Recognizing CEO personality and its impact on business performance: Mining linguistic cues from social media

Upper echelons theory suggests that CEO personality will influence organizational performance. However, difficulty in measuring CEO personality restrains related research. We capture linguistic cues CEOs leaving on social media and recognize their personality by text mining. To our knowledge, it is the first study introducing social media text mining approaches into the research stream that empirically inquires and extends upper echelons theory. Then, we investigate the CEO personality’s impact on both operational and financial performance. Results show that CEO Extraversion, Emotional Stability, and Agreeableness improve Cost Efficiency and Profitability, while CEO Conscientiousness reduces them. CEO Openness to Experience negatively influences Profitability, and all facets of CEO personality improve Employee Productivity except for CEO Conscientiousness. The contribution of our research is multi-sided: (1). methodologically, we introduce a text mining approach to measure CEO personality; (2). theoretically, we provide empirical evidence for upper echelons theory; (3). practically, our results help companies evaluate CEO candidates from a personality perspective.     

基于情绪特征的中文微博用户性别识别

随着互联网的蓬勃发展,微博受到了越来越多用户的青睐,对微博用户性别的研究也逐渐成为学术界研究的热点。目前,对英文微博文本用户的性别识别已有研究,但针对中文微博用户性别识别的研究较少。从两性表达情绪的差异出发,提出了一种基于情绪特征的中文微博用户性别识别方法。本文考虑的情绪特征包括情绪词特征和与情绪相关的语言风格特征。实验结果表明,利用情绪特征提高了用户性别识别的精度。     

Home location profiling for users in social media

In this paper, we focus on the problem of estimating the home locations of users in the Twitter network. We propose a Social Tie Factor Graph (STFG) model to estimate a Twitter user's city-level location based on the user's following network, user-centric data, and tie strength. In STFG, relationships between users and locations are modeled as nodes, while attributes and correlations are modeled as factors. An efficient algorithm is proposed to learn model parameters and predict unknown relationships. We evaluate our proposed method by investigating Twitter networks. The experimental results demonstrate that our proposed method significantly outperforms several state-of-the-art methods.     

Modeling user interest in social media using news media and wikipedia

Social media has become an important source of information and a medium for following and spreading trends, news, and ideas all over the world. Although determining the subjects of individual posts is important to extract users' interests from social media, this task is nontrivial because posts are highly contextualized and informal and have limited length. To address this problem, we propose a user modeling framework that maps the content of texts in social media to relevant categories in news media. In our framework, the semantic gaps between social media and news media are reduced by using Wikipedia as an external knowledge base. We map term-based features from a short text and a news category into Wikipedia-based features such as Wikipedia categories and article entities. A user's microposts are thus represented in a rich feature space of words. Experimental results show that our proposed method using Wikipedia-based features outperforms other existing methods of identifying users' interests from social media.     

Recognizing composite daily activities from crowd-labelled social media data

Human activity recognition is a core component of context-aware, ubiquitous computing systems. Traditionally, this task is accomplished by analysing signals of wearable motion sensors. While successful for low-level activities (e.g. walking or standing), high-level activities (e.g. watching movies or attending lectures) are difficult to distinguish from motion data alone. Furthermore, instrumentation of complex body sensor network at population scale is impractical. In this work, we take an alternative approach of leveraging rich, dynamic, and crowd-generated self-report data from social media platforms as the basis for in-situ activity recognition. By treating the user as the “sensor”, we make use of implicit signals emitted from natural use of mobile smartphones, in the form of textual content, semantic location, and time. Tackling both the task of recognizing a main activity (multi-class classification) and recognizing all applicable activity categories (multi-label tagging) from one instance, we are able to obtain mean accuracies of more than 75%. We conduct a thorough analysis and interpret of our model to illustrate a promising first step towards comprehensive, high-level activity recognition using instrumentation-free, crowdsourced, social media data.     

