SenseLens: An Efficient Social Signal Conditioning System for True Event Detection

This article narrows the gap between physical sensing systems that measure physical signals and social sensing systems that measure information signals by (i) defining a novel algorithm for extracting information signals (building on results from text embedding) and (ii) showing that it increases the accuracy of truth discovery—the separation of true information from false/manipulated one. The work is applied in the context of separating true and false facts on social media, such as Twitter and Reddit, where users post predominantly short microblogs. The new algorithm decides how to aggregate the signal across words in the microblog for purposes of clustering the miscroblogs in the latent information signal space, where it is easier to separate true and false posts. Although previous literature extensively studied the problem of short text embedding/representation, this article improves previous work in three important respects: (1) Our work constitutes unsupervised truth discovery, requiring no labeled input or prior training. (2) We propose a new distance metric for efficient short text similarity estimation, we call Semantic Subset Matching , that improves our ability to meaningfully cluster microblog posts in the latent information signal space. (3) We introduce an iterative framework that jointly improves miscroblog clustering and truth discovery. The evaluation shows that the approach improves the accuracy of truth-discovery by 6.3%, 2.5%, and 3.8% (constituting a 38.9%, 14.2%, and 18.7% reduction in error, respectively) in three real Twitter data traces.

A Computational Look at Oral History Archives

Computational technologies have revolutionized the archival sciences field, prompting new approaches to process the extensive data in these collections. Automatic speech recognition and natural language processing create unique possibilities for analysis of oral history (OH) interviews, where otherwise the transcription and analysis of the full recording would be too time consuming. However, many oral historians note the loss of aural information when converting the speech into text, pointing out the relevance of subjective cues for a full understanding of the interviewee narrative. In this article, we explore various computational technologies for social signal processing and their potential application space in OH archives, as well as neighboring domains where qualitative studies is a frequently used method. We also highlight the latest developments in key technologies for multimedia archiving practices such as natural language processing and automatic speech recognition. We discuss the analysis of both visual (body language and facial expressions), and non-visual cues (paralinguistics, breathing, and heart rate), stating the specific challenges introduced by the characteristics of OH collections. We argue that applying social signal processing to OH archives will have a wider influence than solely OH practices, bringing benefits for various fields from humanities to computer sciences, as well as to archival sciences. Looking at human emotions and somatic reactions on extensive interview collections would give scholars from multiple fields the opportunity to focus on feelings, mood, culture, and subjective experiences expressed in these interviews on a larger scale.

Frequent Interpersonal Stress and Inflammatory Reactivity Predict Depressive-Symptom Increases: Two Tests of the Social-Signal-Transduction Theory of Depression

The social-signal-transduction theory of depression asserts that people who experience ongoing interpersonal stressors and mount a greater inflammatory response to social stress are at higher risk for depression. The current study tested this theory in two adult samples. In Study 1, physically healthy adults ( N = 76) who reported more frequent interpersonal tension had heightened depressive symptoms at Visit 2, but only if they had greater inflammatory reactivity to a marital conflict at Visit 1. Similarly, in Study 2, depressive symptoms increased among lonelier and less socially supported breast-cancer survivors ( N = 79). This effect was most pronounced among participants with higher inflammatory reactivity to a social-evaluative stressor at Visit 1. In both studies, noninterpersonal stress did not interact with inflammatory reactivity to predict later depressive symptoms.

Perception of group membership from spontaneous and volitional laughter

Laughter is a ubiquitous social signal. Recent work has highlighted distinctions between spontaneous and volitional laughter, which differ in terms of both production mechanisms and perceptual features. Here, we test listeners' ability to infer group identity from volitional and spontaneous laughter, as well as the perceived positivity of these laughs across cultures. Dutch ( n = 273) and Japanese ( n = 131) participants listened to decontextualized laughter clips and judged (i) whether the laughing person was from their cultural in-group or an out-group; and (ii) whether they thought the laughter was produced spontaneously or volitionally. They also rated the positivity of each laughter clip. Using frequentist and Bayesian analyses, we show that listeners were able to infer group membership from both spontaneous and volitional laughter, and that performance was equivalent for both types of laughter. Spontaneous laughter was rated as more positive than volitional laughter across the two cultures, and in-group laughs were perceived as more positive than out-group laughs by Dutch but not Japanese listeners. Our results demonstrate that both spontaneous and volitional laughter can be used by listeners to infer laughers’ cultural group identity. This article is part of the theme issue ‘Voice modulation: from origin and mechanism to social impact (Part II)’.

Face masks increase compliance with physical distancing recommendations during the COVID-19 pandemic

Governments across the world have implemented restrictive policies to slow the spread of COVID-19. Recommended face mask use has been a controversially discussed policy, among others, due to potential adverse effects on physical distancing. Using a randomized field experiment (N = 300), we show that individuals kept a significantly larger distance from someone wearing a face mask than from an unmasked person during the early days of the pandemic. According to an additional survey experiment (N = 456) conducted at the time, masked individuals were not perceived as being more infectious than unmasked ones, but they were believed to prefer more distancing. This result suggests that wearing a mask served as a social signal that led others to increase the distance they kept. Our findings provide evidence against the claim that mask use creates a false sense of security that would negatively affect physical distancing. Furthermore, our results suggest that behavior has informational content that may be affected by policies.

