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With the rapid development of the mobile Internet, online social networks are playing an increasingly vital role in the dissemination of information. Accurately predicting the size of information cascades in advance has become a crucial issue, particularly in the realms of viral marketing, risk management and resource allocation. There are numerous studies that have tackled this prediction task, but the outcomes are unsatisfactory. In this paper, we explore the predictability of information cascade size through the lens of percolation theory. Our investigation reveals that the accuracy of cascade size prediction is notably diminished in the proximity of the threshold, evident in both artificial and empirical networks. Moreover, we observe a degradation and an user-level difference in prediction performance as social media platforms undergo evolution. Our findings underscore the necessity for additional factors to enhance prediction accuracy. [ABSTRACT FROM AUTHOR]

Social media has entrenched itself as an indispensable marketing tool. We introduce a quantitative approach to predicting the popularity of social media posts within the café and bakery sector. Employing Multi‐Pop, a multimodal popularity prediction model that harnesses both images and text from post content, it utilizes the features of posts that significantly influence their popularity on one of the most widely used platforms, Instagram. By focusing solely on post‐content features and excluding user information, we analysed 8765 Instagram posts from the cafe and bakery domain, revealing that our model attains a superior accuracy rate of 82.0% compared with existing popularity prediction methods. Furthermore, the study identifies hashtags and post captions as exerting a greater impact on post popularity than images. This research furnishes valuable insights, particularly for small business owners and individual entrepreneurs, by introducing novel computational and empirical methodologies for Instagram marketing strategy and post popularity prediction, thereby enhancing the comprehension of social media marketing dynamics. [ABSTRACT FROM AUTHOR]

Nowadays, social networks are inseparable part of people everyday life. Large amounts of check-ins collected by location-based social networks (LBSNs) are rich information sources of social and spatio-temporal aspects of human life. In LBSNs, a co-occurrence refers to an event that a pair (almost) simultaneously appears at the same location. There are very few efforts on co-occurrence prediction in LBSNs in the literature. This paper aims to predict the co-occurrences between pairs of users, whether in the future or the past. This can be used in epidemiological studies like today COVID-19 pandemic. We have identified several effective spatial/temporal/social factors. For each, the method of the predictability measurement has been proposed. The spatial, regional, categorical and hierarchical-temporal aspects have been measured with inverse diversity, which is based on Entropy. In general, lower entropy implies higher predictability. Coincidences are unintentional encounters between users. Unlike previous studies that eliminated them, we have been considered them as a co-occurrence type and their effects have been measured and considered in the prediction task. By utilizing these identified factors along with the friendship information, a novel framework has been developed to enable the co-occurrence pattern inference among user pairs. Furthermore, valid patterns have been identified to prevent predictions based on expired patterns. To the best of our knowledge, this issue has not been studied in any other human behavior investigations. Evaluations with two real LBSN datasets, show the efficacy of our co-occurrence inference framework, outperforming other comparable method. [ABSTRACT FROM AUTHOR]

Purpose: Ensuring accessible urban infrastructure remains a challenge to inclusive societies and equal participation of people with disabilities in economic, cultural & social life and is thus a stunting factor in economic development. This paper proposes using an Artificial Intelligence-based model for evaluating accessibility in urban infrastructure towards identifying & predicting problematic areas in the existing or future built environment. The objective is to describe a reliable and extensible model capable of detecting mobility-problematic areas, evaluating the quality of urban infrastructure, proposing alternative routes and creating the base of a holistic detection and evaluation digital tool for better urban planning and efficient application of European Social Policies. Methodology: The research identifies obstacle and difficulty components useful within a Digital AI system via structured interviews performed with members of 2 key organizations in social development and inclusion in Eastern Macedonia and Thrace, Greece. Findings: The set of obstacles and difficulties is aggregated in a vector of solvable difficulties suitable for an AI system. Additionally, we propose methodologies for collecting and comparing data from predefined pilot routes between people with disabilities and the general population to build an initial training dataset for a continuous decision-making and evaluation AI system. Originality: Research originality is derived from combining Artificial Intelligence with the sector of computational evaluation of material infrastructure, as perceived by humans with disabilities, and as a tool of increased economic activity. It additionally defines key obstacles perceived by PwDs that are sufficiently measurable and subsequently solvable by AI. [ABSTRACT FROM AUTHOR]

Social relationship prediction has garnered significant attention in the development of artificial intelligence technology due to its potential to promote socioeconomic development. Meantime, the increasing popularity and adoption of intelligent devices located in the Internet of Things (IoT) environment have generated abundant data, which can serve as a basis for social relationship prediction. Nevertheless, the highly fragmented distribution of user data and the implementation of data protection policies can lead to the dilemma of "data scarcity" during the process of social relationship prediction. The application of federated learning can effectively address data segregation and fragmentation. Additionally, existing studies on social relationship prediction fail to consider users' temporal sequence information. To this end, we present a novel Federated Deep Relationship Prediction framework, named FDRP, which adopts the principle of vertical federated learning. Specifically, under the IoT environment, the offline operation initially assigns virtual items and ratings to users. Subsequently, the online operation executes social relationship prediction, where the client converts the sparse data into dense vectors and extracts the overall temporal sequence features, as well as the server performs the model parameter aggregation. Through extensive experiments conducted on the Epinions dataset, FDRP demonstrates excellent prediction accuracy and convergence speed, effectively mitigating the risk of inference attacks. [ABSTRACT FROM AUTHOR]

Social environments impose a number of constraints on individuals' behavior. These constraints have been hypothesized to generate behavioral variation among individuals, social responsiveness, and within-individual behavioral consistency (also termed "predictability"). In particular, the social niche specialization hypothesis posits that higher levels of competition associated with higher population density should increase among-individual behavioral variation and individual predictability as a way to reduce conflicts. Being predictable should hence have fitness benefits in group-living animals. However, to date empirical studies of the fitness consequences of behavioral predictability remain scarce. In this study, we investigated the associations between social behavior, its predictability, and fitness in the eastern water dragon (Intellagama lesueurii), a wild gregarious lizard. Since this species is sexually dimorphic, we examined these patterns both between sexes and among individuals. Although females were more sociable than males, there was no evidence for sex differences in among-individual variation or predictability. However, females exhibited positive associations between social behavior, its predictability, and survival, while males exhibited only a positive association between mean social behavior and fitness. These findings hence partly support predictions from the social niche specialization hypothesis and suggest that the function of social predictability may be sex dependent. [ABSTRACT FROM AUTHOR]

