Analysis Of Social Behavior Research Articles

Detection and Model of Thermal Traces Left after Aggressive Behavior of Laboratory Rodents

Automation of complex social behavior analysis of experimental animals would allow for faster, more accurate and reliable research results in many biological, pharmacological, and medical fields. However, there are behaviors that are not only difficult to detect for the computer, but also for the human observer. Here, we present an analysis of the method for identifying aggressive behavior in thermal images by detecting traces of saliva left on the animals’ fur after a bite, nape attack, or grooming. We have checked the detection capabilities using simulations of social test conditions inspired by real observations and measurements. Detection of simulated traces different in size and temperature on single original frame revealed the dependence of the parameters of commonly used corner detectors (R score, ranking) on the parameters of the traces. We have also simulated temperature of saliva changes in time and proved that the detection time does not affect the correctness of the approximation of the observed process. Furthermore, tracking the dynamics of temperature changes of these traces allows to conclude about the exact moment of the aggressive action. In conclusion, the proposed algorithm together with thermal imaging provides additional data necessary to automate the analysis of social behavior in rodents.

Effect of Space Configurational Attributes on Social Interactions in Urban Parks

Urban parks are one of the most common spaces for social interactions in modern cities. The design of park spaces, especially space configuration, has significant influences on people’s social behaviors in parks. In this study, the associations between space configurational attributes and social interactions were investigated using space syntax theory. An observation analysis of social behaviors was carried out in two urban parks in Beijing, China. Nine space configurational attributes, including depth to the gate, depth to the main road, connectivity, normalized angular integration (NAIN), and normalized angular choice (NACH) with three radii, were calculated using a segment model. The variance analysis and regression analysis reveal the strong joint effect of space type, space scale factors, and space configurational attributes on social interaction behaviors in parks. The personal interaction group contained 23% of the total observed people involved in social interactions. Pathway length, zone area, and NACH-10K (NACH with a radius of 10,000 m) are positively associated with the number of people involved in personal interactions. For the social interaction group (77% of the total observed people), the space scale and depth to main city road were found to have a positive and negative influence on social interaction intensity.

The COVID-19 Pandemic in Ukraine: Process Evaluation, Opinion Analysis

The SARS-CoV-2 coronavirus pandemic has proved to be a real test of survival for modern humanity. The global crisis has affected not only the health care system, but also the entire social sphere. For the second year in a row, the world economy is under stress, and countries around the world are assessing and forecasting the scale of economic losses, trying to determine the size of direct and indirect demographic losses. It’s impossible to coordinate the implementation of anti-epidemic measures and disease prevention without adequate response and support from the population. And this requires an analysis of social behavior, its motivation and attitude to the relevant actions of state and local authorities.
 The article considers the current trends in the spread of SARS-CoV2 infection in Ukraine and in some European countries. Seasonal fluctuations and cyclical changes in the prevalence of this disease and the number of deaths caused by coronavirus are analyzed. The 7-day seasonal fluctuations are to some extent explained by the peculiarities of the initial registration of such events in Ukraine. After all, some of the test results and medical certificates of death caused by coronavirus disease are submitted to the relevant authorities with some delay. There is a multi-day registration lag after the weekend, when most instances do not work, so the input information is processed with a delay.
 The statistical estimates of expected daily number of infected with COVID-19 virus and deaths from this cause (data of the National Security and Defense Council of Ukraine for March-December 2020 and January 2021) are based on an analysis of these trends in the context of effectiveness of preventive measures from “quarantine in free day” up to a hard lockdown.
 The article provides a quantitative assessment of the population's response to anti-epidemic measures of government agencies. Database of research comprised a series of opinion polls within the monitoring “Ukraine under quarantine” and “Assessment of Healthcare”, the sociological group “Rating” for 2020–2021. The results of the statistical analysis of psycho-emotional state of the population during the COVID-crisis showed significant differences in the responses of certain respondents groups by age, gender and place of residence.
 Using non-parametric methods of statistics, the level of Ukrainian population trust in international and domestic medical institutions, as well as in information coming from various sources was estimated. It turned out that respondents are almost 20 times more willing to read news from regular Internet resources than from the official website of the Ministry of Health of Ukraine and rely more on WHO data.
 In the perspective of further research, a statistical analysis of the demographic consequences of the spread of SARS-CoV2 infection in Ukraine is planned.