Using Social Signals to Predict Shoplifting: A Transparent Approach to a Sensitive Activity Analysis Problem

Retail shoplifting is one of the most prevalent forms of theft and has accounted for over one billion GBP in losses for UK retailers in 2018. An automated approach to detecting behaviours associated with shoplifting using surveillance footage could help reduce these losses. Until recently, most state-of-the-art vision-based approaches to this problem have relied heavily on the use of black box deep learning models. While these models have been shown to achieve very high accuracy, this lack of understanding on how decisions are made raises concerns about potential bias in the models. This limits the ability of retailers to implement these solutions, as several high-profile legal cases have recently ruled that evidence taken from these black box methods is inadmissible in court. There is an urgent need to develop models which can achieve high accuracy while providing the necessary transparency. One way to alleviate this problem is through the use of social signal processing to add a layer of understanding in the development of transparent models for this task. To this end, we present a social signal processing model for the problem of shoplifting prediction which has been trained and validated using a novel dataset of manually annotated shoplifting videos. The resulting model provides a high degree of understanding and achieves accuracy comparable with current state of the art black box methods.

A Biosemiotic Perspective on Reward-Based Animal Training Techniques

In this paper, I examine the way humans interact with domestic companion animals, with a focus on ‘positive reward-based training’ methods, particularly for dogs. From a biosemiotic perspective, I discuss the role of animal training in today’s society and examine what binary reward- based reinforcement schedules communicate, semiotically. I also examine the extent to which reward-based training methods promote better welfare, when compared to the more traditional methods which rely on aversive stimuli and punishment, if and when they are relied upon excessively. I conclude that when used as the primary means of communication, they have the potential to be detrimental to animal welfare, because the underlying social signal is control and resource dominance. As an alternative view to behaviourist-based learning theory and conditioning, I outline how enactivist theories of cognition support a semiotic approach to interspecific human-animal communication. I therefore propose a move toward a dynamic semiosis and mutual understanding based upon Peirce’s phenomenology, resulting in a more balanced merging of Umwelten. The aim is to create rich and more complex semiospheres around humans and domestic animals, which allow for individual agency and autonomy.

Nonverbal Social Sensing: What Social Sensing Can and Cannot Do for the Study of Nonverbal Behavior From Video

The study of nonverbal behavior (NVB), and in particular kinesics (i.e., face and body motions), is typically seen as cost-intensive. However, the development of new technologies (e.g., ubiquitous sensing, computer vision, and algorithms) and approaches to study social behavior [i.e., social signal processing (SSP)] makes it possible to train algorithms to automatically code NVB, from action/motion units to inferences. Nonverbal social sensing refers to the use of these technologies and approaches for the study of kinesics based on video recordings. Nonverbal social sensing appears as an inspiring and encouraging approach to study NVB at reduced costs, making it a more attractive research field. However, does this promise hold? After presenting what nonverbal social sensing is and can do, we discussed the key challenges that researchers face when using nonverbal social sensing on video data. Although nonverbal social sensing is a promising tool, researchers need to be aware of the fact that algorithms might be as biased as humans when extracting NVB or that the automated NVB coding might remain context-dependent. We provided study examples to discuss these challenges and point to potential solutions.

Luxury Branding in the Hospitality Industry: The Impact of Employee’s Luxury Appearance and Elitism Attitude

Some of the luxury consumption literature suggests that luxury consumption is a beneficial social signal for the actor which facilitates social interaction. However, a different stream of recent research suggests that luxury consumption bears social costs to the actor. In the employee–customer interaction context, wearing luxury brands can either benefit or backfire for the employee depending on the situation whether luxury status or warmth is necessary. Based on the gaps in the literature, this study examines the impact of employee conspicuous cues by utilizing luxury consumption and elitism attitude on employee–customer rapport and behavioral intentions. The study results show that employees wearing luxury brands increase customers’ perceived impression management toward the employee. Such perception is strengthened when employees show an elitism attitude. In addition, when employees wear luxury brands, customers are more likely to build rapport with employees when they show a democratic attitude, as they perceive the employees are less likely to involve in impression management than showing an elitism attitude. The results build upon the luxury hospitality literature, aesthetic labor, impression management, and rapport literature.

AGGRESSIVE BEHAVIORS AND THEIR EFFECT ON RESOURCE USE BY FEMALE SINCELLA LATERALIS

Studies of aggression and space use are essential to understanding resource use by reptiles, particularly lizards. Research in this area, however, exhibits bias in that the seminal work has been done on (1) species that are highly visible in their habitats (e.g. Iguanians); and (2) males. Studies of secretive species such as skinks and of females are less common. Here, we present results of a lab study of dyadic encounters of adult females of a common North American skink: Scincella lateralis (Little Brown Skink), and compare them to results obtained from an earlier study of adult males of the same species. Female S. lateralis never interacted unless they were within one body length of each other. The most common behavior exhibited was avoidance of one lizard (the subordinate) away from the other lizard (the dominant). As a result, the two lizards spent more time apart than close together and rarely shared the retreat. The larger of the two females was dominant in 9 of 10 trials. Compared to adult males, adult females showed far fewer aggressive behaviors such as lunging or chasing, and never bit each other. Unlike males, however, subordinate female S. lateralis exhibited tail twitching significantly more often than did dominants, suggesting this behavior may be a social signal for females, though the data suggest there may be other possible functions. Despite differences in the frequency of behaviors exhibited, patterns of space use and retreat use were the same in females as they were in males.