People often stick to their existing beliefs, ignoring contradicting evidence or only interacting with those who reinforce their views. This tendency, referred to as being rigid in one's beliefs, is often driven by one's emotional connection to their beliefs and worsened by social media platforms which promote highly personalized content to maximize user engagement. As increased belief rigidity can negatively impact decisions, it is crucial to study and design interventions to reduce belief rigidity in online social networks. We design an experimental framework to passively infer belief rigidity and empirically quantify the effects of introducing randomness into the network structure as an intervention strategy. We recruit 163 participants into two conditions: one emulating traditional online platforms and the other more random. Our results suggest that individuals' beliefs are influenced by peer opinions, regardless of whether those opinions are similar to or differ from their own. Moreover, people are more likely to incorporate peers with (slightly) differing opinions into their networks when the recommendations are more randomized. Our findings highlight the need for future exploration into how design interventions can shape people's psycho-social functionalities in online social platforms.

An objective, face-valid method for scoring idea originality is to measure each idea's statistical infrequency within a population-an approach long used in creativity research.Yet, computing these frequencies requires manually bucketing idea rephrasings, a process that is subjective, labor-intensive, error-prone, and brittle at scale.We introduce MUSESCORER, a fully automated, psychometrically validated system for frequency-based originality scoring.MUSESCORER integrates a Large Language Model (LLM) with externally orchestrated retrieval: given a new idea, it retrieves semantically similar prior idea-buckets and zero-shot prompts the LLM to judge whether the idea fits an existing bucket or forms a new one.These buckets enable frequencybased originality scoring without human annotation.Across five datasets (N participants =1143, n ideas =16,294), MUSESCORER matches human annotators in idea clustering structure (AMI = 0.59) and participant-level scoring (r = 0.89), while demonstrating strong convergent and external validity.The system enables scalable, intent-sensitive, and human-aligned originality assessment for creativity research.

Understanding the users' patterns of visiting various location categories can help online platforms improve content personalization and user experiences. Current literature on predicting future location categories of a user typically employs features that can be traced back to the user, such as spatial geo-coordinates and demographic identities. Moreover, existing approaches commonly suffer from cold-start and generalization problems, and often cannot specify when the user will visit the predicted location category. In a large social platform, it is desirable for prediction models to avoid using user-identifiable data, generalize to unseen and new users, and be able to make predictions for specific times in the future. In this work, we construct a neural model, LocHabits, using data from Snapchat. The model omits user-identifiable inputs, leverages temporal and sequential regularities in the location category histories of Snapchat users and their friends, and predicts the users' next-hour location categories. We evaluate our model on several real-life, large-scale datasets from Snapchat and FourSquare, and find that the model can outperform baselines by 14.94% accuracy. We confirm that the model can (1) generalize to unseen users from different areas and times, and (2) fall back on collective trends in the cold-start scenario. We also study the relative contributions of various factors in making the predictions and find that the users' visitation preferences and most-recent visitation sequences play more important roles than time contexts, same-hour sequences, and social influence features.

In this article, we present a database of multimodal communication features extracted from debate speeches in the 2019 North American Universities Debate Championships (NAUDC). Feature sets were extracted from the visual (facial expression, gaze, and head pose), audio (PRAAT), and textual (word sentiment and linguistic category) modalities of raw video recordings of competitive collegiate debaters (N=716 6-minute recordings from 140 unique debaters). Each speech has an associated competition debate score (range: 67-96) from experienced judges as well as competitor demographic and per-round reflection surveys. We observe the fully multimodal model performs best in comparison to models trained on various compositions of individual modalities. We also find that the weights of some features (such as the expression ofjoyand the use of the word ”we”) change in direction between the aforementioned models. We use these results to highlight the value of a multimodal dataset for studying competitive, collegiate debate.

We explore the characteristics and values of online peer-interactions in developing a fundamental soft-skill such as speaking. 60 participants recorded speech videos on 5 job-interview prompts and exchanged comments and performance ratings with their peers. We find that both (i) receiving suggestions for improvement ('tips') and (ii) having access to peers with better average ratings than one's own correspond to performance improvement (p<; 0.001 for both). Using linguistic features (e.g., emotions, personality, sentiment), we are able to classify tips from non-tip comments (AUC 0.89). Linguistic features from the received comments and the average ratings of one's peers incrementally improve the prediction of one's future ratings, showing the simultaneous importance of the two peer-learning sources. Qualitative analysis reveals dyadic and community-level peer-influence factors: context-driven feedback, first-hand demonstration, empathetic support, acknowledgment, opinion diversity, sense of community and comfort in interaction. These insights inform the building of intelligent human-machine symbiosis systems for speaking-skills development.

This paper demonstrates that analyzing language patterns in light of their associated facial expressions elicits significant differences between deceptive and truthful communication. Facial Action Units (AU) were analyzed in video recordings (1.2M frames) of 151 dyadic conversations following an interrogation protocol, in which one of the participants is known to be either lying or telling the truth. Linguistic features were extracted from the transcripts using Linguistic Inquiry and Word Count (LIWC) dictionary. Our framework extracted facial-feature contexts automatically corresponding to high and low intensities of AU occurrences. This helped us dive deeper into answers corresponding to the video segments where the witnesses kept their eyes wide open (high intensity of AU05-upper lid raise). We found that in these segments, deceivers used significantly fewer `Seeing', `Perceptual' and `Cognitive' words and their answers were significantly shorter than truth-tellers.

Telling a great story often involves a deliberate alteration of emotions. In this paper, we objectively measure and analyze the narrative trajectories of stories in public speaking and their impact on subjective ratings. We conduct the analysis using the transcripts of over 2000 TED talks and estimate potential audience response using over 5 million spontaneous annotations from the viewers. We use IBM Watson Tone Analyzer to extract sentence-wise emotion, language, and social scores. Our study indicates that it is possible to predict (with AUC as high as 0.88) the subjective ratings of the audience by analyzing the narrative trajectories. Additionally, we find that some trajectories (for example, a flat trajectory of joy) correlate well with some specific ratings (e.g. "Longwinded') assigned by the viewers. Such an association could be useful in forecasting audience responses using objective analysis.

This paper explores gender differences in the evaluation of male and female speakers' affective features in public speaking. We analyzed 260 two-minute behavioral videos (200 of females and 60 of males), collected from an online public speaking practice tool. We adopted a linear regression model that utilized facial and prosodic features, including facial action units (AU), word count, pitch, and volume, to automatically assess speaker performance. The model was evaluated against ratings from 2 expert speakers from Toastmasters, an international public speaking club, on speaker performance. Our feature analysis suggests that certain combinations of features are correlated with higher ratings only in males, such as the combined increase of speech rate and vocal pitch variation. Moreover, our clustering analysis suggests that exhibiting certain negative emotions correlates with higher ratings for males but not for females, illustrating the impact of gender in generating effective feedback on public speaking.