ABSTRACT The chick‐a‐dee call of chickadees, tits, and titmice is a vocal system used in a wide range of social contexts by both sexes throughout the year and is one of the more structurally complicated vocal systems outside of human language. Relatively little is known about the chick‐a‐dee calls of mountain chickadees, Poecile gambeli, however. This is an important species for increasing our comparative understanding of variation in chick‐a‐dee calls as they are one of the chickadee species with the largest naturally occurring flock sizes. Flock size relates to the social complexity of flocks, and the social complexity hypothesis for communication predicts that individuals in more complex social groups should communicate with greater complexity than individuals in simpler social groups. Correlational and experimental evidence in support of the hypothesis has been found in the calls of a wide range of species, including Carolina chickadees, P. carolinensis. Here, we provide the first description of the variation in note composition and note‐ordering rules in calls from mountain chickadee flocks in California and Colorado. California flocks were found to be significantly larger than Colorado flocks. Analysis of note‐type usage and transition probabilities between note types found that calls of California birds were more complex than calls of Colorado birds, supporting a key prediction of the social complexity hypothesis for communication. We also found relatively high rates of reversals of note‐ordering rules in mountain chickadee calls, which might help explain the complexity of the chick‐a‐dee calls of this species. Additionally, birds in flight produced calls with different note compositions when compared to perched birds. Generally, the note‐type ordering and transition probabilities of calls of mountain chickadees seem comparable to other better‐studied chickadee species, although their frequent note‐type order rule reversals suggest potential syntax‐like properties in this call system. [ABSTRACT FROM AUTHOR]

The TEST pervasive use of social media has highlighted the importance of developing sophisticated models for early information warning systems within online communities. Despite the advancements that have been made, existing models often fail to adequately consider the pivotal role of network topology and temporal dynamics in information dissemination. This results in suboptimal predictions of content propagation patterns. This study introduces the User Propagation Influence-based Linear Threshold (UPI-LT) model, which represents a novel approach to the simulation of information spread. The UPI-LT model introduces an innovative approach to consider the number of active neighboring nodes, incorporating a time decay factor to account for the evolving influence of information over time. The model's technical innovations include the incorporation of a homophily ratio, which assesses the similarity between users, and a dynamic adjustment of activation thresholds, which reflect a deeper understanding of social influence mechanisms. Empirical results on real-world datasets validate the UPI-LT model's enhanced predictive capabilities for information spread. [ABSTRACT FROM AUTHOR]

With the development of automatic driving and path planning technology, predicting the moving trajectory of pedestrians in dynamic scenes has become one of key and urgent technical problems. However, most of the existing techniques regard all pedestrians in the scene as equally important influence on the predicted pedestrian's trajectory, and the existing methods which use sequence‐based time‐series generative models to obtain the predicted trajectories, do not allow for parallel computation, it will introduce a significant computational overhead. A new social trajectory prediction network, Social‐ATPGNN which integrates both temporal information and spatial one based on ATPGNN is proposed. In space domain, the pedestrians in the predicted scene are formed into an undirected and non fully connected graph, which solves the problem of homogenisation of pedestrian relationships, then, the spatial interaction between pedestrians is encoded to improve the accuracy of modelling pedestrian social consciousness. After acquiring high‐level spatial data, the method uses Temporal Convolutional Network which could perform parallel calculations to capture the correlation of time series of pedestrian trajectories. Through a large number of experiments, the proposed model shows the superiority over the latest models on various pedestrian trajectory datasets. [ABSTRACT FROM AUTHOR]

Location prediction based on social media information is highly valuable in human mobility research and has multiple real-life applications. However, existing research methods often ignore social influences, largely ignoring implicit information regarding interactions between users and geographical locations. Additionally, they generally employ single modeling structures, which restricts the effective integration of complex spatiotemporal characteristics and factors influencing user mobility. In this context, we propose a novel network with physical and social awareness that expresses both physical and social influences of user mobility from a global perspective based on a heterogeneous graph constructed using users and spatial locations as nodes and relationships between them as edges. This graph enables the model to leverage information from connected nodes and edges to infer missing or unobserved data. The model predicts future locations of users by effectively integrating the temporal and spatial features of user trajectory series. The proposed model is validated using three social media datasets. The experimental results demonstrate that the proposed method outperforms the state-of-the-art baseline models. This indicates the importance of considering complex interactions between users and locations, as well as the various influences of physical and social spaces. [ABSTRACT FROM AUTHOR]

The use of social media has grown exponentially in recent years up to become a reflection of human social attitudes and to represent today the main channel for conducting discussions and sharing opinions. For this reason, the vast amount of information generated is often used for predicting outcomes of real-world events in different fields, including business, politics, and health, as well as in the entertainment industry. In this paper, we focus on how data from Twitter can be used to predict ratings of a large set of TV shows regardless of their specific genre. Given a show, the idea is to exploit features concerning the pre-release hype on Twitter for rating predictions. We propose a novel machine learning-based approach to the genre-independent TV show popularity prediction problem. We compared the performance of several well-known predictive methods, and as a result, we discovered that LSTM and Random Forest can predict the ratings in the USA entertainment market, with a low mean squared error of 0.058. Furthermore, we tested our model by using data of "never seen" shows, by deriving interesting results in terms of error rates. Finally, we compared performance against relevant solutions available in the literature, with discussions about challenges arousing from the analysis of shows in different languages. [ABSTRACT FROM AUTHOR]

The article argues that social catastrophes are the product of networks of unsaturated social relations that lead to the exponential spread of a social evil (pandemic, poverty, desertification, etc.). In the exponential curve of catastrophe there is an inflection point where, if unsaturated social relations are saturated, the catastrophe can be halted and ultimately avoided. The inflection point can be conceived as a generative relational complex in which both necessity and contingency of social relations are at work. Necessity is due to constitutive mechanisms that are automatic and, therefore, to some extent mathematically predictable. Contingency refers to non‐automatic, in principle unpredictable relational mechanisms. However, contingency can be of two kinds. It can mean "dependence on" other factors, which can have some degree of predictability, or can be understood as the possibility of "being otherwise", which is less predictable in its outcomes, but can also open up new opportunities to change the catastrophic trend. The reduction of the exponential curve to logistics can be expected if the networks of relationships at the inflection point are saturated in one way or another. This can be achieved by managing the contingent factors of both types in order to steer the morphogenetic processes of the social networks that configure the inflection point as a relational complex. [ABSTRACT FROM AUTHOR]

