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Visualization of 24 primary moods extracted from the data on the core affect circle of emotion structure using valence and arousal proposed in (Russell 2009).
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Social media provides rich sources of personal information and community interaction which can be linked to aspect of mental health. In this paper we investigate manifest properties of textual messages, including latent topics, psycholinguistic features, and authors' mood, of a large corpus of blog posts, to analyze the aspect of social capital in...
Citations
... First is the study related to psychological conditions such as emotion, degree of depression, and mood, through language on social media [1][11] [12][15] [17]. The researchers also investigated the information related to linguistic aspects to identify emotion on Twitter [1][2] [15] [18]. ...
... Measurement of Social Capital [82] social capital social capital as the degree of social participation (connectivity) + social support (content generation engagement). Connectivity = number of friends, number of community memberships, number of followers; social support = number of posts written, number of comments made, number of comments received [83] social capital own measurement developed for civic engagement (1 item), interpersonal trust (2 items), political knowledge (6 items) [84] social capital life satisfaction = adapted from the Satisfaction with Life Scale (developed by [85]); social trust = adapted and modified version of [86]; civic and political participation = a reduced form of the Index of Civic and Political Engagement developed by CIRCLE [87] [88] social capital political participation = 6 items adapted from the National Election Studies; civic participation = 5 items developed; confidence in government = 3 items developed ...
The introduction of the Web 2.0 era and the associated emergence of social media platforms opened an interdisciplinary research domain, wherein a growing number of studies are focusing on the interrelationship of social media usage and perceived individual social capital. The primary aim of the present study is to introduce the existing measurement techniques of social capital in this domain, explore trends, and offer promising directions and implications for future research. Applying the method of a scoping review, a set of 80 systematically identified scientific publications were analyzed, categorized, grouped and discussed. Focus was placed on the employed viewpoints and measurement techniques necessary to tap into the possible consistencies and/or heterogeneity in this domain in terms of operationalization. The results reveal that multiple views and measurement techniques are present in this research area, which might raise a challenge in future synthesis approaches, especially in the case of future meta-analytical contributions.
... Many researchers have acknowledged that online communication, including activities on SNSs such as Facebook and Twitter (Boyd & Ellison, 2007), is gradually replacing traditional social interactions, such as face-to-face communication (Arampatzi et al., 2018). SNSs have become popular media for online communication (Horng et al., 2016;Nguyen et al., 2013). ...
The main objective of this study was checking the role of social capital in promoting entrepreneurial readiness of engineering students in Shiraz University. The method of this study was descriptive and correlational. The statistical society of this study included all engineering students of Shiraz University. The statistical sample in this study was 103 student that were selected among available sampling method. The data collection instrument was entrepreneurial readiness questionnaire of Omenyi & et al (2009) and social capital questionnaire of Gu & et al (2013) that their reliability was calculated respectively, 0.77 and 0.74. After the calculated of validity and reliability, the instruments was distributed among the participants and then the data was analyzed thorough single-sample t-test, Pearson correlation and structural equation modeling. The results of single-sample t-test showed that the social capital and entrepreneurial readiness in engineering students, were higher than acceptable levels (Q2). Pearson correlation analysis showed that there is a significant positive relationship between social capital and all of its components with entrepreneurial readiness, and job readiness. The test results of structural equation modeling using LISREL 8.8 software, showed that social capital, is a significant positive predictor of readiness entrepreneurship among the students of engineering.
... Many researchers have acknowledged that online communication, including activities on SNSs such as Facebook and Twitter (Boyd & Ellison, 2007), is gradually replacing traditional social interactions, such as face-to-face communication (Arampatzi et al., 2018). SNSs have become popular media for online communication (Horng et al., 2016;Nguyen et al., 2013). ...
Studies have revealed that individuals’ identities can be shaped not only offline but also online. However, the formation of online identity of students in higher education environments is complex. Empirical evidence has suggested that academics are interested in using social media to enhance their professional identity and reputation. The current study examined the effects of online social capital and social networking on the formation of graduate students’ professional identity. Structural equation modeling was used to test the research hypotheses. The sample consisted of 298 graduate students from one leading public university and three private higher education centers in Iran. The results showed that online social networks in higher education environments could affect the construction of students’ professional identity through online social capital as a mediator.
