Social Interaction Information Research Articles

Early neural development of social interaction perception: evidence from voxel-wise encoding in young children and adults.

From a young age, children have advanced social perceptual and reasoning abilities. However, the neural development of these abilities is still poorly understood. To address this gap, we used fMRI data collected 122 3-12-year-old children (64 females) and 33 adults (20 females) watched an engaging and socially rich movie to investigate how the cortical basis of social processing changes throughout development. We labeled the movie with visual and social features, including motion energy, presence of a face, presence of a social interaction, theory of mind (ToM) events, valence and arousal. Using a voxel-wise encoding model trained on these features, we find that models based on visual (motion energy) and social (faces, social interaction, ToM, valence, and arousal) features can both predict brain activity in children as young as three years old across the cortex, with particularly high predictivity in motion selective middle temporal region (MT) and the superior temporal sulcus (STS). Furthermore, models based on individual social features showed that while there may be some development throughout childhood, social interaction information in the STS is present in children as young as three years old and appears adult-like by age seven. The current study, for the first time, links neural activity in children to pre-defined social features in a narrative movie and suggests social interaction perception is supported by early developing neural responses in the STS.Significance Statement This study investigates the neural basis for social scene perception ability in children using fMRI data collected while participants watch a short, animated movie. Unlike most prior studies with movies, we labeled a range of visual and social features in the movie and used machine learning analyses to link each feature to fMRI responses in adults and children ages 3-12. Notably, our results demonstrate strong evidence that children as young as three years old show significant responses to most visual and social features in the movie, including social interaction responses in the superior temporal sulcus (STS), a region in the brain that is well known to be important in social interaction processing in adults.

SISGAN: A Generative Adversarial Network Pedestrian Trajectory Prediction Model Combining Interaction Information and Scene Information

Accurate pedestrian trajectory prediction is crucial in many fields. This requires the full use and learning of pedestrians’ social interactions, movements, and environmental information. In view of the current research on pedestrian trajectory prediction, wherein most of the pedestrian interaction information is explored from the level of overall interaction, this paper proposes the SISGAN model, which designs a social interaction module from the perspective of the target pedestrian, and takes four kinds of interaction information as the influencing factors of pedestrian interaction, so as to describe the influence mechanism of pedestrian–pedestrian interaction. In addition, in terms of environmental information, the index density of pedestrian historical trajectory in space is taken into account in the extraction of environmental information, which increases the potential correlation between environmental information and pedestrians. Finally, we integrate social interaction information and environmental information and make the final trajectory prediction based on GAN. Experiments on ETH and UCY datasets demonstrate the effectiveness of the SISGAN model proposed in this paper.

Vocal Call Locator Benchmark (VCL) for localizing rodent vocalizations from multi-channel audio.

Understanding the behavioral and neural dynamics of social interactions is a goal of contemporary neuroscience. Many machine learning methods have emerged in recent years to make sense of complex video and neurophysiological data that result from these experiments. Less focus has been placed on understanding how animals process acoustic information, including social vocalizations. A critical step to bridge this gap is determining the senders and receivers of acoustic information in social interactions. While sound source localization (SSL) is a classic problem in signal processing, existing approaches are limited in their ability to localize animal-generated sounds in standard laboratory environments. Advances in deep learning methods for SSL are likely to help address these limitations, however there are currently no publicly available models, datasets, or benchmarks to systematically evaluate SSL algorithms in the domain of bioacoustics. Here, we present the VCL Benchmark: the first large-scale dataset for benchmarking SSL algorithms in rodents. We acquired synchronized video and multi-channel audio recordings of 767,295 sounds with annotated ground truth sources across 9 conditions. The dataset provides benchmarks which evaluate SSL performance on real data, simulated acoustic data, and a mixture of real and simulated data. We intend for this benchmark to facilitate knowledge transfer between the neuroscience and acoustic machine learning communities, which have had limited overlap.