A nonlinear-dynamical algorithm for city planning is proposed as an impulse pattern formulation (IPF) for predicting relevant parameters such as health, artistic freedom, or financial developments of different social or political stakeholders over the cause of a planning process. The IPF has already shown high predictive precision at low computational cost in musical instrument simulations, brain dynamics, and human–human interactions. The social and political IPF consists of three basic equations of system state developments, self-adaptation of stakeholders, two adaptive interactions, and external impact terms suitable for respective planning situations. Typical scenarios of stakeholder interactions and developments are modeled by adjusting a set of system parameters. These include stakeholder reaction to external input, enhanced system stability through self-adaptation, stakeholder convergence due to adaptive interaction, as well as complex dynamics in terms of fixed stakeholder impacts. A workflow for implementing the algorithm in real city planning scenarios is outlined. This workflow includes machine learning of a suitable set of parameters suggesting best-practice planning to aim at the desired development of the planning process and its output. [ABSTRACT FROM AUTHOR]

Social anhedonia is a hallmark symptom of schizophrenia. Discrepancies in anticipated versus consummatory pleasure for non-social stimuli are well-documented. Thus, a similar emotional paradox may underlie social anhedonia. If so, our understanding of social anhedonia—including how to treat it in schizophrenia—could be enhanced. This project used a 5-day experience sampling method (ESM) to measure discrepancies between anticipated and consummatory pleasure for real-world social activities in people with schizophrenia and healthy controls (n = 30/group). ESM results were compared to laboratory assessments of negative symptoms and neurocognition. The schizophrenia group exhibited similar levels of anticipated and consummatory social pleasure as controls throughout daily life, and both groups were accurate in their short-term predictions of pleasure. Clinical interviews revealed those with schizophrenia showed significant deficits in long-term social pleasure prediction (i.e., a 1-week timeframe). Thus, people with schizophrenia may exhibit differences in ability to predict pleasure in the short-term versus the long-term. Negative symptoms and neurocognition were related to anticipated, but not consummatory, social pleasure, suggesting anhedonia is driven by deficits in thinking about pleasure, rather than inability to experience pleasure. Clinical implications include focusing on building upon short-term ability to predict pleasure in therapy to increase social motivation in schizophrenia. • People with schizophrenia report as much social pleasure in daily life as controls. • Those with schizophrenia can accurately predict social pleasure in the short-term. • Those with schizophrenia show deficits in long-term social pleasure prediction. • Cognitive impairment is implicated in ability to predict social pleasure. [ABSTRACT FROM AUTHOR]

Speakers tend to engage in adaptive behavior, known as entrainment, when they reuse their partner's linguistic representations, including lexical, acoustic prosodic, semantic, or syntactic structures during a conversation. Studies have explored the relationship between entrainment and social factors such as likeability, task success, and rapport. Still, limited research has investigated the relationship between entrainment and gaze. To address this gap, we conducted a within-subjects user study (N = 33) to test if gaze behavior of a robotic head affects entrainment of subjects toward the robot on four linguistic dimensions: lexical, syntactic, semantic, and acoustic-prosodic. Our results show that participants entrain more on lexical and acoustic-prosodic features when the robot exhibits well-timed gaze aversions similar to the ones observed in human gaze behavior, as compared to when the robot keeps staring at participants constantly. Our results support the predictions of the computers as social actors (CASA) model and suggest that implementing well-timed gaze aversion behavior in a robot can lead to speech entrainment in human-robot interactions. [ABSTRACT FROM AUTHOR]

In Web3 social platforms, i.e. social web applications that rely on blockchain technology to support their functionalities, interactions among users are usually multimodal, from common social interactions such as following, liking, or posting, to specific relations given by crypto-token transfers facilitated by the blockchain. In this dynamic and intertwined networked context, modeled as a financial network, our main goals are (i) to predict whether a pair of users will be involved in a financial transaction, i.e. the transaction prediction task, even using textual information produced by users, and (ii) to verify whether performances may be enhanced by textual content. To address the above issues, we compared current snapshot-based temporal graph learning methods and developed T3GNN, a solution based on state-of-the-art temporal graph neural networks' design, which integrates fine-tuned sentence embeddings and a simple yet effective graph-augmentation strategy for representing content, and historical negative sampling. We evaluated models in a Web3 context by leveraging a novel high-resolution temporal dataset, collected from one of the most used Web3 social platforms, which spans more than one year of financial interactions as well as published textual content. The experimental evaluation has shown that T3GNN consistently achieved the best performance over time and for most of the snapshots. Furthermore, through an extensive analysis of the performance of our model, we show that, despite the graph structure being crucial for making predictions, textual content contains useful information for forecasting transactions, highlighting an interplay between users' interests and economic relationships in Web3 platforms. Finally, the evaluation has also highlighted the importance of adopting sampling methods alternative to random negative sampling when dealing with prediction tasks on temporal networks. [ABSTRACT FROM AUTHOR]

Social controllability, or the ability to exert control during social interactions, is crucial for optimal decision-making. Inability to do so might contribute to maladaptive behaviors such as smoking, which often takes place in social settings. Here, we examined social controllability in nicotine-dependent humans as they performed an fMRI task where they could influence the offers made by simulated partners. Computational modeling revealed that smokers under-estimated the influence of their actions and self-reported a reduced sense of control, compared to non-smokers. These findings were replicated in a large independent sample of participants recruited online. Neurally, smokers showed reduced tracking of forward projected choice values in the ventromedial prefrontal cortex, and impaired computation of social prediction errors in the midbrain. These results demonstrate that smokers were less accurate in estimating their personal influence when the social environment calls for control, providing a neurocomputational account for the social cognitive deficits in this population. Pre-registrations: OSF Registries|How interoceptive state interacts with value-based decision-making in addiction (fMRI study). OSF Registries|COVID-19: social cognition, mental health, and social distancing (online study). A neurocomputational model of social forward thinking shows that smokers under-estimate the influence of their actions on future interactions, a cognitive deficit associated with aberrant prefrontal and midbrain activities. [ABSTRACT FROM AUTHOR]

