PRIOR RESEARCH: MAKING SENSE OF COLLECTIVE BEHAVIOR

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1 I combine the power of computing, technology and big data with possibilities to answer fundamental questions in social science: relating to human behavior. My research interest is in computational social science: an emerging domain at the intersection of machine learning, social science, and human computer interaction. Over the past couple of decades, the Internet has enabled great things; it has affected our everyday lives in multiple ways. Online presence is increasingly becoming intertwined with our lives one in six people in the world visit Facebook at least once a month. Egypt and Haiti demonstrated how technologies like Twitter can bring individuals together, empowering them in times of crisis. In essence, such technological advances bear a relationship with how we frame and refine our social norms as well as a variety of fundamental social and cognitive processes that engender our behavior. My interest is to understand such behavior that characterize our online social lives both at the macroscopic level of populations, and at the microscopic, i.e., the individual level, by developing computational methods and analyzing large-scale data. Through such understanding of behavior, my goal is to make a difference to real world societal problems, such as ones spanning healthcare, and thereby improve the quality of life of people in general. Deciphering human behavior, collective or individual, requires resorting to theories, methods, and resources from a variety of domains. This makes next generation research in this domain to be data-intensive, multi-disciplinary and collaborative 1. Being embedded in a cross-disciplinary environment during graduate school at the Arizona State University, as well as building several inter-team collaborative ties at Microsoft Research, I have been fortunate to have received broad experience and opportunities to spearhead research in this area. I have had the chance to collaborate with over twenty different coauthors from twelve disciplines (such as sociology, psychology, statistics, design), pursuing research from a transdisciplinary angle, in order to discover and devise better ways to make sense of our online social lives. PRIOR RESEARCH: MAKING SENSE OF COLLECTIVE BEHAVIOR A significant focus of my research has been to understand collective behavior that is manifested online. Sociologist Herbert Blumer refers to collective behavior to be social processes which do not reflect existing social structure (laws, conventions), but which emerge in a spontaneous way. I believe that such a phenomenon embodies to a large extent, the rich and diverse actions we undertake on today s social platforms handful of memes garnering unexpected viral popularity based on independent actions by individuals, or activity of online crowds giving birth to revolutions and sociopolitical activism. My research in this direction has involved three themes that touch on several prime facets of collective behavior communication dynamics among individuals, their intrinsic information roles, and finally, their emotional and affective expression. I present these in the following subsections. Theme I: Communication Dynamics My dissertation work focused on the modeling and analysis of communication dynamics in large social networks, blogs and social media, in order to characterize collective behavior. I focused on three broad questions (1) how communication among individuals leads to the diffusion of information among large populations; (2) how groups emerge (and dissolve) based on communication; and (3) how we can characterize the evolutionary characteristics of media artifacts (e.g., image, video) that engender communication on online platforms. 1 Rebuilding the Mosaic: Fostering Research in the Social, Behavioral and Economic Sciences at the National Science Foundation in the Next Decade. Publication Number: NSF /6

2 The computational models developed and the studies conducted to answer these questions revealed the importance of online social communication patterns in their ability to account for our collective behavior. (1) First, we found that the strength of ties, as measures through reciprocal interactions was a key predictor of whether a certain topic would diffuse from one individual to another in the MySpace network [1]. (2) Second, we observed that groups emerged and dissolved in large networks subject to interactions over time and over differences in language and topics, and thereafter we proposed a group evolution technique to model this phenomenon. We demonstrated the utility of our group detection and evolution technique with their ability to predict external world events and indicators. In this light, we observed that the activity of groups that showed evidence of communication around different NASDAQ companies on the technology blog Engadget could be correlated with financial indicators over time (e.g., stock market movement) [2]. Similarly, the communication patterns of the detected groups on the politics section of the blog Huffington Post were predictive of the outcomes in the 2008 US Presidential elections [3]. (3) Finally, our studies revealed that subject to interactions among individuals, the characteristics of shared artifacts change we observed that the interestingness property of YouTube videos evolved over time, as the interactional behavior of the commenters changed; and thereafter we proposed a model that can quantify such an interestingness metric of shared media