Improving geolocation of social media posts

Pervasive social systems often take advantage of geographical information to provide real-time information to users based on their location. However, due to privacy concerns, many social media users do not share their exact geographical coordinates. In this paper, we describe our technique that predicts locations of posts that are not associated with explicit coordinates, a process called geolocation. Existing research has utilized the content of a post as well as the post author’s social media relationships with other users to estimate location. Our research provides a novel approach to geolocation by combining multiple techniques, as well as adding a new technique: estimating location by clustering similar social media posts that are centered in a geographical area.     

Folksonomy-based personalized search and ranking in social media services

In recent years, social Web users have been overwhelmed by the huge numbers of social media available. Consequentially, users have trouble finding social media suited to their needs. To help such users retrieve useful social media content, we propose a new model of tag-based personalized searches to enhance not only retrieval accuracy but also retrieval coverage. By leveraging social tagging as a preference indicator, we build two models: (i) a latent tag preference model that reflects how a certain user has assigned tags similar to a given tag and (ii) a latent tag annotation model that captures how users have tagged a certain tag to resources similar to a given resource. We then seamlessly map the tags onto items, depending on an individual user's query, to find the most desirable content relevant to the user's needs. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the art algorithms and show our method's feasibility for personalized searches in social media services.     

An assessment of equivalence between paper and social media surveys: The role of social desirability and satisficing

Investigation of the underlying mechanisms responsible for measurement variance has received little attention. The primary objective of this study is to examine whether paper and social media surveys produce convergent results and investigate the underlying psychological mechanisms for the potential measurement nonequivalence. Particularly, we explored the role of social desirability and satisficing on the measurement results. We collected data via five different survey modes, including paper survey, ad hoc Web survey, online forum (message boards)-based, SNS-based and microblog-based surveys. The findings show that socially desirable responding does not lead to inconsistent results. Rather we found that satisficing causes inconsistent results in paper versus online surveys. Sociability reduces the possibility of engaging in satisficing that results in inconsistent results between traditional Web surveys and social media-based Web surveys.     

A novel hybrid social media platform selection model using fuzzy ANP and COPRAS-G

The growing popularity of social media platforms has sparked new marketing opportunities for companies. Marketers have turned to social media campaigns as a means to build brand loyalty, exposure, and engagement. While social media has evolved into a powerful marketing tool, marketers must carefully choose the most suitable social media platform. Improper selection of the social media platform can be costly and can be detrimental to the brand. Despite all of the supposed benefits, selecting the right social media platform has been a daunting task for corporate marketers. The social media platform selection problems are inherently complex problems with multiple and often conflicting criteria. We propose a novel analytical framework for social media platform selection. The proposed hybrid framework integrates the Analytic Network Process (ANP) with fuzzy set theory and the COmplex PRoportional ASsessment of alternatives with Grey relations (COPRAS-G) method. The ANP and fuzzy set theory are used to determine the importance weight of the social media platform selection criteria in a fuzzy environment. The COPRAS-G method is used to rank and select the most suitable social media platform. A case study is presented to demonstrate the applicability of the proposed framework and exhibit the efficacy of the procedures and algorithms.     

A motives framework of social media website use: A survey of young Americans

Social media is increasingly important in daily life and is an especially important social interaction mechanism for young people. Although research has been conducted evaluating user types based on motives for using social media, no such framework has been extended to social media websites. We extend previous research by evaluating the underlying structure of social media website usage motivations using a 13 item survey and evaluations from 19 different social media websites administered to 1686 young Americans. Using a multidimensional scaling approach, we uncover 2 major motive dimensions underlying social media website use: fun-related and content-specific. Based on the derived dimensions, we generate a graphical “quadrant” system for classifying social media websites and depict all 19 social media sites based on their quadrant. We propose that our quadrant system can be used by other researchers to further refine understanding of social media website usage motives.     