A stereotype is a generalization about a class of people which is often used to make probabilistic predictions about individuals within that class. Can stereotypes can be understood as conditional probabilities that distinguish among groups in ways that follow Bayesian posterior prediction? For instance, the stereotype of Germans as industrious can be understood as the conditional probability of someone being industrious given that they are German. Whether such representations follow Bayes’ rule was tested in a replication and extension of past work. Across three studies (N = 2,652), we found that people’s judgments of different social categories were appropriately Bayesian, in that their direct posterior predictions were aligned with what Bayes’ rule suggests they should be. Moreover, across social categories, traits with a high calculated diagnostic ratio generally distinguished stereotypic from non-stereotypic traits. The effects of cognitive ability, political orientation, and motivated stereotyping were also explored. [ABSTRACT FROM AUTHOR]

In today's society, people increasingly need information acquisition due to the rapid development of science and technology and the consequent increase in available data. However, finding the information users need from this vast data has become challenging. To tackle this problem, recommending preferred information to users is becoming increasingly important. However, accurately recommending information by analyzing existing models such as GraphRec is still a challenging problem. A method called PSO_KFCM is proposed in this paper to solve this problem better. The technique combines Particle Swarm Optimization (PSO) with hybrid optimization and the kernel fuzzy C-means clustering technique to cluster similar recommendation data into one class. This way, the complexity and randomness of the recommendation data are reduced. It improves the speed and accuracy of the model prediction, which lays a solid foundation for the subsequent recommendation. Various factors will impact the recommendation process, and channel and spatial characteristics are essential. CBAM attention is added to the original attention mechanism to fully utilize these features in the recommendation data to enhance its performance. Furthermore, this paper proposes a social recommendation prediction method that combines CBAM attention and PSO_KFCM clustering and introduces a new social model called TTYGNN. The TTYGNN model optimizes the recommendation effect while maintaining the original advantages, enabling users to obtain the required information more quickly and accurately. To verify the effectiveness and practicality of the proposed model, extensive experimental comparisons were conducted on two widely used datasets. The results show that the TTYGNN model outperforms similar methods in all indicators, proving its superiority in information recommendation. [ABSTRACT FROM AUTHOR]

As the landscape of online social networks continues to evolve, the task of expanding connections and uncovering novel relationships presents a growing complexity. Link prediction emerges as a crucial strategy, harnessing the current network dynamics to forecast future interactions among users. While traditional single-layer network link prediction models boast a storied legacy, recent attention has shifted towards tackling analogous challenges within the realm of multilayer networks. This paradigm shift underscores the critical role of extracting topological and multimodal features to effectively evaluate link weights, thereby enriching link prediction within weighted networks. Furthermore, the establishment of trustworthy pathways between users emerges as a pivotal tactic for translating unweighted similarities into meaningful weighted metrics. Leveraging the foundational principles of local random walk techniques, this paper introduces the trustworthy Lévy-flight semi-local (TLFSL) random walk framework for link prediction in multilayer social networks. By seamlessly integrating intralayer and interlayer information, TLFSL harnesses a dependable Lévy-flight random walk mechanism to anticipate new links within target layers of multilayer networks. Traditional local random walk techniques often overlook global relationships, as they confine path exploration to immediate neighbours. However, the absence of a direct edge between nodes does not necessarily imply a lack of relationship; nodes with semantic affinity may be spatially distant within the network. To overcome this limitation, we introduce the concept of semi-local random walk, which enables walker hopping with a wider global perspective. Meanwhile, TLFSL includes a distributed local community detection strategy to improve the performance of TLFSL in dealing with large-scale networks. Rigorous experimentation across diverse real-world multilayer networks consistently demonstrates TLFSL's superior performance compared to equivalent methods. [ABSTRACT FROM AUTHOR]

Aim/purpose - Consumers are showing interest in digital marketing tools that augment purchase intention (PI). Among several products, apparels are well-known for its own creativity and fashionable brands and the increasing rate of purchase also augments economic condition of the country. The present investigation aimed to predict PI as per dataset of Social Media Marketing Activities (SMMA) viz. Facebook (FB) add (advertisement), other social media (SM) add, short message service (SMS) add and online add among consumers who visited organized apparel sector in eastern India. Design/methodology/approach - The forecast of PI through machine learning algorithm modelling plays an important role in recent days. In the present study, 599 datasets of consumers as per categorization of Likert scale in which 14 algorithms were selected by using WEKA tool. Findings - The better performance accuracy predicted that three models followed by other eight models as per training and testing dataset. The PI has cumulative effect when these four SMMA viz. FB add, other SM add, SMS add and online add are functioning at a time. Research implications/limitations - As per the study methods, a limitation of the research attributes viz. FB add, other platform, adv., SMS adv. and online adv. are focused on SMMA, without consideration of each platform adv. being used as Twitter, YouTube, Instagram, etc. Each platform along with FB may provide better data accuracy on machine learning algorithms. Other limitation observed is small number of respondents (599 nos.) and the study was carried out only in one region of India. Originality/value/contribution - The findings showed that performance accuracy of PI of apparel products through the influence of SMMA was much better as per ML algorithms in the Eastern part of India. [ABSTRACT FROM AUTHOR]

Poverty rates are not a taboo subject. In actuality, it occurs in every nation on earth when governments and social activists fight to lower the poverty rate. Unfortunately, there are a number of problems with the current methods for identifying the appropriate targeted underprivileged population to whom financial aid should be given, including data openness and inaccurate, redundant, or imbalanced data. The goal of this research set out to determine the ways in which social activists in india have contributed to the decline of poverty. The following were the goals of the study: to determine the influence of social activists in enhancing livelihoods in order to forecast a decrease in poverty in india and to evaluate social activists' impact on india's efforts to reduce poverty through forecasting. Several feature engineering and data cleaning pre-processing methodologies are used in this research's machine learning approach, which is then followed by three different regression algorithms for predictive analysis. Using the costa rican household poverty dataset, the proposed machine learning model is tested. According to the experimental results, the random forest regressor outperforms the other two regression models tested in terms of r2 and rmse score, scoring 0.8355 well-fitted and 0.148 error rates, respectively. [ABSTRACT FROM AUTHOR]