artifacts [4]. Theme II: Intrinsic Roles of Individuals A key characteristic that seamlessly impacts our online communication processes is the intrinsic role that characterizes an individual. Such roles affect the larger collective individuals are a part of such as, the information sharing decisions they make; how they socially influence members of various groups; as well as the manner in which their reputation and interests drive the popularity or interestingness of shared media artifacts. With this motivation in mind, during my employment at Rutgers University, we characterized the intrinsic information roles of individuals on Twitter in the light of how they post content around small and big events [5,6]. We developed an automated classifier of high accuracy that identified three information roles: organizations, journalists/media bloggers and ordinary individuals 2. Across events ranging from local music concerts to happenings of global impact such as the death of Osama bin Laden, we observed that these automatically inferred intrinsic information roles helped us better understand collective behavior at scale, overcoming limitations of prior efforts on manually coding such roles. We could identify who are the stakeholders of information in a large community, as well as what is the relationship between production and consumption of information as important events unfold in the landscape of today s online social platforms. Theme III: Emotion and Mood Beyond communication processes and intrinsic roles that characterize our behavior, it is wellestablished that our emotion and mood play a critical role in our everyday lives, fundamentally directing our attention and responses to environment, framing our attitudes and impacting our social relationships. Given the important role played by mood and emotion in defining our collective behavior, after I joined Microsoft Research, I directed my research towards the analysis of mood and emotion manifested in social media. We utilized a popular psycholinguistic instrument, known as the circumplex model, that characterizes affective experience through two dimensions: valence and activation, and developed a methodology to identify more than 200 moods frequently shared on Twitter [7]. Thereafter, through the study of these moods, we reported on four aspects of emotional 2 The paper [6] received a Best Paper Honorable Mention award at CHI /6

3 expression defining collective behavior: the relationship between moods and (1) their usage levels, (2) the social ties individuals form, (3) amount of network activity, and (4) participatory patterns of individuals such as conversational engagement. Our results provided, for the first time and the largest so far, at-scale naturalistic assessments and extensions of existing conceptualizations of human mood. In response to requests from other researchers both within and outside of Microsoft, recently we released the mood lexicon together with their valence and activation attributes to assist others in advancing the understanding of human mood in online social platforms. As a complementary research direction, we also developed automated classifiers of high accuracy to infer mood states from Twitter posts [8] an aspect extensively useful in characterizing user behavior in search and business intelligence-centric analytics. This work was eventually picked up by several research groups at Microsoft as well as product teams, and went on to become part of the Social Media Analytics release 3. CURRENT RESEARCH: MAKING SENSE OF INDIVIDUAL BEHAVIOR Beyond collective behavior that emerges out of the activity and interactions between several individuals, today s social platforms are increasingly becoming a rich source of sensors that record and reflect individual-centric thoughts, feeling or opinions about small and big happenings in one s life. As many of these platforms increasingly gain traction among individuals, they are building up large user bases, including many people who have been using the service for years. What could analyses of individual-centric behavior, thus manifested over long periods of time, explain? Potentially, the longitudinal records of behavior can help detect behavioral changes around a variety of life events: childbirth, marriage, loss of a job, divorce, or a severe car accident. Detecting these changes could be useful not only from a self-reflection perspective, some of these changes could also indicate and reveal otherwise not apparent mental or behavioral health concerns. My current research [9] pursues the exploration of individual-focused behavioral patterns on social media sites to help reveal changes in the lives of new mothers following the birth of a child. We specifically examine patterns of activity, emotional, and linguistic correlates for childbirth and postnatal course based on postings of new mothers on Twitter. Based on our empirical studies, we found that childbirth causes some change for most new mothers, but approximately 15% show significant changes in their activity and emotional expression postpartum, including decreased social interactions, reduced activation of moods and generally negative emotions. Personal Health and Well-being. An exciting implication of our findings is the possibility of leveraging individual-centric behavior in social media for unobtrusive diagnostic measures of emotional disorders in new mothers, such as postpartum depression (PPD). Such modeling can be extended to make predictions in advance of birth about those mothers who are at high risk of suffering with a mood disorder following the birth of the child they are carrying. In fact, our detected group of approximately 15% of new mothers who showed broad and significant changes in behavioral and emotional expression following childbirth aligns with published reported rates of PPD in the United States. To this end, as a concrete way to validate that social media signals could indeed help in the diagnosis of PPD, currently we are engaging in conversations with physicians and healthcare providers to collect ground truth clinical data on PPD incidence in the general population. Through these collaborations, we hope to be able to gauge the practical utility of this research in helping new mothers improve their postpartum experiences, and perhaps even provide valuable and timely interventions that can in turn promote wellness. 