Rating prediction based on combination of review mining and user preference analysis

Review websites allow users to share their reviews of products or businesses, give ratings to products or businesses, and interact with other users. Due to the rapid growth of online review data, users face the problem of information overload. To resolve this problem, many researches have proposed various recommendation methods based on the analysis of users’ ratings. Besides user ratings, the review websites contain unstructured textual data and information of different aspects which has different impact and importance to both users and businesses. It may lead to inaccurate rating predictions, as it is difficult to know users’ preferring aspects and their corresponding importance by analyzing users’ rating data.To resolve the above-stated problems, this research proposed aspect-based rating prediction methods, i.e., ARPM and ARPM-Social, which integrate aspect detection and sentiment analysis to generate user preference and business performance, combined with the results of social behavior analysis to predict the ratings of the businesses that users will be interested in the future. Based on the experimental results, the proposed methods perform better than other traditional rating-based prediction methods. They can effectively analyze various aspects and sentiments from the reviews of users and businesses, as well as improve the accuracy of rating predictions.

Embryo Injections for CRISPR-Mediated Mutagenesis in the Ant <em>Harpegnathos saltator </em>

The unique traits of eusocial insects, such as social behavior and reproductive division of labor, are controlled by their genetic system. To address how genes regulate social traits, we have developed mutant ants via delivery of CRISPR complex into young embryos during their syncytial stage. Here, we provide a protocol of CRISPR-mediated mutagenesis in Harpegnathos saltator, a ponerine ant species that displays striking phenotypic plasticity. H. saltator ants are readily reared in a laboratory setting. Embryos are collected for microinjection with Cas9 proteins and in vitro synthesized small guide RNAs (sgRNAs) using home-made quartz needles. Post-injection embryos are reared outside the colony. Following emergence of the first larva, all embryos and larvae are transported to a nest box with a few nursing workers for further development. This protocol is suitable for inducing mutagenesis for analysis of caste-specific physiology and social behavior in ants, but may also be applied to a broader spectrum of hymenopterans and other insects.

Embryo Injections for CRISPR-Mediated Mutagenesis in the Ant Harpegnathos saltator.

The unique traits of eusocial insects, such as social behavior and reproductive division of labor, are controlled by their genetic system. To address how genes regulate social traits, we have developed mutant ants via delivery of CRISPR complex into young embryos during their syncytial stage. Here, we provide a protocol of CRISPR-mediated mutagenesis in Harpegnathos saltator, a ponerine ant species that displays striking phenotypic plasticity. H. saltator ants are readily reared in a laboratory setting. Embryos are collected for microinjection with Cas9 proteins and in vitro synthesized small guide RNAs (sgRNAs) using home-made quartz needles. Post-injection embryos are reared outside the colony. Following emergence of the first larva, all embryos and larvae are transported to a nest box with a few nursing workers for further development. This protocol is suitable for inducing mutagenesis for analysis of caste-specific physiology and social behavior in ants, but may also be applied to a broader spectrum of hymenopterans and other insects.

Social Group Behavior Analysis Model Integrating Multitask Learning and Convolutional Neural Network

Social group behavior analysis has always been the key research direction of sociologists and psychologists. With the rapid development of the Internet of Things and the proposal of deep learning theory, convolutional neural networks are also used in social group research. In the process of social development, group incidents continue to increase, and there are more and more studies on social group behavior analysis. Although the research content and research methods are also richer, the research that combines the Internet of Things, convolutional neural network, and group behavior is more. This article will specifically propose a social group behavior analysis model that combines multitask learning and convolutional neural networks. This paper deeply learns the research of convolutional neural network and group behavior‐related theories, makes full use of the advantages of convolutional neural network algorithm and multitask learning mode, and builds a social group behavior analysis model based on multitask learning and convolutional neural network. The experimental results on different data sets are analyzed. The results show that the accuracy rate of the experimental algorithm of convolutional neural network is as high as 95.10%, and it is better than other algorithms in time complexity, which is very suitable for social group behavior analysis.