A social context-aware graph-based multimodal attentive learning framework for disaster content classification during emergencies

In times of crisis, the prompt and precise classification of disaster-related information shared on social media platforms is of paramount importance for effective disaster response and public safety. During such critical events, people utilize social media as a medium for communication, sharing multimodal textual and visual content. However, due to the substantial influx of unfiltered and diverse data, humanitarian organizations face challenges in effectively leveraging this information. Numerous methods have been proposed for classifying disaster-related content, but these methods lack modeling users’ credibility, emotional context, and social interaction information, which is crucial for classification. In this context, we propose a method, CrisisSpot, that leverages a Graph-based Neural Network to comprehend intricate relationships between textual and visual modalities and Social Context Features to incorporate user-centric and content-centric information. We also propose Inverted Dual Embedded Attention, which captures both harmonious and contrary patterns present in the data to harness complex interactions and facilitate richer insights in multimodal data. We have developed a multimodal disaster dataset, TSEqD (Turkey-Syria Earthquake Dataset), which is a large annotated dataset for a single disaster event containing 10,352 data samples. Through extensive experimentation, CrisisSpot has demonstrated significant improvements, achieving an average gain of 9.45% and 5.01% in F1-score compared to the state-of-the-art methods on the publicly available CrisisMMD dataset and TSEqD dataset, respectively.

The whole network approach to understanding language contact reporting inconsistency in peer interactions

Language contact in peer interactions during study abroad has drawn considerable attention from applied linguists and educators. Scholars have developed various tools to collect language contact and social interaction information, such as the Language Contact Profile, the Language Log, and the Social Interaction Questionnaire. This paper employs a whole network design with peer evaluation to collect data on social interaction, use of languages, perceived language proficiency, and total time spent using different languages among 23 students with diverse backgrounds. These students were enrolled in an English-medium program in China that caters exclusively to international students. Findings show that the whole network approach can effectively cross-validate students’ self-reported language use and proficiency alongside their social interactions. The paper also offers several strategies, rooted in the whole network approach, to detect potential inconsistencies in these self-reported measures that warrant further investigations.

Analysis of Sarcasm Language Use on Instagram Social Media (Case Study of Students from the Faculty of Social Sciences UINSU)

This article aims to examine the dynamics of sarcasm language use by students of the Faculty of Social Sciences at the State Islamic University of North Sumatra (UINSU) in their interactions on Instagram social media. The urgency of this research arises from the observation that sarcasm, although often used as a communication tool, frequently leads to misinterpretations and interpersonal conflicts in the digital context that lacks non-verbal cues. This study adopts a qualitative case study approach with a constructivist paradigm, using thematic analysis techniques to interpret data obtained through in-depth interviews and observation of social media activities. Data validity testing was conducted through source triangulation to ensure the reliability of the findings. The research results indicate that students have significantly varied understandings of sarcasm, which is often misinterpreted as serious communication, leading to undesired emotional reactions. In the segment "Social Cognition: Student Poll on Sarcastic Comments on Instagram," it was found that although students could identify sarcasm, emotional reactions to sarcasm varied, indicating a need for higher digital literacy to effectively manage and process information in social interactions. The discussion in this article emphasizes that sarcasm on social media, while being a dynamic expression tool, requires deep contextual understanding to avoid misinterpretation. Therefore, enhancing media literacy is crucial to help students understand communication nuances and reduce the risk of misunderstandings in digital interactions. This study highlights the importance of effective digital literacy education, which not only enhances students' communication skills but also prepares them to navigate the complex and often ambiguous digital ecosystem.

SIF-TF: A Scene-Interaction fusion Transformer for trajectory prediction

Accurate pedestrian trajectory prediction is essential for the advancement of intelligent robot or autonomous vehicle, which is a challenging and interesting task. In this paper, a Scene-Interaction fusion Transformer (SIF-TF) for trajectory prediction is proposed, which takes into account three fundamental factors, i.e. social interaction, past trajectory, and semantic scene. A scene-social modeling method is added to the model to integrate social interaction and semantic scene. The proposed SIF-TF contains two critical components: the scene-social transformer and the temporal transformer. The scene-social transformer is tasked with capturing social interaction and semantic scene information, while the temporal transformer focuses on extracting temporal correlation information. Furthermore, the SIF-TF employs a two-stage trajectory prediction approach to jointly generate future trajectories. To evaluate the effectiveness, the comparative experiments were conducted on five widely-used public datasets. The experiments results, with an average evaluation metric of ADE/FDE of 0.23/0.47, significantly outperforms other state-of-the-art methods. These findings demonstrate that the proposed SIF-TF is capable of delivering more precise pedestrian trajectory predictions across diverse scene backgrounds.