This research aimed to explore relations between self-discrepancies, particularly in the actual and ought self, on one side, and depression and social anxiety on the other. The inconsistency in findings in existing studies is speculated to arise from variations in the definition of the ought self, which represents expectations of significant others about who we should be, with the term significant others not being defined. The results of research conducted on 543 high school students showed that all discrepancies are positively correlated with depression and social anxiety, and negatively with two dimensions of self-esteem: self-competence and self-liking. The findings indicate that all self-discrepancies serve as significant predictors of depression, with the discrepancy in the actual-ideal self and the actual-ought self by parents demonstrating a stronger predictive power than the discrepancy between the actual and ought self by peers. With regards to social anxiety, the discrepancy between actual and ought self by peers is a more influential determinant than the discrepancy between the actual and ought self by parents. It was also found that the discrepancy between the actual and ideal self is more significant than the expected discrepancy in the actual and ought self by peers in the prediction of social anxiety. Data on self-competence showed it was a mediating variable in the correlation between discrepancy in actual-ought self by parents, as well as actual and ideal self, and depression. Finally, self-liking appeared to be a mediating variable in the correlation between the actual-ideal discrepancy and social anxiety. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is gaining popularity for severe weather forecasting. Recently, the authors developed an AI system using machine learning (ML) to produce probabilistic guidance for severe weather hazards, including tornadoes, large hail, and severe winds, using the National Severe Storms Laboratory's (NSSL) Warn-on-Forecast System (WoFS) as input. Known as WoFS-ML-Severe, it performed well in retrospective cases, but its operational usefulness had yet to be determined. To examine the potential usefulness of the ML guidance, we conducted a control and treatment (experimental) group experiment during the 2022 NOAA Hazardous Weather Testbed Spring Forecasting Experiment (HWT-SFE). The control group had full access to WoFS, while the experimental group had access to WoFS and ML products. Explainability graphics were also integrated into the WoFS web viewer. Both groups issued 1-h convective outlooks for each hazard. After issuing their forecasts, we surveyed participants on their confidence, the number of products viewed, and the usefulness of the ML guidance. We found the ML-based outlooks outperformed non-ML-based outlooks for multiple verification metrics for all three hazards and were rated subjectively higher by the participants. However, the difference in confidence between the two groups was not significant, and the experimental group self-reported viewing more products than the control group. Participants had mixed sentiments toward explainability products as it improved their understanding of the input/output relationships, but viewing them added to their workload. Although the experiment demonstrated the usefulness of ML guidance for severe weather forecasting, there are avenues to improve upon the ML guidance, and more training and exposure are needed to exploit its benefits fully. Significance Statement: We developed an artificial intelligence (AI) system to predict tornadoes, large hail, and damaging straight-line winds. The AI system was leveraged in real time during the 2022 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. This study reveals that forecasters using AI guidance produced more reliable and spatially accurate outlooks than those without. While AI and complementary explainability products did not reduce forecaster workload, both demonstrated great potential for improving severe weather forecasting. This research also highlights the importance of user feedback in refining AI tools for severe weather forecasting. [ABSTRACT FROM AUTHOR]

The problem of recommender system is very popular with myriad available solutions. Recommender systems recommend items to users and help them in narrowing their search from huge amount of options available to the user. In this work, a novel approach for the recommendation problem is proposed by incorporating techniques from the link prediction problem in social networks. The proposed approach models the typical user-item information as a bipartite network, and predicts future links using link prediction measures, in which link prediction would actually mean recommending an item to a user. The standard recommender system methods suffer from the problems of sparsity and scalability. Since link prediction measures involve computations pertaining to local neighborhoods in the network, this approach would lead to a scalable solution to recommendation. In this work, we present top k links that are predicted by link prediction measures as recommendations to the users. Our work initially applies different existing link prediction measures to the recommendation problem by making suitable adaptations. The prime contribution of this work is to propose a recommendation framework routed from link prediction problem in social networks, that effectively utilizes probabilistic measures of link prediction and embed temporal data accessible on existing links. The proposed approach is evaluated on one movie-rating dataset of MovieLens, two product-rating datasets of Epinions & Amazon and one hotel-rating dataset of TripAdvisor. Results show that the link prediction measures based on temporal probabilistic information prove to be more effective in improving the quality of recommendation. Especially, Temporal cooccurrence probability measure improves the area under ROC curve (AUROC) by 10% for MovieLens, 23% for Epinions, 17% for TripAdvisor, 9% for Amazon over standard item-based collaborative filtering method. Similar improved performance is observed in terms of area under Precision-Recall curve (AUPR) as well as Normalized Rank-Score. [ABSTRACT FROM AUTHOR]

Influence maximization (IM) in dynamic social networks is an optimization problem to analyze the changes in social networks for different periods. However, the existing IM methods ignore the context propagation of interaction behaviors among users. Hence, context-based IM in multiplex networks is proposed here. Initially, multiplex networks along with their contextual data are taken as input. Community detection is performed for the network using the Wilcoxon Hypothesized K-Means (WH-KMA) algorithm. From the detected communities, the homogeneous network is used for extracting network topological features, and the heterogeneous networks are used for influence path analysis based on which the node connections are weighted. Then, the influence-path-based features along with contextual features are extracted. These extracted features are given for the link prediction model using the Parametric Probability Theory-based Long Short-Term Memory (PPT-LSTM) model. Finally, from the network graph, the most influencing nodes are identified using the Linear Scaling based Clique (LS-Clique) detection algorithm. The experimental outcomes reveal that the proposed model achieves an enhanced performance. [ABSTRACT FROM AUTHOR]