3 codename social analytics/archive/2012/02/02/lab-bonus-enhanced -sentiment-analysis-for-twitter-from- microsoft-research.aspx 3/6

4 FUTURE AGENDA: SOCIOECONOMIC DEVELOPMENT & PROSPERITY Short-term Goals Mental & Public Health. A key aspect of socioeconomic progress and prosperity lies in enabling better healthcare opportunities to individuals and populations. Like the case of new mothers in postpartum, behavioral cues obtained from naturalistic data shared in a public setting, like social media, may be useful to detect and even forecast a variety of mental and public health concerns in individuals. For example, in an imminent research direction, I am interested in investigating how these cues may help predict bipolar mood disorder, dysthymia, depressive episodes, or other types of behavioral disorders or concerns (e.g., Posttraumatic stress disorder: PTSD) that may be triggered due to traumatic life events: including personal events such as loss of a loved one, or community or global events such as political unrest or natural disasters. These are conditions like PPD whose early detection can benefit individuals not only from a personal health monitoring and intervention perspective, but also through privacy-honoring design, can help healthcare providers and practitioners identify people at risk ahead of time, by complementing traditional clinical diagnosis techniques. Thereby they can create opportunities for individuals either to receive adequate medical help early on, reduce the stigma around mental illness, even lower healthcare cost, or generally to enable better estimation of the risks of populations to these conditions 4. In another imminent research direction, I am interested in mining the behavior of individuals from the perspective of encouraging healthful living. As an example, I hope to devise methods and analytical tools that mine cues of physical fitness and generally healthy and active lifestyle (e.g., via smartphone applications like FitBit, NikePlus). Identifying the behavioral patterns of healthy lifestyle in general what healthy people do, what they eat, where they go and so on, as well as understanding the nature of their friend networks, can possibly help us identify ways that these attributes be embedded in the design of future social platforms or search engines to motivate people adopt a healthy and active lifestyle. Perhaps even their network-centric attributes may be leveraged to help combat one of the most outstanding questions in the public health domain: how to motivate people to be fit and thereby control the so called obesity epidemic, an increasingly grave concern in the United States 5. Long-term Goals Environment, Education, Disaster & Urban Informatics. My long-term plans comprise generalizations from the healthcare domain to other important socioeconomic issues environmental change, economic decision-making and disaster response, education, as well as cultural evolution. I believe the analysis of human behavior on online social platforms bears significant potential in making a difference to generating awareness among individuals (environmental change); identifying systemic causes behind socioeconomic disasters and crises (economics); devising methods to understand mobility patterns of people and thereby promoting civic engagement among them for sociocultural good (urbanization); propagating and spreading important ideas and thoughts among large populations (education), and thereby promoting prosperity in general. In the coming years, I hope to be able to make an impact in these areas, broadly in the domain of socioeconomic development, by leveraging the breadth of data we are gathering about individuals behavior online. I believe there is also tremendous value in attempting to understand collective and individualcentric behavior of people with limited computing capabilities. A particular subarea and population 4 Several mental health disorders today are under-reported according to the Centers for Disease Control & Prevention (CDC) /6