Determinants of Implementing an Information and Communication Technology Tool for Social Interaction Among Older People: Qualitative Content Analysis of Social Services Personnel Perspectives.

Older people are particularly vulnerable to social isolation and loneliness, which can lead to ill-health, both mentally and physically. Information and communication technology (ICT) can supplement health and social care and improve health among the vulnerable, older adult population. When ICT is used specifically for communication with others, it is associated with reduced loneliness in older populations. Research is sparse on how the implementation of ICT, used specifically for communication among older people in social services, can be performed. It is recommended to consider the determinants of implementation, that is, barriers to and facilitators of implementation. Determinants related to older people using ICT tools are reported in several studies. To the best of our knowledge, studies investigating the determinants related to the social services perspective are lacking. This study aims to explore the determinants of implementing the Fik@ room, a new, co-designed, and research-based ICT tool for social interaction among older people, from a social services personnel perspective. This study used an exploratory, qualitative design. An ICT tool called the Fik@ room was tested in an intervention study conducted in 2021 in 2 medium-sized municipalities in Sweden. Informants in this study were municipal social services personnel with experience of implementing this specific ICT tool in social services. We conducted a participatory workshop consisting of 2 parts, with 9 informants divided into 2 groups. We analyzed the data using qualitative content analysis with an inductive approach. The results included 7 categories of determinants for implementing the ICT tool. Being able to introduce the ICT tool in an appropriate manner concerns the personnel's options for introducing and supporting the ICT tool, including their competencies in using digital equipment. Organizational structure concerns a structure for communication within the organization. Leadership concerns engagement and enthusiasm as driving forces for implementation. The digital maturity of the social services personnel concerns the personnel's skills and attitudes toward using digital equipment. Resources concern time and money. IT support concerns accessibility, and legal liability concerns possibilities to fulfill legal responsibilities. The results show that implementation involves an entire organization at varying degrees. Regardless of how much each level within the organization comes into direct contact with the ICT tool, all levels need to be involved to create the necessary conditions for successful implementation. The prerequisites for the implementation of an ICT tool will probably change depending on the digital maturity of future generations. As this study only included 9 informants, the results should be handled with care. The study was performed during the COVID-19 pandemic, which has probably affected the results.

The Impact of Impression Management Tactics on Faculty Job Performance: A Study of Private Sector Universities of Peshawar

Impression management (IM) is a conscious or subconscious process in which people attempt to influence the perceptions of other people about a person, object or event by regulating and controlling information in social interaction and which can also be used as a metaphor for human social interaction. Impression Management tactics are broadly categorized into five to help people, to understand and use IM tactics in an organizational setting: ingratiation, intimidation, self-promotion, supplication, and exemplification. Impression management has been associated with the positive performance in workplace. In this study, purposive-cum-snow ball sampling techniques are used to collect primary data from the targeted audience i-e, the faculty of the private university. According to literature studies, employees are pushed to comprehend impression management and the associated strategies by the competitive and dynamic atmosphere in educational sector, which has a favorable impact on their job performance. Analysis of the results revealed that ingratiation, self-promotion and exemplification has significant impact on job performance. In contrast to the hypothesis, exemplification show negative impact on the overall job performance, while intimidation has significant negative impact on employee’s job performance. The supplication tactics of impression management were removed during factor loading because of its multifaceted nature. The findings will help to strengthen horizontal and vertical social interactions with particular focus on impression management tactics, which can have an impact on job performance.

