Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia (2022)

Abstract: While neural networks demonstrate a remarkable ability to model linguistic content, capturing contextual information related to a speaker’s conversational role is an open area of research. In this work, we analyze the effect of speaker role on language use through the game of Mafia, in which participants are assigned either an honest or a deceptive role. In addition to building a framework to collect a dataset of Mafia game records, we demonstrate that there are differences in the language produced by players with different roles. We confirm that classification models are able to rank deceptive players as more suspicious than honest ones based only on their use of language. Furthermore, we show that training models on two auxiliary tasks outperforms a standard BERT-based text classification approach. We also present methods for using our trained models to identify features that distinguish between player roles, which could be used to assist players during the Mafia game.

## introduction correct interpretation of language must take into account not only the meaning of utterances, but also characteristics of the speaker and the context in which their utterances are produced. modeling the impact of this context on language is still challenging for nlp systems. for example, differences in language identification accuracy, speech recognition word error rates, and translation quality have been observed on the basis of attributes such as a speaker's gender, race, dialect, or role @xcite @xcite . moreover, these systems systematically underperform on data generated by those in the minority, having implications for the ethics and fairness of using these technologies. ## background & related work the game of mafia is particularly well-suited for the goal of determining whether the deceptive participants in a conversation can be identified from the contents of their utterances. ## dataset a total of 460 english-speaking participants based in the united states were recruited from amazon mechanical turk using the experiment platform dallinger foot_0 . between 4 and 10 participants were recruited for each mafia game: 1 to 2 participants were designated mafia, and the rest were bystanders. forty-four of these mafia games are included in the final analysis. participants were paid @xmath05/hour. upon recruitment, participants were shown a consent form, per irb approval, followed by an instructional video and accompanying transcript describing how to play the text-based mafia game using an interface we developed (see appendix). after they completed a quiz demonstrating they understood the information, they entered a waiting room until the desired number of participants was reached. participants were then assigned a role (mafioso or bystander) and fake name, after which they began playing the game. the game dynamics were as follows. each mafia member was aware of the roles of their fellow mafia members and thus, by process of elimination, knew the roles of the bystanders. however, the bystanders did not know the true role of anyone else in the game. the goal of the mafia was to eliminate bystanders until the number of mafia was greater than or equal to that of the bystanders. the goal of the bystanders was to identify and eliminate all of the mafia members. since the incentive structure was set up such that bystanders benefited from true beliefs about who the mafia members were, whereas mafia members benefited from false beliefs, bystanders were thus motivated to be honest actors, whereas mafia members were motivated to m and b denote the mafioso and bystander classes, respectively, while t denotes the total number for both groups. the last row shows the distribution of roles among the players with no utterances throughout the game. note that nearly all of the no-utterance players are bystanders. from these experiments, we collected a dataset consisting of both mafia and bystander utterances over the course of each game, as well as the participants' voting behavior. dataset statistics appear in upon further inspection of the data, we can observe several strategies used by mafia members to deceive bystanders: 1. mafia members may suggest that there is not enough information to decide on who to eliminate, despite their knowledge of everyone's roles (eg. "should we wait to eliminate someone?" / "it's a little early to tell." / "it's a shot in the dark."), 2. mafia members may raise suspicion about another player, despite knowing that said player is a bystander (eg. hmm ok analyzing this conversation....i think bianca was a little to flippant in how she was like "sucks to be andrew" haha / i'm going to vote bianca. she's so casual with life and death), 3. mafia members may invent a false motive and assign that motive to another player, despite knowing that the player is a bystander (eg. it might be jonathan kim... killing off erin who accused him "yesterday"). ## approach given our mafia dataset, there are several tasks that one might address, for example, predicting participants' daytime voting behavior or generating mafia members' nighttime dialog. as our aim is to identify deceptive actors, however, we focus on predicting participants' roles, i.e. bystander or mafioso. due to the asymmetry in the knowledge available to each group and the goals which incentivize bystanders to increase true belief and mafia members to reduce it, the bystanders are said to take on an honest role in the game, whereas the mafia members take on a deceptive role. to focus on the relationship between language and deception, we ignore voting behavior and consider just the daytime dialog in the game, as only the mafia members were able to converse during the nighttime. as shown in to investigate whether linguistic information can be used to identify players' roles, we train and evaluate classifiers that predict the role of a particular player. since we have a small dataset, we chose to fine-tune pre-trained transformer models rather than train them from scratch @xcite . to predict the role for a player p, we construct an input representation r(c, p) of the full game dialog c that encodes the player of interest p. we develop three approaches which differ in both the dialog representation function r and the modeling approach. ## experiments we train three fine-tuned models on the corpus of mafia game records and compare their performance to a random baseline. the specifications for the baseline and models can be found below, and the results are shown in ## discussion the decoding ability of the gpt-2 model provides us a more straightforward way to understand what the model has learned. given a prompt sentence, we can use our fine-tuned gpt-2 model to generate what a mafioso and a bystander would say. a few examples are shown in to confirm that our fine-tuned gpt-2 model captures some of the above features, we hand-label these features on 5 training games and 1 validation game, obtain each player's feature vector, and see whether there exists a correlation between the model's predicted p (m p |c) for validation players and the similarity of their feature vectors compared to the training set mafioso and bystander players. these feature vectors are shown in for each row, f1 to f4 give the feature vector u for the respective player. d(u) gives the similarity of u compared to the training feature vectors v 1 and v 2 . players are sorted by p red, the probability p (m p |c) given by our gpt-2 model, and t ruth gives the true label for each player (m for mafioso, b for bystander). since p8 and p9 have no utterances throughout the game, as per our heuristic, they are predicted to be bystanders with p (m p |c) = 0. ## limitations & potential risks we find that we are able to train models to help differentiate players with different roles in the game of mafia based only on their language use, as well as to identify features that may distinguish between these roles. we also noticed that the mafia were twice as likely to win the mafia game than were the bystanders. these findings lead us to believe that the bystanders may benefit from being provided hints based on our model's predictions and identified features. however, there are several ethical considerations in regards to using these methods. first, as our model is trained on this particular version of mafia, the specific models trained would not apply to other cases of deceptive language use. applying these models to out-of-domain data, or even adapting this general approach to new settings, may yield unexpected results. our experimental results only establish the effectiveness of our approach on the game of mafia. future work must evaluate these approaches on other deception detection tasks before they can be safely deployed in real-world scenarios. next, information that may aid bystanders in detecting deception may also aid mafia members in being deceptive. though mafia members may attempt to use it for this purpose, because our model is trained to increase true belief, which is directly in line with the bystander goal to identify the truth and against the mafia goal to obscure it, our approach is inherently more useful to bystanders. however, since the models we evaluate are far from perfectly accurate, there is a risk that users using these models for hints would rely too much on their output and thereby be misled. more work should be done to increase the model's performance in order to mitigate this risk. ## conclusion how one uses language depends not only on the content they wish to convey, but also on the context within which they convey it, and speaker attributes such as conversational role contribute to such context. in this work, we leveraged an environment for which roles are explicitly labelled in order to make progress toward the task of deception detection, an essential task to protect users in our increasingly virtual world.