... To understand aspects of online mental health-related communities including online depression and autism communities, several research has been done for identifying characteristics of these communities [29,44,46,47,[49][50][51]. With questionnaire-based methods, existing studies (e.g. ...
... In a related work, by investigating both language styles and topics expressed in the content of blog posts in online autism communities, the study [46] indicated that substantial differences between autism and general online communities are significantly characterized by both latent topics of discussion and psycholinguistic features. In addition, existing studies [41,44,60] analysed several informative features of blog posts including topical content, linguistic styles, and sentiment conveyance (mood) for examining online social capital and mood in two extremes (high and low) to identify distinct communities among dif-ferent social capital groups of both depression and general communities. The study suggests that mining blogs have the potential to detect clinical information from online communities. ...
... probabilistic latent semantic indexing (pLSI) [25], latent Dirichlet allocation (LDA) [2], or hierarchical Dirichlet processes (HDP) [65]) have shown to be effective in discovering latent topics from the corpus of blog posts. Several studies [35,41,43,44,58,66] used the standard parametric model LDA to learn latent topics from the content of blogs and tweets in the blogosphere for their research on mental health signals in social media. Using LDA to gain latent topics, [41] found significant differences among study cohorts which are characterized by the latent topics of discussion, psycholinguistic features, and tagged moods. ...
Social media are an online means of interaction among individuals. People are increasingly using social media, especially online communities, to discuss health concerns and seek support. Understanding topics, sentiment, and structures of these communities informs important aspects of health-related conditions. There has been growing research interest in analyzing online mental health communities; however analysis of these communities with health concerns has been limited. This paper investigate and identify latent meta-groups of online communities with and without mental health-related conditions including depression and autism. Large datasets from online communities were crawled. We analyse both sentiment-based, psycholinguistic-based and topic-based features from blog posts made by members of these online communities. The work focuses on using nonparametric methods to infer latent topics automatically from the corpus of affective words in the blog posts. The visualization of the discovered meta-communities in their use of latent topics shows a difference between the groups. This presents evidence of the emotion-bearing difference in online mental health-related communities, suggesting a possible angle for support and intervention. The methodology might offer potential machine learning techniques for research and practice in psychiatry.
... This aspect is periodically revisited by (Kovanovic et al. 2014;Licamele, Getoor 2006;Misra et al. 2013). The most popular aspect is civic engagement (Adler, Kwon 2002b;Horst, Toke 2010;Kovanovic et al. 2014;Nguyen et al. 2013;Putnam 2000;Rutten et al. 2010;Sampson, Graif 2009;Svendsen 2010;Turner-Lee 2010;Wilson 2006). It is also worth mentioning trustworthiness (Adler, Kwon 2002;Bourdieu 2011;Kawachi et al. 1997;Rutten et al. 2010). ...
... Authors opinions differ according to the conception of what is measured with the properties of social capital. Some state that social capital is the propriety of a person (Bourdieu 2011;Kovanovic et al. 2014;Licamele, Getoor 2006;Nguyen et al. 2013) for others, the propriety of a network or location (Sampson, Graif 2009;Smith, Giraud-Carrier 2010;Wilson 2006). Bourdieu (2011) points out that social capital is only a subtype of unified capital. ...
