Understanding trust in ms-commerce: The roles of reported experience,linguistic style,profile photo,emotional, and cognitive trust

This paper examines what influences trust in mobile social commerce environment. Drawing on trust-based acceptance model (i.e. cognitive and emotional trust) and online review features (i.e. profile photo, linguistic style, and reported experience), we examine how these factors affect trust in mobile social commerce. Hypotheses were tested using survey data. The results of our model showed that there are significant influences of profile photo, reported experience, cognitive, and emotional trust towards trust in ms-commerce. This work contributes to existing literature by examining the roles of previous trust in mobile payments and online reviews on trust in mobile social commerce.     

A novel approach for efficient stance detection in online social networks with metaheuristic optimization

In the 19th and 20th centuries, social networks have been an important topic in a wide range of fields from sociology to education. However, with the advances in computer technology in the 21st century, significant changes have been observed in social networks, and conventional networks have evolved into online social networks. The size of these networks, along with the large amount of data they generate, has introduced new social networking problems and solutions. Social network analysis methods are used to understand social network data. Today, several methods are implemented to solve various social network analysis problems, albeit with limited success in certain problems. Thus, the researchers develop new methods or recommend solutions to improve the performance of the existing methods. In the present paper, a novel optimization method that aimed to classify social network analysis problems was proposed. The problem of stance detection, an online social network analysis problem, was first tackled as an optimization problem. Furthermore, a new hybrid metaheuristic optimization algorithm was proposed for the first time in the current study, and the algorithm was compared with various methods. The analysis of the findings obtained with accuracy, precision, recall, and F-measure classification metrics demonstrated that our method performed better than other methods.     

Muzzling social media: The adverse effects of moderating stakeholder conversations online

Many organizations struggle to meaningfully engage with their stakeholders on political, societal and environmental topics via social media. Often such discourses unravel into splintered and negative conversations, raising the question whether organizations can and should exercise some level of control and ‘steering’ in these conversations and, if so, how stakeholders would react to such ‘top down’ moderation. Existing studies lack empirical insights into the impacts of different levels of moderation in social media conversations on stakeholder attitudes. Two experimental studies were developed to test the effect of different levels of organizational moderation on stakeholder attitudes towards organizations. We show that increased levels of moderation negatively affect attitudes towards an organization, satisfaction with an organization's performance, and trust in the organization. Increased moderation also significantly undermines beliefs in the commitment of the organization to its stakeholders and control mutuality. This paper extends recent qualitative attempts to build new theory around stakeholder dialogues on social media by testing the effects of varying levels of moderation in such dialogues.     

ED-SWE: Event detection based on scoring and word embedding in online social networks for the internet of people

Online social media networks are gaining attention worldwide, with an increasing number of people relying on them to connect, communicate and share their daily pertinent event-related information. Event detection is now increasingly leveraging online social networks for highlighting events happening around the world via the Internet of People. In this paper, a novel Event Detection model based on Scoring and Word Embedding (ED-SWE) is proposed for discovering key events from a large volume of data streams of tweets and for generating an event summary using keywords and top-k tweets. The proposed ED-SWE model can distill high-quality tweets, reduce the negative impact of the advent of spam, and identify latent events in the data streams automatically. Moreover, a word embedding algorithm is used to learn a real-valued vector representation for a predefined fixed-sized vocabulary from a corpus of Twitter data. In order to further improve the performance of the Expectation-Maximization (EM) iteration algorithm, a novel initialization method based on the authority values of the tweets is also proposed in this paper to detect live events efficiently and precisely. Finally, a novel automatic identification method based on the cosine measure is used to automatically evaluate whether a given topic can form a live event. Experiments conducted on a real-world dataset demonstrate that the ED-SWE model exhibits better efficiency and accuracy than several state-of-art event detection models.     

The mediating role of problematic social media use in the relationship between social avoidance/distress and self-esteem

The number of social media users in Bangladesh is increasing in every year. Young people – mainly college and university students – are the heavy users of it. Problematic social media use (PSMU) has received increasing research attention over the past decade. However, there are very few studies concerning PSMU in Bangladesh. The present study examined the role of PSMU in the relationship between social avoidance/distress and self-esteem. The study comprised a sample of 370 university students from the University of Chittagong (Bangladesh). Participants were administered a survey including questions regarding socio-demographics, the Bergen Social Media Addiction Scale (BSMAS), the Rosenberg Self-Esteem Scale (RSES), and the Social Avoidance and Distress Scale (SADS). Results showed that PSMU partially mediated the relationship between social avoidance/distress and self-esteem, while social avoidance/distress predicted the level of self-esteem. However, PSMU had a non-significant influence on the relationship between these two variables while self-esteem predicted social avoidance/distress.     