Monitoring Cyber SentiHate Social Behavior During COVID-19 Pandemic in North America

With communications being shifted to online social networks (OSNs) as a result of travel and social restrictions during COVID-19 pandemic, the need has arisen for discovering emerging trends and concerns formed during the pandemic as well as understanding the corresponding online social behavior that reflects its offline settings. The online connectivity of devices through social media is one example of Internet of Things (IoT) in which a two-way communication between societies and officials, could be created. Therefore, it is possible to monitor people’s behavior through OSNs, especially during pandemics, to prevent potential social and psychological instabilities that might lead to undesired consequences. This is particularly crucial for governmental and non-governmental organizations to ensure the stability and well-being in societies. In response, we propose a pandemic-friendly real-time framework for monitoring cyber social behavior by utilizing unsupervised and supervised learning approaches. Two BERT-based supervised classifiers are trained and constructed to analyze two types of online social behaviors, hate and sentiment. Unsupervised framework is proposed for OSNs data exploration and coherent interpretation that is used as a complementary tool to facilitate the analysis of online social behaviors during pandemics. Extensive experimentation and evaluation have been conducted to validate the effectiveness of the proposed work. Our results have shown superior performance of our BERT-based models in two classification tasks: 1) binary classification for hate behavior detection and 2) multi-class classification for sentiment behavior detection. In addition to our experimentation results, our large-scale analysis of COVID-19 pandemic has illustrated the capability of our unsupervised framework for concerns and trends discoveries using OSNs data, along with reliability in automatically and dynamically providing phrase-based interpenetration of the inferred trends and concerns. This paper provides a twelve-month comparison analysis of data discoveries and online social behavior between Canada and USA during COVID-19 pandemic.

Demographic profile building for cold start in recommender system: A social media fusion approach

New user recommendation is a big challenge for social networks site like reddit, where user profile is not so much rich. The aim of this paper is to enhance recommendation process that reddit can used for its users. We know that twitter and Facebook generate rich user information for social media behavior analysis and research. reddit recommendation system usually implement generic recommendation for its users in order to recommend subreddit, other side of coin, Like LinkedIn, amazon have very specialized user and item recommendation system based on user detail and create effective user profile for future reference. In this work fusion twitter and Facebook dataset user profile building by using tweets and post posted on Facebook by authentic unique user, and after that we extract user profile feature and parallelly perform subreddit listing for specific user feature generated by profile building. As per the reddit rank it is the 5th most visited site of USA and in world it holds 13th rank of most visited site. Twitter has 330 million active users with rich user profile like user demography. In this paper we analyze user timeline and identify user interest using machine learning and extract keywords using TF-IDF. Given the output with this machine learning used as input for a web based reddit subreddit listing application.

Human Motion State Recognition Based on Multi-input ConvLSTM

The human body generates acceleration signals during movement. After collecting and processing this signal, the movement state of the human body can be analyzed and the behavior of the human body can be judged. Human motion state recognition has a wide range of applications in fields such as health monitoring, somatosensory games, and user social behavior analysis. In this paper, the UCI data set collected by mobile phone sensors is used to build the model using convolutional long and short-term memory neural network (ConvLSTM) combined with multi-input CNN (Multi-head CNN), and combined with long and short-term memory neural network (LSTM) and convolutional memory Neural network (ConvLSTM) for comparative evaluation. The accuracy of the model in this paper reached 93.75%. Experimental results show that the new algorithm can more accurately classify and recognize the human motion state.

Multi-View Mouse Social Behaviour Recognition With Deep Graphic Model.

Home-cage social behaviour analysis of mice is an invaluable tool to assess therapeutic efficacy of neurodegenerative diseases. Despite tremendous efforts made within the research community, single-camera video recordings are mainly used for such analysis. Because of the potential to create rich descriptions for mouse social behaviors, the use of multi-view video recordings for rodent observations is increasingly receiving much attention. However, identifying social behaviours from various views is still challenging due to the lack of correspondence across data sources. To address this problem, we here propose a novel multi-view latent-attention and dynamic discriminative model that jointly learns view-specific and view-shared sub-structures, where the former captures unique dynamics of each view whilst the latter encodes the interaction between the views. Furthermore, a novel multi-view latent-attention variational autoencoder model is introduced in learning the acquired features, enabling us to learn discriminative features in each view. Experimental results on the standard CRMI13 and our multi-view Parkinson's Disease Mouse Behaviour (PDMB) datasets demonstrate that our proposed model outperforms the other state of the arts technologies, has lower computational cost than the other graphical models and effectively deals with the imbalanced data problem.