This article refers to the topic of psychological (mental/emotional) and social pain in the prediction of suicidality (suicide and its associated behaviors) as an extraordinarily complex and pressing societal and adult educational challenge. The aim of this review article is to present a literature review of the current picture of the evidence regarding the impact of psychological and social pain on suicidal behavior as a societal and adult educational problem. Currently known biological factors are weak predictors of future suicidal behaviors. Psychological pain is a transdiagnostic significant predictive factor of suicidality, even in the absence of a diagnosed depression. Psychological pain has been rarely evaluated or examined in routine practice for suicide reduction. A valid tool to measure social pain is a necessary step in decreasing and minimizing rates of suicidal behavior in an at risk population of socially excluded adults. Additional practical implications for the impact of psychological and social pain on suicidality prediction in the settings of adult education are highly and urgently recommended. The hypothetical impact of different social factors (e.g., immigration status, gender, multiple psychoactive substance use as social norms, general self-efficacy) on the experience of psychological pain will be studied by mixed methods research in the field of sociology in an at risk population of socially excluded adults. [ABSTRACT FROM AUTHOR]

This systematic literature review evaluates the role of machine learning, artificial intelligence (AI), and social determinants of health (SDOH) in identifying loneliness during the COVID-19 pandemic. By examining various studies and articles through a comprehensive search of databases EBSCOhost, Medline Complete, Academic Search Complete, Directory of Open Access Journals, and Complementary Index, the research team sought to discern consistent themes and patterns. We identified four constructs central to understanding the impact of the pandemic on societal well-being: (1) the prediction of compliance with COVID-19 measures, (2) the prediction of loneliness and its effects, (3) the prediction of well-being and social inclusion, and (4) the prediction of drug use. Within these constructs, prevalent themes related to opioid overdose, stress levels, mental health, well-being, and cognitive decline emerged. The adherence to the PRISMA 2020 checklist has resulted in a PRISMA flow diagram that categorizes the selected literature. The findings of this review, including the proportion of studies predicting various attributes related to loneliness, demonstrate the critical intersections between machine learning, AI, SDOH, and the psychosocial phenomenon of loneliness amidst a global health crisis. The review results provide a summary of the occurrences and predictive percentages of each construct as determined by the literature, contributing to a nuanced understanding of the pandemic's multifaceted impact on loneliness, social isolation, and drug use. Using AI to predict these constructs has remarkable capabilities in identifying individuals at risk and facilitating timely interventions to mitigate adverse outcomes and promote mental health resilience in the face of challenges such as the COVID-19 pandemic. Moving forward, future research is warranted to refine AI algorithms, validate predictive models and utilize AI-based interventions in healthcare and mental health services while ensuring data security, and individuals' privacy. [ABSTRACT FROM AUTHOR]

Although average body size in the U.S. has increased in recent decades, stigma directed at individuals with higher weight has not diminished. In this study, we explored this phenomenon by investigating the relationship between people's perceived social norms regarding higher weight and their reported levels of weight bias (i.e., anti-fat attitudes). Our predictions for perceived social norms drew on the concepts of intergroup contact and ingroup favoritism, which were also probed in this study. We hypothesized that both greater descriptive norms and more favorable injunctive norms regarding higher weight would be associated with lower reported weight bias. Individuals' quantity and quality of social contact with people with higher weight were also predicted to be associated with lower weight bias. Finally, we predicted that individuals who perceived themselves as heavier would display ingroup favoritism (i.e., report less weight bias). Participants (N = 272) from the United States completed a set of online questionnaires about their perceived social norms, social contact with people with higher weight, and explicit weight bias. We found support for each of these pre-registered predictions (ps < 0.03), and post hoc analyses revealed that quality, but not quantity, of social contact with individuals with higher weight was an important predictor of lower weight bias. Together, these findings provide insight into the social psychology of weight bias and help to lay a theoretical foundation for future efforts to reduce weight stigma. [ABSTRACT FROM AUTHOR]

While the ubiquity and importance of narratives for human adaptation is widely recognized, there is no integrative framework for understanding the roles of narrative in human adaptation. Research has identified several cognitive and social functions of narratives that are conducive to well-being and adaptation as well as to coordinated social practices and enculturation. In this paper, we characterize the cognitive and social functions of narratives in terms of active inference, to support the claim that one of the main adaptive functions of narrative is to generate more useful (i.e., accurate, parsimonious) predictions for the individual, as well as to coordinate group action (over multiple timescales) through shared predictions about collective behavior. Active inference is a theory that depicts the fundamental tendency of living organisms to adapt by proactively inferring the causes of their sensations (including their own actions). We review narrative research on identity, event segmentation, episodic memory, future projections, storytelling practices, enculturation, and master narratives. We show how this research dovetails with the active inference framework and propose an account of the cognitive and social functions of narrative that emphasizes that narratives are for the future--even when they are focused on recollecting or recounting the past. Understanding narratives as cognitive and cultural tools for mutual prediction in social contexts can guide research on narrative in adaptive behavior and psychopathology, based on a parsimonious mechanistic model of some of the basic adaptive functions of narrative. [ABSTRACT FROM AUTHOR]

This paper aims to highlight the latest and most widely used artificial intelligence methods in text similarity prediction. The study focuses on articles published from 2010 to 2022 in the field of text similarity prediction and algorithms applied in this field. In this paper, systematic scientific comparisons have been made between existing approaches for predicting text similarity to answer the question raised in this study about the most used and accurate approach. Through previous studies and the comparisons made in this paper, the Ant Colony Optimization Algorithm (ACO) approach was found to be the most frequently used in text similarity prediction and solving scheduling problems. [ABSTRACT FROM AUTHOR]

With the progress of the Internet and information technology, emotion analysis has been applied to analyse the emotional orientation and evolution trend of online public opinion of online tweets. At present, most of the existing methods use econometric model and machine learning algorithm to predict the trend of online public opinion. Although these methods have achieved good prediction results, they do not take into account the influence of internal factors on network public opinion prediction, such as mutual migration among emotion classes. The emotion may change dynamically because different events trigger it in the evolution process. In this view, this article proposes a novel method, called Deviation Rule Markov Model (DRMM), to predict the emotional change trend of Internet users in online public opinion by analysing the correlation between Internet users' emotional categories. Structurally, the proposed DRMM involves various processes such as pre-processing, emotion classification, data mining and transfer prediction. For the processing of network comment data, the proposed model initially undergoes pre-processing to delete unnecessary data. Then, the extended fuzzy emotion ontology is used to annotate the emotion class of the comment data. Besides, an extended association rule mining algorithm is used in the emotion association analysis process to obtain the transfer probability between emotion classes. Moreover, Markov chain is used to construct an emotional state transition matrix to predict the transition probability of positive or negative emotions. According to the predicted single emotion transfer probability results, the analytic hierarchy process is used to assign values to different emotion classes, and finally, the transfer probability of the overall emotion in a certain period is obtained. Compared with the actual case, the mean absolute error (MAE) and root mean square error (RMSE) of the proposed model are 2.7119 and 3.7254, respectively, which has good prediction performance. [ABSTRACT FROM AUTHOR]