اسلامی معاشروں پر سوشل میڈیا کے اثرات کا تجزیاتی مطالعہ An Analytical Study of the Impact of Social-Media on Islamic Societies

Social media has connected people having various cultures with each other at the global level. It is an exquisite source of social interaction and instant information. The significant role of social media in the contemporary world cannot be denied. It has connected people having various cultures with each other at the global level. It is an exquisite source of social interaction and instant information. The significant role of social media in the contemporary world cannot be denied. It has emerged as a transformative force in the modern age, reshaping various aspects of society, communication, interconnection, and individual behaviour. It can play a key role in the development of any society in the contemporary world. Many benefits can be achieved in a short period by proper and positive use of it. But, on the other side, social media is also a source of false information, character assassination and propaganda. If it is used correctly and positively, a lot of benefits can be obtained in a very short time, but it has become an easy and instant source of misinformation, character assassination, obscenity and nudity, social evils, Islamophobia, Atheism and Western culture in societies. It has brought about radical changes in the patterns of social interactions in Islamic societies. Nowadays, the negative impact of social media on Islamic societies has become a challenge for the Muslim Ummah. The negative impact of social media on the Muslim young generation is a major issue in Muslim society. A new generation of our society is misusing social media. Character assassination of any person has become common by sharing edited pictures and videos on social media in recent times. Islam provides a code of conduct for the excessive use of social media and prevents its negative use. This research article presents a comprehensive analysis of the impact of social media on Muslim societies.

Hierarchical organization of social action features along the lateral visual pathway

Recent theoretical work has argued that in addition to the classical ventral (what) and dorsal (where/how) visual streams, there is a third visual stream on the lateral surface of the brain specialized for processing social information. Like visual representations in the ventral and dorsal streams, representations in the lateral stream are thought to be hierarchically organized. However, no prior studies have comprehensively investigated the organization of naturalistic, social visual content in the lateral stream. To address this question, we curated a naturalistic stimulus set of 250 3-s videos of two people engaged in everyday actions. Each clip was richly annotated for its low-level visual features, mid-level scene and object properties, visual social primitives (including the distance between people and the extent to which they were facing), and high-level information about social interactions and affective content. Using a condition-rich fMRI experiment and a within-subject encoding model approach, we found that low-level visual features are represented in early visual cortex (EVC) and middle temporal (MT) area, mid-level visual social features in extrastriate body area (EBA) and lateral occipital complex (LOC), and high-level social interaction information along the superior temporal sulcus (STS). Communicative interactions, in particular, explained unique variance in regions of the STS after accounting for variance explained by all other labeled features. Taken together, these results provide support for representation of increasingly abstract social visual content-consistent with hierarchical organization-along the lateral visual stream and suggest that recognizing communicative actions may be a key computational goal of the lateral visual pathway.

Multiple Precaching Vehicle Selection Scheme Based on Set Ranking in Intermittently Connected Vehicular Networks

In vehicular networks, vehicles download vehicular information for various applications, including safety, convenience, entertainment, and social interaction, from the corresponding content servers via stationary roadside units. Since sufficient RSUs might be difficult to deploy due to rough geographical conditions or high deployment costs, vehicular networks can feature uncovered outage zones between two neighboring RSUs. In these outage zones, vehicles cannot download content, and thus the vehicle networks are defined as intermittently connected vehicular networks. In intermittently connected vehicular networks, the download delay and traffic overhead on the backhaul links are increased due to the large size of the content requested by vehicle users and the long distances between RSUs. Using the mobility information of vehicles, several schemes have been proposed to solve this issue by precaching and relaying content via multiple relaying vehicles in the outage zone. However, because they involved the individual ranking of vehicles for precaching and allocated all of the available precaching amounts to the top-ranking vehicles, they decreased the success rate of content requests and the fairness of vehicle precaching. To overcome the problem of these previous schemes, this paper proposes a multiple precaching vehicle selection (MPVS) scheme that efficiently selects a content-precaching vehicle group with multiple precaching vehicles to precache relayed content in outage zones. To achieve this, we first designed numerical models to decide the necessity and the amount of precaching and to calculate the available precaching amounts of vehicles. Next, MPVS calculates all available vehicle sets and ranks each set based on the available precaching amount. Then, the content-precaching vehicle group is identified from the sets by considering both set rankings and vehicle communication overheads. MPVS also provides a content downloading process through the content-precaching vehicle group in the outage zone. Simulation results conducted in various environments with a content request model and a highway mobility model verified that MPVS was superior to a representative previous scheme.