Classification of human activity detection based on an intelligent regression model in video sequences

The most critical objective in security surveillance is abnormal event detection in public scenarios. A scheme is presented for detecting abnormal behaviours in the activities of human groups based on social behaviour analysis. This approach efficiently models group activities than some of the previous strategies that use independent local features. This paper presents a feature descriptor method to signify the movement by implementing the optical flow through covariance matrix coding. The multi-RoI (region of interest) covariance matrix has some frames or patches which could represent the movement in high accuracy. Normal samples are plentiful in public surveillance videos, while there are only a few abnormal samples. For that, the model of a hybridised optical flow covariance matrix is represented in this paper. Optical flow (OF) in the temporal domain is measured as a critical feature of video streams. The logistic regression method is used to detect abnormal activities in a crowded scene. Finally, the behaviours of human crowds can be predicted using benchmark datasets such as UMN, UCSD as well as BEHAVE. The obtained experimental results show that the proposed approach can effectively detect abnormal events from the abandoned environment of surveillance videos.

Addressing the Sustainability Paradox: The Analysis of “Good Food” in Everyday Life

This paper investigates food consumption in terms of socio-spatial practices as complex patterns of meanings, competencies and materialities that shape daily life. The praxeological approach that we advise might improve food sustainability policies by tackling the current sustainability paradox: persisting unsustainable food consumption despite significant media coverage of food sustainability issues and considerable political attention to this matter. Acknowledging the importance of both individual action and collective conditions in shaping food routines, we argue that the sustainability paradox might be overcome through integrating the analysis of social structures and individual behavior, and consequently addressing the determinants of sustainability in daily life. To this end, we analyze narrative interviews on “good food” regarding cultural meanings, individual competencies, and diverse materialities that govern food consumption, identify common themes and discuss their relevance for food policy. We show that food is part of complex orderings of socio-spatial practices, including embodied knowledge, patterns of commensality and constraints of orchestrating daily life, which cannot be addressed appropriately by targeting individual consumption behavior only.

Prediction of English Scores of College Students Based on Multi-source Data Fusion and Social Behavior Analysis

Multi-source data fusion is the premise of applying big data technology in specific fields. Inspired by the theory on multi-source data fusion, this paper fuses various data on college students, including motion trajectories, consumptions, and social behaviors, and adopts support vector machine (SVM), a machine learning (ML) classifier to predict the English scores of college students. The behavior trajectories were taken into account, because this type of data represents the social similarity between students. Specifically, the behavioral features of college students were extracted, and subject to principal component analysis (PCA). Based on these features, the correlation between student score and social relation was analyzed, and used to predict the English scores of college students. Experimental results show that our method can accurately reflect the relationship between the social behaviors and course scores of college students.

Analysis of Cattle Social Transitional Behaviour: Attraction and Repulsion.

Understanding social interactions in livestock groups could improve management practices, but this can be difficult and time-consuming using traditional methods of live observations and video recordings. Sensor technologies and machine learning techniques could provide insight not previously possible. In this study, based on the animals’ location information acquired by a new cooperative wireless localisation system, unsupervised machine learning approaches were performed to identify the social structure of a small group of cattle yearlings () and the social behaviour of an individual. The paper first defined the affinity between an animal pair based on the ranks of their distance. Unsupervised clustering algorithms were then performed, including K-means clustering and agglomerative hierarchical clustering. In particular, K-means clustering was applied based on logical and physical distance. By comparing the clustering result based on logical distance and physical distance, the leader animals and the influence of an individual in a herd of cattle were identified, which provides valuable information for studying the behaviour of animal herds. Improvements in device robustness and replication of this work would confirm the practical application of this technology and analysis methodologies.

Moment-by-moment interpersonal behaviors in poor vs. good psychodynamic psychotherapy outcomes: Does complementarity say it all?

Can patient-therapist moment-by-moment transactions uncover contrary treatment outcomes? The current study answers this question by analyzing the transcripts from eight therapy sessions of 20 patients each, for a total of 160 sessions and nearly 30,000 units of analysis. Patients were matched into ten pairs, each having the same diagnosis and the same clinician but with opposite treatment results: Ten patients were classified as responders (i.e., good-outcome patients) and as many as nonresponders (i.e., poor-outcome patients). Patient and therapist behaviors were coded using the Structural Analysis of Social Behavior (SASB) model. Overall, patients and therapists engaged in complementary relational patterns: Good-outcome patients tended to adopt loving and protecting interaction styles, similarly, therapists treating good-outcome cases employed protection and self-disclosure behaviors. In contrast, poor-outcome patients tried to interpersonally separate from the therapist, and both—patients and therapists alike—exhibited attacking and recoiling behaviors. However, when taking a closer look, i) separation appeared to be disruptive per se, that is, beyond any evidenced interpersonal asymmetry; ii) self-disclosure on the therapist side turned out to be supportive of therapeutic complementarity; iii) when facing failure, highly experienced therapists seemed to indulge into noncomplementary or even hostile behaviors. Findings confirm that the target of the patient- therapist transferential transactions should be distinguished from transactions regarding other people or other life circumstances in order to avoid misleading interpretation of data and, consequently, conducting therapy based on misleading grounds.