Summary: Software stakeholders report bugs in issue tracking system (ITS) with manually labeled priorities. However, the lack of knowledge and standard for prioritization may cause stakeholders to mislabel the priorities. In response, priority predictors are actively developed to support them. Prior studies trained machine learners based on textual similarity, categorical, and numeric technical features of bug reports. Most models were validated by time‐insensitive approaches, and they were producing suboptimal results for practical usage. While they ignored the social aspects of ITS, the technical aspects were also limited in surface features of bug reports. To better model the bug report, we extract their topic and most similar code structures. Since ITS bridges users and developers as the main contributors, we also integrate their experience, sentiment, and socio‐technical features to construct a new dataset. Then, we perform two‐classed and multiclassed bug priority prediction based on the dataset. We also introduce adversarial training using generated training data with random word swap and random word deletion. We validate our model in within‐project, cross‐project, and time‐wise scenarios, and it outperforms the two baselines by up to 15% in area under curve‐receiver operating characteristics (AUC‐ROC) and 19% in Matthews correlation coefficient (MCC). We reveal involving contributor (i.e., assignee and reporter) features such as sentiment that could boost prediction performance. Finally, we test statistically the mean and distribution of the features that reflect the differences in social and technical aspects (e.g., quality of communication and resource distribution) between high and low priority reports. In conclusion, we suggest that researchers should consider both social and technical aspects of ITS in bug report priority prediction and introduce adversarial training to boost model performance. [ABSTRACT FROM AUTHOR]

Link prediction is recognized as a crucial means to analyze dynamic social networks, revealing the principles of social relationship evolution. However, the complex topology and temporal evolution characteristics of dynamic social networks pose significant research challenges. This study introduces an innovative fusion framework that incorporates entropy, causality, and a GCN model, focusing specifically on link prediction in dynamic social networks. Firstly, the framework preprocesses the raw data, extracting and recording timestamp information between interactions. It then introduces the concept of "Temporal Information Entropy (TIE)", integrating it into the Node2Vec algorithm's random walk to generate initial feature vectors for nodes in the graph. A causality analysis model is subsequently applied for secondary processing of the generated feature vectors. Following this, an equal dataset is constructed by adjusting the ratio of positive and negative samples. Lastly, a dedicated GCN model is used for model training. Through extensive experimentation in multiple real social networks, the framework proposed in this study demonstrated a better performance than other methods in key evaluation indicators such as precision, recall, F1 score, and accuracy. This study provides a fresh perspective for understanding and predicting link dynamics in social networks and has significant practical value. [ABSTRACT FROM AUTHOR]

Tweet prediction plays a crucial role in sentiment analysis, trend forecasting, and user behavior analysis on social media platforms such as X (Twitter). This study delves into optimizing Machine Learning (ML) models for precise tweet prediction by capturing intricate dependencies and contextual nuances within tweets. Four prominent ML models, i.e. Logistic Regression (LR), XGBoost, Random Forest (RF), and Support Vector Machine (SVM) were utilized for disaster-related tweet prediction. Our models adeptly discern semantic meanings, sentiment, and pertinent context from tweets, ensuring robust predictive outcomes. The SVM model showed significantly higher performance with 82% accuracy and an F1 score of 81%, whereas LR, XGBoost, and RF achieved 79% accuracy with average F1-scores of 78%. [ABSTRACT FROM AUTHOR]

Link prediction in social network is an important topic due to its applications like finding collaborations and recommending friends. Among existing link prediction methods, similarity-based approaches are found to be most effective since they examine the number of common neighbours (CN). Current work presents a novel link prediction algorithm based on particle swarm optimization (PSO) and implemented on four real world datasets namely, Zachary's karate club (ZKC), bottlenose dolphin network (BDN), college football network (CFN) and Krebs' books on American politics (KBAP). It consists of three experiments: i) to find the measures on existing methods and compare them with our proposed algorithm; ii) to find the measured values of the existing methods along with our proposed one to determine future links among nodes that have no CN; and iii) to find the measures of the methods to determine future links among nodes having same number of CN. In experiment 1, our proposed approach achieved 75.88%, 78.34%, 82.63% and 78.36% accuracy for ZKC, BDN, CFN, and KBAP respectively. These results beat the performances of traditional algorithms. In experiment 2, the accuracies are found as 75.53%, 74.25%, 81.63% and 78.34% respectively. In experiment 3, accuracies are detected as 72.75%, 81.53%, 78.35% and 75.13% respectively. [ABSTRACT FROM AUTHOR]

In competitive interactions, humans have to flexibly update their beliefs about another person's intentions in order to adjust their own choice strategy, such as when believing that the other may exploit their cooperativeness. Here we investigate both the neural dynamics and the causal neural substrate of belief updating processes in humans. We used an adapted prisoner's dilemma game in which participants explicitly predicted the coplayer's actions, which allowed us to quantify the prediction error between expected and actual behavior. First, in an EEG experiment, we found a stronger medial frontal negativity (MFN) for negative than positive prediction errors, suggesting that this medial frontal ERP component may encode unexpected defection of the coplayer. The MFN also predicted subsequent belief updating after negative prediction errors. In a second experiment, we used transcranial magnetic stimulation (TMS) to investigate whether the dorsomedial prefrontal cortex (dmPFC) causally implements belief updating after unexpected outcomes. Our results show that dmPFC TMS impaired belief updating and strategic behavioral adjustments after negative prediction errors. Taken together, our findings reveal the time course of the use of prediction errors in social decisions and suggest that the dmPFC plays a crucial role in updating mental representations of others' intentions. [ABSTRACT FROM AUTHOR]