Milk- and starter-feeding strategies to reduce cross sucking in pair-housed calves in outdoor hutches

Social housing of preweaned calves can benefit their welfare, but housing and cross sucking are potential barriers to adoption for farmers. For farms using outdoor hutches, an option is to pair adjacent hutches with a shared fence. Our objective was to investigate milk- or starter-feeding strategies to mitigate cross sucking in this system. Holstein heifers were housed in pairs (n = 32 pairs) and divided into 4 treatments (n = 8 pairs each) in a 2 × 2 factorial design: milk in an open bucket with starter in only a bucket (OB-SB), milk in an open bucket and starter in both a bucket and a specialized teat bottle (Braden bottle; OB-BB), milk in a slow-flow teat bucket with starter in only a bucket (TB-SB), or milk in a slow-flow teat bucket and starter in both a bucket and a specialized teat bottle (TB-BB). When starter was first offered (d 6 ± 1 of life, mean ± SD), calf latency to approach was recorded, averaged within pairs, and compared between starter treatments using a linear mixed model with fixed effect of treatment and random effect of pair within treatment. Calves were initially bottle fed; milk treatments began on d 14 ± 1 of life and ended when calves were completely weaned (d 53 ± 1). Calves were observed for behaviors such as drinking milk and cross sucking twice weekly for 30 min during the afternoon milk meal using continuous video, with values averaged within pairs. Linear mixed models were run separately before (wk 3–6) and after (wk 7–8) weaning, with fixed effects of milk- and starter-feeding treatments, week, and the 2- and 3-way interactions, with week as repeated measure and pair as subject. Pairs with Braden bottles and buckets approached starter sooner than those with only buckets (Braden bottles vs. no Braden bottles: 13.1 ± 6.1 vs. 33.2 ± 6.1 min, LSM ± SEM). Before weaning, pairs with open buckets for both milk and starter cross sucked for at least twice as long (OB-SB: 2.9 ± 0.3 min) as all other treatments (OB-BB: 1.5 ± 0.3 min; TB-SB: 0.4 ± 0.3 min; TB-BB: 0.5 ± 0.3 min). This pattern held during weaning, when cross sucking increased overall (OB-SB: 3.9 ± 0.4 min; OB-BB: 1.8 ± 0.4 min; TB-SB: 0.9 ± 0.4 min; TB-BB: 1.6 ± 0.4 min). Regardless of starter treatment, calves spent less time cross sucking when fed milk in teat buckets, which extended the milk meal relative to open buckets (teat bucket vs. open bucket: preweaning = 7.0 ± 0.2 vs. 1.6 ± 0.2 min; weaning = 3.0 ± 0.1 vs. 0.6 ± 0.1 min). When calves are fed milk in open buckets, a novel option for reducing cross sucking is to provide starter through a specialized bottle. Nonetheless, providing milk in slow-flow teat buckets had the greatest effect on reducing cross sucking by directing suckling to the teat instead of another calf or pen objects.     

基于节点1-邻居图相似性的社会网络匿名技术

利用传统的k匿名技术在社会网络中进行隐私保护时会存在聚类准则单一、图中数据信息利用不足等问题. 针对该问题, 提出了一种利用Kullback-Leibler (KL)散度衡量节点1-邻居图相似性的匿名技术(anonymization techniques for measuring the similarity of node 1-neighbor graph based on Kullback-Leibler divergence, SNKL). 根据节点1-邻居图分布的相似性对原始图节点集进行划分, 按照划分好的类进行图修改, 使修改后的图满足k匿名, 完成图的匿名发布. 实验结果表明, SNKL方法与HIGA方法相比在聚类系数上的改变量平均降低了17.3%, 同时生成的匿名图与原始图重要性节点重合度保持在95%以上. 所提方法在有效保证隐私的基础上, 可以显著的降低对原始图结构信息的改变.     

A calm companion lowers fear in groups of dairy cows

Dairy cows are generally calm and compliant, but some management procedures can make cows fearful or stressed. Not only are fearful cattle a threat to human safety, but fear is also detrimental to animal welfare and productivity. This study aimed to test whether fear in small groups of dairy cattle could be attenuated by the presence of a calm and experienced companion. Twenty-seven dairy cows from a Swedish agricultural school participated in the study. The study included a standardized fear-eliciting stimulus, which was 3 sudden, repetitive openings of a red and white umbrella. Demonstrator cows (n = 9) were selected based on age to ensure that all demonstrators were older than the naïve test cows (n = 18). Of these 9 demonstrator cows, 6 were selected as untrained (i.e., habituated to the presence of the test person) and 3 were selected as trained demonstrators (i.e., additionally habituated to the fear-eliciting stimulus). The remaining 18 test cows comprised 6 test-cow groups of 3 cows each, which were their own controls, resulting in a crossover design; 3 groups were tested with a trained demonstrator first and then with an untrained demonstrator, and vice versa for the other 3 groups, resulting in a total of 12 trials (4 sub-treatments). Response variables were heart rate increase from baseline, behavioral reaction indicative of fear, and latency to resume feeding after exposure to the fear-eliciting stimulus. The study found a calming effect of a trained demonstrator on test cows' heart rate but not on latency to resume feeding or behavioral reaction. Post hoc analyses revealed a carryover effect on latency, indicating that test cows who were accompanied by an untrained demonstrator first had longer latencies than cows in all other sub-treatments. Adding a calm, experienced cow to groups of dairy cattle may mitigate fear and thereby improve welfare and safety.     

基于农产品品牌社会责任的消费者智能消费推荐研究

为了在竞争日益激烈的市场中提高竞争力,达到个性化销售目的,创造更大品牌价值,提出一种基于农产品品牌社会责任的消费者智能消费推荐研究。实验利用离散函数及品牌认可度描述推荐结果,分析推荐结果指数,证明基于社会责任的智能推荐研究令消费者认可度更高,与文献方法比较推荐结果有更高的精确度,设计的推荐模型可实现农产品个性销售目的,令农产品有更大的发展空间。     

煤炭企业社会责任问题及对策研究

介绍了企业应承担的社会责任,重点分析了我国煤炭企业在社会责任方面的现状及存在的问题,并提出若干建议。