Understanding Affect in the Workplace via Social Media

We investigate the landscape of affective expression of employees at a large Fortune 500 software corporation via an internal microblogging tool. We present three analyses of emotional expression among employees, based on literature in organizational behavior: its relationship to (1) exogenous/lifestyle and endogenous workplace factors, (2) geography and (3) organizational structure. We find that employees tend to make significant accommodations in affect expression when interacting with others over the organizational hierarchy. We also find that positive affect is expressed through interpersonal communications that connect disparate geographic regions. Our findings have implications for enabling emotional reflection of employees and for management in that they can help uncover emotional patterns associated with episodes of high and low productivity, allowing organizations to improve employee engagement and promote positive attitudes

Not All Moods Are Created Equal! Exploring Human Emotional States in Social Media

Emotional states of individuals, also known as moods, are central to the expression of thoughts, ideas and opinions, and in turn impact attitudes and behavior. As social media tools are increasingly used by individuals to broadcast their day-to-day happenings, or to report on an external event of interest, understanding the rich ‘landscape’ of moods will help us better interpret and make sense of the behavior of millions of individuals. Motivated by literature in psychology, we study a popular representation of human mood landscape, known as the ‘circumplex model’ that characterizes affective experience through two dimensions: valence and activation. We identify more than 200 moods frequent on Twitter, through mechanical turk studies and psychology literature sources, and report on four aspects of mood expression: the relationship between (1) moods and usage levels, including linguistic diversity of shared content (2) moods and the social ties individuals form, (3) moods and amount of network activity of individuals, and (4) moods and participatory patterns of individuals such as link sharing and conversational engagement. Our results provide at-scale naturalistic assessments and extensions of existing conceptualizations of human mood in social media contexts

Social Media as a Measurement Tool of Depression in Populations

Depression is a serious and widespread public health challenge. We examine the potential for leveraging social media postings as a new type of lens in understanding depression in populations. Information gleaned from social media bears potential to complement traditional survey techniques in its ability to provide finer grained measurements over time while radically expanding population sample sizes. We present work on using a crowdsourcing methodology to build a large corpus of postings on Twitter that have been shared by individuals diagnosed with clinical depression. Next, we develop a probabilistic model trained on this corpus to determine if posts could indicate depression. The model leverages signals of social activity, emotion, and language manifested on Twitter. Using the model, we introduce a social media depression index that may serve to characterize levels of depression in populations. Geographical, demographic and seasonal patterns of depression given by the measure confirm psychiatric findings and correlate highly with depression statistics reported by the Centers for Diseas

Identifying relevant social media content: leveraging information diversity and user cognition

ABSTRACT As users turn to large scale social media systems like Twitter for topic-based content exploration, they quickly face the issue that there may be hundreds of thousands of items matching any given topic they might query. Given the scale of the potential result sets, how does one identify the "best" or "right" set of items? We explore a solution that aligns characteristics of the information space, including specific content attributes and the information diversity of the results set, with measurements of human information processing, including engagement and recognition memory. Using Twitter as a test bed, we propose a greedy iterative clustering technique for selecting a set of items on a given topic that matches a specified level of diversity. In a user study, we show that our proposed method yields sets of items that were, on balance, more engaging, better remembered, and rated as more interesting and informative compared to baseline techniques. Additionally, diversity indeed seemed to be important to participants in the study in the consumption of content. However as a rather surprising result, we also observe that content was perceived to be more relevant when it was highly homogeneous or highly heterogeneous. In this light, implications for the selection and evaluation of topic-centric item sets in social media contexts are discussed

Happy, Nervous or Surprised? Classification of Human Affective States in Social Media

Sentiment classification has been a well-investigated research area in the computational linguistics community. However, most of the research is primarily focused on detecting simply the polarity in text, often needing extensive manual labeling of ground truth. Additionally, little attention has been directed towards a finer analysis of human moods and affective states. Motivated by research in psychology, we propose and develop a classifier of several human affective states in social media. Starting with about 200 moods, we utilize mechanical turk studies to derive naturalistic signals from posts shared on Twitter about a variety of affects of individuals. This dataset is then deployed in an affect classification task with promising results. Our findings indicate that different types of affect involve different emotional content and usage styles; hence the performance of the classifier on various affects can differ considerably

Find Me the Right Content! Diversity-Based Sampling of Social Media Spaces for Topic-Centric Search

Social media and networking websites, such as Twitter and Facebook, generate large quantities of information and have become mechanisms for real-time content dissipation to users. An important question that arises is: how do we sample such social media information spaces in order to deliver relevant content on a topic to end users? Notice that these large-scale information spaces are inherently diverse, featuring a wide array of attributes such as location, recency, degree of diffusion effects in the network and so on. Naturally, for the end user, different levels of diversity in social media content can significantly impact the information consumption experience: low diversity can provide focused content that may be simpler to understand, while high diversity can increase breadth in the exposure to multiple opinions and perspectives. Hence to address our research question, we turn to diversity as a core concept in our proposed sampling methodology. Here we are motivated by ideas in the "compressive sensing" literature and utilize the notion of sparsity in social media information to represent such large spaces via a small number of basis components. Thereafter we use a greedy iterative clustering technique on this transformed space to construct samples matching a desired level of diversity. Based on Twitter Firehose data, we demonstrate quantitatively that our method is robust, and performs better than other baseline techniques over a variety of trending topics. In a user study, we further show that users find samples generated by our method to be more interesting and subjectively engaging compared to techniques inspired by state-of-the-art systems, with improvements in the range of 15--45%

Predicting Depression via Social Media

Major depression constitutes a serious challenge in personal and public health. Tens of millions of people each year suffer from depression and only a fraction receives adequate treatment. We explore the potential to use social media to detect and diagnose major depressive disorder in individuals. We first employ crowdsourcing to compile a set of Twitter users who report being diagnosed with clinical depression, based on a standard psychometric instrument. Through their social media postings over a year preceding the onset of depression, we measure behavioral attributes relating to social engagement, emotion, language and linguistic styles, ego network, and mentions of antidepressant medications. We leverage these behavioral cues, to build a statistical classifier that provides estimates of the risk of depression, before the reported onset. We find that social media contains useful signals for characterizing the onset of depression in individuals, as measured through decrease in social activity, raised negative affect, highly clustered egonetworks, heightened relational and medicinal concerns, and greater expression of religious involvement. We believe our findings and methods may be useful in developing tools for identifying the onset of major depression, for use by healthcare agencies; or on behalf of individuals, enabling those suffering from depression to be more proactive about their mental health