Accurately predicting the future trajectory of pedestrians is critical for tasks such as autonomous driving and robot navigation. Previous methods for pedestrian trajectory prediction dealt with social interaction and pedestrian movement factors either concurrently or sequentially, neglecting the link between them. Therefore, a spatial–temporal interaction-aware graph convolution network (STIGCN) is proposed for pedestrian trajectory prediction. STIGCN considers the correlation between social interaction and pedestrian movement factors to achieve more accurate interaction modeling. Specifically, we first constructed spatial and temporal graphs to model social interactions and movement factors. Then, we designed the spatial–temporal interaction-aware learning to utilize the spatial interaction features of each moment to assist the temporal interaction modeling and utilize the temporal interaction features of each pedestrian to assist the spatial interaction modeling, resulting in more accurate interaction modeling. Finally, a time-extrapolator pyramid convolution neural network (TEP-CNN) is designed to jointly estimate the two-dimensional Gaussian distribution parameters of future trajectories by combining the prediction features from multiple layers. Experimental results on two benchmark pedestrian trajectory prediction datasets show that our proposed method outperforms existing methods in terms of average displacement error and final displacement error and achieves more accurate predictions for pedestrian motions such as convergence and encounter. [ABSTRACT FROM AUTHOR]

The amino acid tryptophan (Trp) is a precursor of the neurotransmitter serotonin. Trp supplementation or other forms of serotonergic enhancement generally promote pro-social behavior, decreasing aggression, and also feeding in different animals. However, past research has been conducted in confined spaces, and there is little work in naturalistic conditions where animals move and associate more freely. We gave a Trp-enriched diet to a free-flying flock of common waxbills (Estrilda astrild) in semi-natural conditions and monitored group foraging, aggressions during feeding, and the social network. Contrary to expectations, aggressiveness and feeding increased during Trp supplementation. Consistent with the prediction of increased social associations, foraging groups became larger, and individuals joined more foraging groups, but these changes appear driven by increased appetite during Trp treatment. Also, the mean strength of associations in the social network did not change. Overall, Trp supplementation affected group behavior in this free-flying flock, but mostly in directions unanticipated based on research conducted in small spaces. To harmonize our results with those found in small confined spaces, we hypothesize that free-flying birds have energetic requirements not experienced in lab-housed individuals, which may impact social behavior and responses to Trp. [ABSTRACT FROM AUTHOR]

A recently proposed "Hyperfocusing hypothesis" suggests that schizotypy is associated with a more narrow but more intense way of allocating attention. The current study aims to test a vital prediction of this hypothesis in a social context, that schizotypy may be related to greater difficulty overcoming the distracting effects of gaze. This could cause a longer time to respond to targets that are invalidly cued by gaze. The current study tested this prediction in a modified Posner cueing paradigm by using P3 as an indicator for attentional resources. Seventy-four young healthy individuals with different levels of schizotypy were included, they were asked to detect the location of a target that was cued validly or invalidly by the gaze and head orientation. The results revealed that (a) schizotypy is associated with hyperfocusing on gaze direction, leading to greater difficulty overcoming the distracting effect of gaze. The higher the trait-schizotypy score, the more time needed to respond to targets that were invalidly cued by gaze (b) schizotypy is associated with reduced P3 which is directed by social communicative stimuli. The higher the trait-schizotypy score, the smaller the amplitude of P3 (c) the relationship between schizotypal traits and response times of the gaze-invalid condition is fully intermediated by P3. The findings of the current study suggest the P3 component may be a crucial neural mechanism underlying joint attention deficits in schizophrenia. [ABSTRACT FROM AUTHOR]

It is known that people can distinguish authentic from inauthentic emotional displays. It is also known that emotions are generally impactful in crowdfunding pitches. Yet, the potential lynchpin-like role that displays of authentic emotion may play in funding pitches has been overlooked in entrepreneurial resource acquisition research. More importantly, research on authenticity has not uncovered the mechanisms through which display authenticity positively affects observers' responses. Our work fills this gap by developing a theoretical model that explains the underlying processes of entrepreneurs' display authenticity and success in crowdfunding. Consistent with the predictions of the emotions as social information model, results from a field study and an experiment reveal the mediating roles of inferential and affective processes. Furthermore, our findings provide evidence for the moderating role of funders' epistemic motivation on performance. We find that, depending on path, these effects take different directions. [ABSTRACT FROM AUTHOR]

Social emotion prediction aims to predict readers' emotion, for example, emotion distributions evoked by documents (e.g., news articles). It makes a significant contribution to social media applications, such as opinion summary, election prediction, and emotions investigation of society. While recent studies have focused on encoding consecutive word sequences in documents using neural network models and leveraging topical information, it is essential to acknowledge the influence of documents sharing similar topics or being related to similar events on evoking readers' emotions. The interactions among documents can significantly impact social emotion prediction. In this paper, we propose a novel approach to model the interactions among documents by constructing a heterogeneous graph. This graph captures the interaction among documents based on global word co-occurrence patterns in a corpus and the emotional scores of words obtained from emotion lexicons. Additionally, we develop heterogeneous graph convolution attention network (HGCA) to embed the heterogeneous graph. This network effectively captures the importance of different neighboring nodes and different node types, enabling comprehensive emotion prediction. Furthermore, we develop Taylor series expansion-based Transformer (Tayformer) to derive initialized node representations that can be co-trained with our graph network while having low memory complexity. Experimental results on four benchmark datasets show the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Anonymous encounter cooperation is very common in human behavior. While different types of SVO affect cooperation, the effects on encounter collaboration have not been systematically studied. Here, we experimentally uncover the mechanisms underlying this behavior with SVO. Current study recruited 688 college students from southwest China, through social media and campus online forums. The participants' average age was 21.4 years (SD = 2.05). During the experiment, the researchers manipulated group identity (dialect paradigm) and induced emotions (by showing a short movie). Participants completed measurements of SVO (using a social value orientation slider scale), social projection (using the false consensus paradigm), and initial cooperation (by playing a one-shot Prisoner's Dilemma game). The results showed that SVO, social projection, and cooperation were positively associated. Mediation analyses indicated that social projection partly mediated the effect of SVO on cooperation. Moreover, the relationship between SVO and social projection was moderated by emotion and group identity. The findings suggest that when relying solely on intuition to make judgments, a person's prosocial personality determines their decision to cooperate or not, based on heuristic information. Overall, this study strengthens the conceptual model of SVO and enhances our understanding of unexpected collaboration in some contexts. [ABSTRACT FROM AUTHOR]