Micro-Influencer Recommendation by Multi-Perspective Account Representation Learning

Influencer marketing is emerging as a new marketing method, changing the marketing strategies of brands profoundly. In order to help brands to find suitable micro-influencers as marketing partners, micro-influencer recommendation is regarded as an indispensable part of influencer marketing. However, previous works only focus on modeling the individual image of brands/micro-influencers, which is insufficient to represent the characteristics of brands/micro-influencers over the marketing scenarios. In this case, we propose a micro-influencer ranking joint learning framework which models brands/micro-influencers from the perspective of individual image, target audiences, and cooperation preferences. Specifically, to model accounts' individual image, we extract topics information and images semantic information from historical content information, and fuse them to learn the account content representation. We introduce target audiences as a new kind of marketing role into micro-influencer recommendation, in which audiences information of brand/micro-influencer is leveraged to learn the multi-modal account audiences representation. Afterwards, we build the attribute co-occurrence graph network to mine cooperation preferences from social media interaction information. Based on account attributes, the cooperation preferences between brands and micro-influencers are refined to attributes' co-occurrence information. The attribute node embeddings learned in the attribute co-occurrence graph network are further utilized to construct the account attribute representation. Finally, the global ranking function is designed to generate ranking scores for all brand-micro-influencer pairs from the three perspectives jointly. The extensive experiments on a publicly available dataset demonstrate the effectiveness of our proposed model over the state-of-the-art methods.

Signaling effect in social network and charity crowdfunding: Empirical analysis of charity crowdfunding of Sina MicroBlog in China.

With the increasing number of online charity donations, research on the influencing factors of individual donation behavior has become an important topic. Social interaction information in crowdfunding has become an essential basis for potential backers to make decisions. It provides new research space for charity crowdfunding and social capital theory. The primary purpose of this study is to explore the influence of social capital, social recommendation, and other signals on charity crowdfunding performance. We obtain 4,780 project information on the charity crowdfunding of Sina MicroBlog through data collection procedures. Our research found that both external social capital and internal capital can significantly improve the fundraising performance of crowdfunding projects. Projects with more social recommendations are more likely to obtain financial support. In the case of Medical aid crowdfunding projects, the positive promotion effect of social recommendations on project fundraising ability is enhanced. To get more effective support for crowdfunding projects, it is necessary to pay attention to the construction of social capital and the cultivation of its reputation to obtain the recognition of potential backers.

Association between attachment anxiety and the gaze direction-related N170

ABSTRACT Attachment theory suggests that interindividual differences in attachment security versus insecurity (anxiety and avoidance) contribute to the ways in which people perceive social emotional signals, particularly from the human face. Among different facial features, eye gaze conveys crucial information for social interaction, with a straight gaze triggering different cognitive and emotional processes as compared to an averted gaze. It remains unknown, however, how interindividual differences in attachment associate with early face encoding in the context of a straight versus averted gaze. Using electroencephalography (EEG) and recording event-related potentials (ERPs), specifically the N170 component, the present study (N = 50 healthy adults) measured how the characteristics of attachment anxiety and avoidance relate to the encoding of faces with respect to gaze direction and head orientation. Our findings reveal a significant relationship between gaze direction (irrespective of head orientation) and attachment anxiety on the interhemispheric (i.e. right) asymmetry of the N170 and thus provide evidence for an association between attachment anxiety and eye gaze processing during early visual face encoding.

Beauty affects fairness: facial attractiveness alters neural responses to unfairness in the ultimatum game.

Human faces consist of rich information for social interactions and facial attractiveness is a key dimension affecting social decisions. Previous studies have indicated that human players are less likely to refuse an unfair offer from proposers with high facial attractiveness in the Ultimatum Game (UG). However, the neural mechanisms underlying such beauty premium effect remain unclear. In this study, we used functional magnetic resonance imaging (fMRI) and examined the effects of facial attractiveness on brain responses to fair and unfair offers in the UG. Behavioral data showed that subjects were overall prone to refuse unfair offers across conditions but were more likely to accept unfair offers from higher facial attractive proposers than those from lower facial attractive proposers. Imaging data showed that unfair offers induced greater activity in the anterior insula and medial prefrontal cortex (MePFC) compared to those in fair offers condition for both high and low facial attractive proposers. Moreover, the acceptance rate of unfair offers positively correlated with the MePFC activity for high facial attractive proposers and negatively correlated with the anterior insula activity for low facial attractive proposers. These findings suggest that facial attractiveness modulates brain responses to unfairness through altering the roles of emotion and cognitive motivation in social interactions.