The social aspect is an important but often overlooked part of sustainable development philosophy. In hoping to popularise and show the importance of social sustainable development, this study tries to find a relation between the social environment and urban form. Research in the social capital field provided the methodology to acquire social computational data. The relation between human actions and the environment is noted in many theories, and used in some practices. Human cognition is computationally predictable with natural shape analysis and machine learning methods. In the analysis of shape, a topological skeleton is a proven method to acquire statistical data that correlates with data collected from human experiments. In this study, the analysis of urban form with respect to human cognition was used to acquire computational data for a machine learning model of social capital in counties in the USA
Santrauka
Tvarios plėtros teorijoje socialinė aplinka yra pripažinta kaip svarbus veiksnys, tačiau trūksta praktinės metodikos. Ryšio tarp urbanistinės formos ir socialinės aplinkos radimas aktualizuotų ir padėtų populiarinti socialinę tvarią plėtrą. Aplinkos įtaka žmonių tarpusavio elgesiui yra ne kartą aptartas reiškinys, tačiau praktikoje retai taikomas. Ankstesniuose socialinio kapitalo tyrimuose pateikiamos metodologijos ir statistiniai duomenys esamos situacijos analizei atlikti. Kaip žmonės suvokia formas, yra nuspėjama taikant statistinę formos analizę ir dirbtinio intelekto metodologiją – sistemos mokymąsi. Klasifikuojant formas topologinio skeleto metodologija gaunami rezultatai koreliuoja su duomenimis, surinktais per eksperimentą, kuriame žmonės klasifikuoja formas. Taikant žinomas formos analizės metodologijas, atspindinčias suvokimą, buvo surinkti duomenys modeliuoti socialinį kapitalą su sisteminio mokymosi modeliu. Sisteminis mokymasis yra dirbtinio intelekto sritis, kurioje remiantis pateiktais duomenimis automatiškai sukalibruojama kompleksinė matematinė formulė. Modeliuojant socialinį kapitalą su formos skeleto statistiniais duomenimis, geriausi rezultatai pasiekti taikant neuroniniais tinklais pagristą sisteminį mokymąsi.
Reikšminiai žodžiai: urbanistinė forma, formos analizė statistiniais metodais, socialinis kapitalas, sisteminis mokymasis, daugiasluoksnis perceptronas.
... Social capital can be defined by the capacity of facilitating collective action. Meanwhile, online social capital has attracted much attention recently in impacts of such social capital in online communities [5], [6]. Insights from such analysis have wide applications in healthcare and medical research on both individuals and online communities. ...
... [12] investigated that the development and maintenance of social connectedness probably improve both mental health and well-being among Facebook users. With analyzing a large corpus of blog posts, social capital and sentiment aspects have been examined to understand user's mood transitional patterns in different cohorts of social capital [6], [13], [14], [15], [16]. To understand the characteristics of online depression communities of micro-blogging platforms, both linguistic styles and diurnal patterns of tweeting were used as predictors of affective disorders in Twitter users [3]. ...
Social capital is linked to mental illness. It has been proposed that higher social capital is associated with better mental well-being in both individuals and groups in offline setting. However, in online settings, the association between online social capital and mental health conditions has not yet been explored. Social media offer us a rich opportunity to determine the link between social capital and aspects of mental well-being. In this paper, we examine social capital defined based on levels of social connectivity of bloggers can be connected to aspects of depression in individuals and online depression community. We explore apparent properties of textual contents, including expressed emotions, language styles and latent topics, of a large corpus of blog posts, to analyze the aspect of social capital in the community. Using data collected from an online LiveJournal depression community, we apply both statistical tests and machine learning approaches to examine how predictive factors vary between low and high social capital groups. Significant differences are found between low and high social capital groups when characterized by a set of latent topics, language features derived from blog posts, suggesting discriminative features, proved to be useful in the classification task. It shows that linguistic styles are better predictors than latent topics as features. The findings indicate the potential of using social media as a sensor for monitoring mental well-being in online settings.
... For understanding online mental health-related communities, some research has been done for detecting characteristics of such communities [5], [22], [3], [23], [24], [25], [15]. With questionnaire-based methods, some studies of Nimrod [3], [5], [22] focused on investigating the content and characteristics of the discussions in online depression communities to identify the member's interest and the gained benefit. ...
... In a related work, with analysis of psycholinguistics processes and topics expressed in the content of blog posts in online autism communities, [24] indicated that substantial differences between autism and general online communities are significantly characterized by latent topics of discussion and psycholinguistic features. In addition, [25], [26] analyzed properties of blogs post's textual messages including topical, psycholinguistic features, and sentiment aspects of users to examine online social capital in two extremes (high and low) to identify community among social capital groups. The study suggests that mining blogs has the potential to detect clinical information. ...