Therapeutic Atmosphere in Psychotherapy Sessions.

There is uncertainty concerning what the active ingredients in psychotherapy are. The First Experimental Study of Transference interpretations (FEST) was a randomized controlled trial of the effects of transference work (TW) in psychodynamic psychotherapy. Women with low quality of object relations (QOR) showed a large positive effect of transference work, while men with high QOR showed a slight negative effect. The present study aimed to expand the knowledge from the FEST by investigating the therapeutic atmosphere with Structural Analysis of Social Behavior (SASB). Two-way ANOVAs were conducted to investigate differences between SASB cluster scores between subgroups. The therapeutic atmosphere was characterized by Protect–Trust, Affirm–Disclose and Control–Submit. Multilevel modeling was used to assess the relationship between a therapist variable and outcomes for men and women. Contrary to expectations, no significant differences in therapeutic atmosphere between subgroups (with or without TW in women with low QOR and men with high QOR) were observed using the process measure SASB.

Spaces and Institutional Logics in Post-Conflict Settings of Mitrovica

ABSTRACT Spaces structure interactions between communities in post-conflict settings. They are governed by particular institutional logics, which can foster boundary building and boundary transgression. This article proposes an extended version of the concept of ‘everyday peace’ including a focus not only on micro-level individual actorness in social interactions but also on an important meso-level dimension in the analysis of social behaviour and variation in institutional logics governing spaces where social interactions take place. We apply these concepts to the study of perceptions and practices around bridges in Mitrovica in Kosovo.

Self-directed behaviors differentially explain associations between emotion dysregulation and eating disorder psychopathology in patients with or without objective binge-eating

BackgroundEmotion dysregulation and negative self-directed behaviors are key characteristics of eating disorders (EDs), but their interaction in relation to ED psychopathology is insufficiently explored, and empirically robust and clinically relevant models are needed.MethodsThis study examined whether the association between emotion dysregulation and ED psychopathology was mediated by different negative self-directed behaviors in 999 ED patients divided into two sub-samples based on absence or presence of objective binge-eating episodes (OBE). Several simple and extended mediation models were examined using the Difficulties in Emotion Regulation Scale (DERS) as independent variable, the Structural Analysis of Social Behavior (SASB) as mediator, and the Eating Disorder Examination Questionnaire (EDE-Q) as dependent variable.ResultsAn associational pathway was found where higher emotion dysregulation was associated with more negative self-directed behaviors, which in turn was associated with higher ED psychopathology. Self-directed behaviors of importance differed between patient groups. In participants without OBE, lower self-love and higher self-attack were influential, whereas in participants with OBE, lower self-affirmation and higher self-blame were influential.ConclusionsSelf-directed behaviors may help to explain the association between emotion dysregulation and ED psychopathology. Our findings have both theoretical and clinical implications that are pathology-specific. Addressing specific self-directed behaviors could be an important way of helping patients deal with their emotions in relation to ED psychopathology.

Stimulating Innovation on Social Product Development: An Analysis of Social Behaviors in Online Innovation Communities

With firms’ increasing adoption of social product development strategies, such as mass collaboration and crowdsourcing, online users are actively participating in the development of new products and services via social media platforms. Online innovation communities (OICs), one representative of such social media platforms, have been used by large firms to collect ideas from their users and facilitate the product development process. While it is extensively studied that product ideas with high popularity on OICs are of great importance to the product development, research on what social behaviors of online users lead to the high popularity is largely unclear. This article conducts an empirical study by collecting a large-scale, quantitative dataset from an OIC between 2014 and 2018. With the analysis of users’ online idea posting and commenting behaviors, our results reveal that the idea contribution experience, together with comment diversity, positively influence the overall popularity of an individual's ideas, while the motivation of providing comments is negatively related. Moreover, user's innovation capability poses a positive effect on both overall and average popularity of ideas. These findings can help firms better incentivise their users on OICs to improve the effectiveness and efficiency of social product development.