Healthcare support for underserved communities using a mobile social media platform

Emerging digital technologies for healthcare information support have already contributed to reducing the digital divide among rural communities. Although mobile health (m-health) applications facilitate provision of support for treatment consultation in real-time, their substantial potential has not yet been operationalised for decision support to meet citizen demand in developing nations. Modern healthcare information access, especially in rural areas of developing countries, is critical to effective healthcare, since both information and expert opinions are limited. Mobile phone and social media penetration, however, is often extensive. In this paper, we design and evaluate an innovative mobile decision support system (MDSS) solution for rural citizens healthcare decision support and information dissemination.Developed using a design science approach, the instantiated artifact connects underserved rural patients in Bangladesh to general practitioners (GPs) – allowing GPs, based on queries and information support provided, to evaluate patient conditions virtually and provide answers for further diagnosis or treatment. A cloud platform using social media embodies health record information and is used with a rating technique that matches queries to profiled remote experts, participating asynchronously. A comprehensive evaluation of the MDSS artifact ensures its utility, efficacy, and reliability.     

Exploring rape culture in social media forums

Current research has yet to examine the phenomenon of rape culture, particularly within social media forums. The present study investigated the attitudes about rape, rapists, and gender-based violence within the comments section of newspaper articles reporting about rape and sexual assault. Naturalistic observation was used in order to gather statements within the comment sections following newspaper articles posted on either the periodical website or the periodical’s Facebook page. Four themes and various sub-themes emerged from the data. The major themes include, Victim Blaming and Questioning, Survivor Support, Perpetrator Support, and Trolling Statements about Law and Society. Notable findings were found in the amount of victim blaming statements made in the comments responding to articles (25.8 percent) and perpetrator support comments were found responding to every article collected, except for one. The authors discuss the implications of rape culture within and outside social media and suggest future research to be conducted to further understand the impacts of rape culture within the online sphere.     

Information sharing on social media sites

This study reports on an exploratory survey conducted to investigate the use of social media technologies for sharing information. This paper explores the issue of credibility of the information shared in the context of computer-mediated communication. Four categories of information were explored: sensitive, sensational, political and casual information, across five popular social media technologies: social networking sites, micro-blogging sites, wikis, online forums, and online blogs. One hundred and fourteen active users of social media technologies participated in the study. The exploratory analysis conducted in this study revealed that information producers use different cues to indicate credibility of the information they share on different social media sites. Organizations can leverage findings from this study to improve targeted engagement with their customers. The operationalization of how information credibility is codified by information producers contributes to knowledge in social media research.     

Face recognition using spectral features

Face recognition is a challenging task in computer vision and pattern recognition. It is well-known that obtaining a low-dimensional feature representation with enhanced discriminatory power is of paramount importance to face recognition. Moreover, recent research has shown that the face images reside on a possibly nonlinear manifold. Thus, how to effectively exploit the hidden structure is a key problem that significantly affects the recognition results. In this paper, we propose a new unsupervised nonlinear feature extraction method called spectral feature analysis (SFA). The main advantages of SFA over traditional feature extraction methods are: (1) SFA does not suffer from the small-sample-size problem; (2) SFA can extract discriminatory information from the data, and we show that linear discriminant analysis can be subsumed under the SFA framework; (3) SFA can effectively discover the nonlinear structure hidden in the data. These appealing properties make SFA very suitable for face recognition tasks. Experimental results on three benchmark face databases illustrate the superiority of SFA over traditional methods.     

Adaptive landmark recommendations for travel planning: Personalizing and clustering landmarks using geo-tagged social media

When travelers plan trips, landmark recommendation systems that consider the trip properties will conveniently aid travelers in determining the locations they will visit. Because interesting locations may vary based on the traveler and the situation, it is important to personalize the landmark recommendations by considering the traveler and the trip. In this paper, we propose an approach that adaptively recommends clusters of landmarks using geo-tagged social media. We first examine the impact of a trip’s spatial and temporal properties on the distribution of popular places through large-scale data analyses. In our approach, we compute the significance of landmarks for travelers based on their trip’s spatial and temporal properties. Next, we generate clusters of landmark recommendations, which have similar themes or are contiguous, using travel trajectory histories. Landmark recommendation performances based on our approach are evaluated against several baseline approaches. Our approach results in increased accuracy and satisfaction compared with the baseline approaches. Through a user study, we also verify that our approach is applicable to lesser-known places and reflects local events as well as seasonal changes.     