... For learning latent topics from the content of posted messages, some probabilistic topic modeling approaches (e.g., Probabilistic latent semantic indexing (pLSI) [35], Latent Dirichlet Allocation (LDA) [36] or Hierarchical Dirichlet Process (HDP) [37]) are used to learn latent topics from the corpus of blog posts. Several studies [25], [20], [26], [38], [39], [40] used the standard parametric model LDA to learn latent topics from the content of blogs and tweets in the blogosphere for their research on mental health signals on social media. Using LDA to gain latent topics, the study [26] found significant differences in among study cohorts which are characterized by the latent topics of discussion, psycholinguistic features, and tagged moods. ...
People are increasingly using social media, especially online communities, to discuss mental health issues and seek supports. Understanding topics being discussed, interaction, sentiment and clustering structures of these communities informs important aspects of mental health. It can potentially add understanding to the underlying cognitive dynamics, mood swings patterns, shared interests, and interaction. There has been growing research interest in analyzing mental health on online communities; however sentiment analysis of these communities has been largely under-explored. This study presents an analysis of online communities with and without mental health-related conditions including depression and autism. Data from 57 Live Journal weblog communities are crawled to construct three categories such as depression, autism and general. Latent patterns are extracted from both mood tags, affective words, and textual content in the blog posts of such communities. From these latent patterns, we apply nonparametric approaches to discover meta-groups of online mental communities. The best performance results can be achieved on clustering communities
with mood-based representation for such communities. The study also found significant differences in usage patterns of latent mood tags and affective features between online communities with and without affective disorders. The findings can reveal useful insights into hyper-groups detection of online mental health-related communities.
... Our work aims to improve these studies by the analysis of a set of more extensive official statistics, better detailed in 3.2. Social media analysis also suggests the prediction of stock market [2], and of collective mood state [15]. Emotions have been considered with respect to social media and their dynamics [14] [12], also with geographical concerns [6]. ...
The integration between official statistics and social media data is a challenging topic. This contribution aims to present a recentlydesigned framework to compare sentiment analysis on social media content with social and economic data. Such framework-which has already been applied, in a preliminary fashion, to the Felicitta project-is meant to integrate official statistics and correlate it with online social media data. Its ultimate goal, in fact, namely consists in giving a contribution to the definition of a measure of subjective well-being that could fully benefit from both traditional, well-established social indicators and dynamic data obtained from the web.
... Online social capital is nascent, in definition and scope. Our preliminary work [33] attempted to examine online social capital in two extremes, high and low, and different from this work, it is limited to the extraction of basic features, derived from the content and sentiment, for classification among capital groups. [23] propose a broad definition of social capital -a combination of user profile information, factors reflecting openness to new acquaintances, online activities such as frequency of posting or commenting and the contribution of the user to relationships. ...
Social capital indicative of community interaction and support is intrinsically linked to mental health. Increasing online presence is now the norm. Whilst social capital and its impact on social networks has been examined, its underlying connection to emotional response such as mood, has not been investigated. This paper studies this phenomena, revisiting the concept of “online social capital” in social media communities using measurable aspects of social participation and social support. We establish the link between online capital derived from social media and mood, demonstrating results for different cohorts of social capital and social connectivity. We use novel Bayesian nonparametric factor analysis to extract the shared and individual factors in mood transition across groups of users of different levels of connectivity, quantifying patterns and degree of mood transitions. Using more than 1.6 million users from Live Journal, we show quantitatively that groups with lower social capital have fewer positive moods and more negative moods, than groups with higher social capital. We show similar effects in mood transitions. We establish a framework of how social media can be used as a barometer for mood. The significance lies in the importance of online social capital to mental well-being in overall. In establishing the link between mood and social capital in online communities, this work may suggest the foundation of new systems to monitor online mental well-being.