Individual differences in social intelligence and perception of emotion expression of masked and unmasked faces

Facial expressions provide key information for successful social interactions. Recent research finds that accurate perception of emotion expressions decreases when faces are presented with face masks. What is unknown is how individual differences in social intelligence may influence perception of masked emotion expressions. In this study, participants (n = 224) completed an emotion perception task of face stimuli presented with and without face masks and completed two measures of social intelligence: the Reading the Mind in the Eyes Test (RMET) and the Tromsø Social Intelligence Scale (TSIS). Face masks were found to significantly decrease the accurate identification of emotion expressions, impacting the perception of disgust and sad expressions the most. Further, the type of emotion misattributed to facial expressions varied across expressions. Performance on the RMET test did predict perception accuracy, while scores on the TSIS did not. As face masks continue to be common globally, we must be aware that they cause interference with our social interactions and perceptions. Further, some individuals may be more negatively impacted by these effects than others. As such, it is important that we find ways to ensure that we are effectively communicating with one another and have patience when perception mistakes arise.

Dominance or Integration? Influence of Sexual Dimorphism and Clothing Color on Judgments of Male and Female Targets' Attractiveness, Warmth, and Competence.

The face is an important source of information in social interactions. Prior studies exploring the mechanism of face perception were consistent with either dominance or integration theory. Studies have shown that both sexually dimorphic features and background cues play essential roles in the formation of impressions and the perception of facial attractiveness. In this study, we conducted two experiments to examine 539 participants’ appraisal of attractiveness, warmth, and competence of the target faces of masculine and feminine men and women dressed in red, blue, or white. The results showed that: (1) feminized male/female faces were considered to have a higher degree of attractiveness, warmth, and competence, (2) people rated feminine faces wearing red higher in terms of attractiveness perception, while there was no significant effect of red on attractiveness perception of masculine faces, (3) when evaluating the warmth of targets, the promotion effect of red was found for feminine faces but not for masculine faces. This study, conducted in a pathogen disgust environment, provides direct evidence to support the integration theory over the dominance theory. Feminized red preference found in this study matches Chinese collectivism and the red cultural heritage, which has an important value for people's daily impression management and consumption decisions.

Multi-interaction fusion collaborative filtering for social recommendation

GNNs(Graph Neural Networks) use graph structure to make recommendations, receiving more and more attention. Firstly, existing work focuses on aggregating social interaction information, ignoring users who rate on the same items. Secondly, the existing recommendation methods cannot dynamically reflect changes in user interests. Thirdly, existing methods do not take into account the interaction of subgraphs in GNNs and interactions between user and item factors. In this paper, a graph social fusion recommendation (GSFR) method is proposed. GSFR captures multiple social information simultaneously, based on which it can dynamically adjust user interest weight. Specifically, GSFR captures the interactions in subgraphs with a dynamic attention mechanism, which can represent changes in user interests in heterogeneous networks. A mutualistic mechanism is combined to simulate the mutually reinforcing relationship between social behavior and virtual behavior. User and item latent factors are obtained based on space vectors from the aggregation part. Recommendations are made from inherent characteristics of space vectors interaction behaviors. Comprehensive experimental results on three public datasets show the effectiveness of the proposed model.

Manifold-enhanced CycleGAN for facial expression synthesis

ABSTRACT Facial expressions contain a great deal of information in inter-personal communication and social interaction and have a great impact on face recognition. This paper investigates expression synthesis methods based on deep learning and their performance in expression classification and face recognition. We propose a new synthesis method based on the active appearance model and CycleGAN. The eigendecomposition method is used to obtain the expression shape of a target subject through linear operations. An expression intensity coefficient is developed to control the dynamics of generated expressions. Subsequently, a generative model is introduced for the synthesis on textures. The identity loss is integrated into the traditional CycleGAN model, incorporated with the adversarial loss and the cycle consistency loss, to better preserve texture attributes and identity information of target subjects. Experiments and comparisons show that the proposed method offers marked improvements in verification of expressions and identities, as well as in generalization across databases.