 2006), and court hearings (fornaciari and poesio, 2012; d.-n.-m. et al., 2012; ji et al., 2020). conversational recommendation: everyday scenarios naturally hold potential for influence via recommendations, for instance, a movie fan per-suading their friends to watch a movie that they adore. @xcite and @xcite collected movie recommendation datasets. instead of guiding the conversation towards a specific movie, the goal is simply to provide recommendations based on facts and personal experiences. nevertheless, they still provide interesting examples of scenarios that can involve social influence. miscellaneous: the target-guided dataset @xcite was constructed from the personachat corpus @xcite . instead of being openended, the target-guided scenario defines a concrete goal of naturally guiding the conversation to a designated target subject, thereby, making it a social influence setting. ## methodological progress having summarized the datasets that capture social influence, we now discuss the modeling approaches developed for social influence dialogue systems. most domains have seen efforts in analyzing human dialogue behaviors and their impact on task outcomes. examples include analyzing deception in games @xcite , the impact of persuasive strategies and dialogue acts on charity donations @xcite , cooperative and non-cooperative strategies in mibt @xcite , the use of emotion expression for predicting partner perceptions @xcite , and studying semantic categories of persuasive arguments on web forums @xcite . in addition, researchers have targeted various domain-specific subtasks that can be crucial for the eventual development of dialogue systems in this space. this involves research in lie detection methods @xcite , discourse parsing @xcite , strategy prediction @xcite , breakdown detection (yamaguchi et al., 2021), outcome prediction @xcite @xcite , and argument mining @xcite . research that directly targets the development of dialogue systems in this space is still nascent. among other challenges like limited cross-cultural diversity and relatively smaller dataset size, social influence dialogue settings pose a unique challenge: an average human often exhibits sub-optimal strategic behaviors in social influence tasks @xcite . this means that standard seq2seq approaches trained on these collected datasets using supervised learning are fundamentally insufficient for developing effective dialogue systems with influence capabilities. hence, prior work has put a special attention to the system strategy, employing different ways to model the strategy and language together. we design a taxonomy of methods developed for social influence tasks, assisting readers to comprehend the progress and reflect on future directions. we organize them based on the system strategy, language generation, partner model, architecture, learning process, and the use of pretrained language models. we present annotations for all the surveyed methods in ## discussion and recommendations past few years have seen an exciting progress in social influence dialogue systems. however, building sophisticated and practically useful systems remains a challenging endeavor. several limitations still exist that must be addressed. to guide future work, we now discuss the key challenges and provide our recommendations. need for unifying the efforts: one challenge in this space has been the lack of large-scale datasets for model training. social influence tasks are complex for crowdsourcing workers to understand and to participate in. hence, prior work used extensive instructions and tutorials, making the study expensive and time consuming @xcite . to address this, we recommend the researchers to aim for a more unified view of the efforts in social influence. first, this would encourage researchers to adopt the best practices from other social influence scenarios. for instance, most datasets miss out on user attributes like demographics and personality, which are crucial in social influence scenarios @xcite . most datasets also ignore the partner perception after the interaction is over. this can result in misleading conclusions about the model performance, where the models perform well objectively, but hurt the relationship with their partners, and thus, negatively impacting practical utility @xcite . secondly, a holistic outlook will promote transfer learning and domain adaptation. our taxonomy for datasets ( finally, progress in task-oriented and opendomain systems can inspire more unified modeling for social influence tasks involving multiple skills in the same interaction (e.g. a combination of negotiation and persuasion tactics as common in realistic scenarios). @xcite blend various open-domain tasks to address multiple challenges together (e.g., persona-based, knowledge-enriched, etc.). hosseini-asl et al. (2020) concatenate structured and unstructured data in task-oriented dialogues, and unify task-oriented dialogue system building to be a single sequence generation problem. future work should explore similar unified approaches for social influence settings as well, especially since these tasks follow a common conceptual foundation (figure 1 ), with similar evaluation and theoretical principles @xcite . to encourage this unified view, we encapsulate our insights from this survey effort in a theoretical framework, which is presented in appendix c. the framework covers key components for designing a social influence dialogue task, including system attributes, target audience, underlying modeling techniques, and evaluation mechanisms. theory integration: most modeling efforts are based on crowdsourced datasets. since crowdsourcing workers may not exhibit optimal strategies, supervised training on these datasets is fundamentally insufficient to build an effective system for applications like pedagogy (teaching social skills to students). unfortunately, this holds regardless of how system strategy and partner model are designed. further, using rl to optimize on objective rewards is also not expected to be enough to reliably learn complex influence capabilities, especially when the reward is restrictive. to address this, we recommend to tap into the vast amount of research effort in social sciences and psychology on building theories for social influence @xcite @xcite . instead of solely relying on the collected data, future work should consider leveraging fundamentals from this research to guide the dialogue policy. previous works have studied resistance to social influence @xcite @xcite . @xcite found that people resist persuasion differently depending on their beliefs, suggest-ing personalizing the social influence process. one can also employ the politeness theory @xcite and model the participants' face acts to better understand users in social influence contexts @xcite . ## conclusions we introduced the category of social influence dialogue systems that aim to influence their partners through dialogue. we presented a survey of the recent prior work in this space, compiling datasets and methods across diverse application domains. we pointed out key limitations in existing methodologies and proposed promising directions for designing more sophisticated systems in the future. our survey reveals that although substantial progress has been made, this is still an emerging research area. we hope our work inspires more dedicated interdisciplinary effort and discussion, which is necessary for making progress in this space.