Examining social adjustment to college in the age of social media: Factors influencing successful transitions and persistence

Social adjustment plays a critical role in student persistence at college. Social media such as Facebook, used widely by this population, have the potential to positively enhance students' transition to college by encouraging connection and interaction among peers. The present study examines the role Facebook plays in students' social adjustment during their first year of college using survey data (N = 338) collected from students at a private, liberal arts college in the Midwest. We develop and test a model that includes both traditional and Facebook-specific predictors of social support and social adjustment, as well as explore the role that these factors play in predicting students' enrollment status the following year. Results indicate positive relationships between two Facebook variables—the number of Facebook Friends students have at the college and their engagement in collaborative behaviors with classmates through the site—and measures of social support and social adjustment, as well as a positive relationship between social adjustment and persistence at the university.     

Testing alternative models of individuals’ social media involvement and satisfaction

This study extends the Technology Acceptance Model 3 (TAM 3) within the context of the rapidly evolving area of social media. Since social media requires the user’s active participation and processing of information as well as the creation of user-generated content, this timely study introduces the two relevant constructs of social media involvement and social media satisfaction that are associated with TAM 3. This study then develops three alternative conceptual models and empirically validates each of them using datasets obtained from the United States and Saudi Arabia. The LISREL analysis results show that the Dual Mediation Impact model is the best-fit model for both datasets. Further, the results show that a user’s social media involvement and social media satisfaction are dual mediators of the TAM 3 factors on social media usage intention. While the two country data show some differences in the strength of the relationships in the Dual Mediation Impact model, the identified model provides a common framework for global use. Implications and future research directions are discussed.     

Collaborative storytelling experiences in social media: Influence of peer-assistance mechanisms

Collaborative storytelling activities in social media environments are generally developed in a linear way in which all participants collaborate on a shared story as it is passed from one to another in a relay form. Difficulties with this linear approach arise when collecting the contributions of participants in to a coherent story. This study proposes a hypermedia approach to enable students to integrate the episodes of others to develop different branches of stories. Since these linear and nonlinear approaches facilitate students in developing stories in quite a different manner, students’ perceptions of linear and hypermedia approaches differ in their collaboration mechanisms, which may in turn affect positive inter-dependence and ultimate success in the collaborative storytelling. The results of an empirical study show that the performance of students in the hypermedia group was superior to that of members the linear group insofar as perception of collaborative process, peer support, authorship, and collaborative result where concerned.     

Exploring the relationships between self-presentation and self-esteem of mothers in social media in Russia

Majority of parents use social media platforms, with young mothers being the most active users. Academic research has only recently started addressing the impact of social media on mothers, although they are one of the most engaged online audiences. Instagram and Facebook perceived as positive types of social media, where users post positive content to increase encouraging response from their subscribers and thus enhance their self-esteem. This also relates to mothers portraying positive self-presentation online, therefore enhancing their parental self-esteem. This study provides in-depth analysis of 23 popular online profiles of mothers with more than thirty thousand followers on Instagram and 12 interviews with socially active mothers. This work focuses on mothers in Russia. Research findings show mothers with children of pre-school age are the most regular users of social media. This is due to time availability, as majority of these mothers are on maternity leave and due to little knowledge in child related aspects, which leads to lower self-esteem. They often look for assurance in online community. Mothers that are more confident have positive attitude towards social media communication. Mothers with initially lower self-esteem feel under pressure to maintain positive image to be in line with other mothers' presentation on social media. Mothers find Facebook more informative and supportive vehicle of communication than Instagram.