5 of interest where deciphering these behavioral cues could be profusely beneficial are developing countries. For instance, by dint of these cues, I envision that we can devise low-cost mechanisms and tools to aid health workers and patients in improving health outcomes in underserved rural environments. Social networks and media, facilitated through basic mobile phone usage, a rather widely adopted technology in developing countries like India and China 6, can even perhaps be used to develop novel educational paradigms to help under-privileged children. CONCLUSION: CHALLENGES, OPPORTUNITIES & IMPACT Privacy, Ethics. Although big data as arising from the use of online social tools is a huge opportunity for researchers to make a difference to addressing real world problems, it comes with unique challenges. These include theoretical and methodological concerns ranging from sampling, scale, and diversity issues aspects that I have begun to address in [10,11]. These are among the first efforts to tackle these problems and continue to be highly cited. Another set of serious challenges are privacy and ethical concerns: access to different sources of data raises new issues about human-centric experiment design and methods, as well as the familiar concern about inadvertently disclosing sensitive information, especially when dealing with healthcare-centric domains. Future holds a number of questions to pursue: Can we develop behavioral analysis models and health intervention systems that are privacy-preserving? What are the primary issues that arise when such health intervention systems are deployed in the general population? Essentially, we must bring the possibilities and challenges to the fore, to leverage the benefits of mining and understanding online social behavior to enhance the quality of life for people as well as to stimulate discussion and awareness of potential concerns at the individual and societal levels. Data Access, Evaluation, Standards. Data accessibility and reproducibility also need attention. Standards are needed that will enable researchers in different domains to share and exchange findings relevant to activity on online platforms. To address this issue, apart from the efforts on sharing the lexicon of human moods mentioned earlier, during my tenure at ASU I was actively involved in public release of large-scale social datasets 7 (Twitter/Digg/Flickr etc.) that I had collected or crawled during the course of my PhD research. These resources have already benefited a host of scientists in their respective research problems, spanning academia and industry research labs. Transparency of data and methods will help us develop a set of standards, metrics and evaluation methodologies in the coming years which will in turn help us make better sense of human behavior across platforms and domains. In closing, even in the midst of these concerns, it is worth noting again that the modern world presents us with a series of challenges that call for integrated responses across a variety of disciplines. There is growing recognition that deciphering the behavior of individuals and populations can be of utmost utility: such opportunities are increasingly acknowledged by the National Science Foundation and the National Institutes of Health. In this light, I am confident that I can make a practical impact in advancing the state of the art in our understanding of human behavior, as well as find ways to make my research insights actionable for the general population. I passionately believe that building on my endeavors so far, while building new collaborations, will lead to progress in realizing this vision. 6 The majority of the world s 4 billion mobile subscriptions are in the developing world: /6

6 REFERENCES [1] De Choudhury, M., Sundaram, H., John, A., and Seligmann, D. D. (2007). Contextual Prediction of Communication Flow in Social Networks. In Proc. IEEE/WIC/ACM international Conference on Web intelligence (WI 2007) [2] De Choudhury, M., Sundaram, H., John, A., and Seligmann, D. D. (2008). Can blog communication dynamics be correlated with stock market activity? In Proc. ACM conference on Hypertext and hypermedia (HT 2008) [3] De Choudhury, M., Sundaram, H., John, A., and Seligmann, D. D. (2010). Extraction, Characterization and Utility of Prototypical Groups in the Blogosphere. In ACM Trans. On Information Systems (TOIS), Vol 29, Issue 1, Article 6, 53 pages. [4] De Choudhury, M., Sundaram, H., John, A., and Seligmann, D. D. (2009). What Makes Conversations Interesting? Themes, Participants and Consequences of Conversations in Online Social Media. In Proc. Int l World Wide Web Conference (WWW 2009) [5] De Choudhury, M., Diakopoulos, N., and Naaman, M. (2012). Unfolding the event landscape on twitter: classification and exploration of user categories. In Proc. ACM 2012 conference on Computer Supported Cooperative Work (CSCW 2012) Best Note Nomination [6] Diakopoulos, N., De Choudhury, M., and Naaman, M. (2012). Finding and assessing social media information sources in the context of Journalism. In Proc ACM annual conference on Human Factors in Computing Systems (CHI 2012) Best Paper Honorable Mention [7] De Choudhury, M., Counts, S., and Gamon, M. (2012). Not All Moods are Created Equal! Exploring Human Emotional States in Social Media. In Proc. Int l AAAI Conference on Weblogs and Social Media (ICWSM 2012). [8] De Choudhury, M., Gamon, M., and Counts, S. (2012). Happy, Nervous or Surprised? Classification of Human Affective States in Social Media. In Proc. Int l AAAI Conference on Weblogs and Social Media (ICWSM 2012). [9] De Choudhury, M., Counts S., and Horvitz, E. (2013). Major Life Events and Behavioral Markers in Social Media: Case of Childbirth. In Proc. ACM 2013 conference on Computer Supported Cooperative Work (CSCW 2013). To appear. [10] De Choudhury, M., Lin, Y-R., Sundaram, H., Candan, K. S., Xie, L., and Kelliher, A. (2010). How Does the Sampling Strategy Impact the Discovery of Information Diffusion in Social Media? In Proc. Int l AAAI Conference on Weblogs and Social Media (ICWSM 2010). [11] De Choudhury, M., Mason, W. A., Hofman, J. M., and Watts, D. J. (2010). Inferring relevant social networks from interpersonal communication. In Proc. International conference on World wide web (WWW 2010). 452(2008), /6