Depression is a crippling burden on a great many people, and it is often well hidden. Mental health professionals are able to treat depression, but the general public is not well versed in recognizing depression symptoms or assessing their own mental health. Active Listening Journal Interaction (ALJI) is a computer program that seeks to identify and refer people suffering with depression to mental health support services. It does this through analyzing personal journal entries using machine learning, and then privately responding to the author with proper guidance. In this thesis, we focus on determining the feasibility and usefulness of the machine learning models that drive ALJI. With heavy data limitations, we cautiously report that with a single journal entry, our model detects when a person's symptoms warrant professional intervention with a 61% accuracy. A great amount of discussion on the proposed solution, methods, results, and future directions of ALJI is included. / Master of Science / An incredibly large number of people suffer from depression, and they can rightfully feel trapped or imprisoned by this illness. A very simple way to understand depression is to first imagine looking at the most beautiful sunset you've ever seen, and then imagine feeling absolutely nothing while looking that same sunset, and you can't explain why. When a person is depressed, they are likely to feel like a burden to those around them. This causes them to avoid social gathering and friends, making them isolated away from people that could support them. This worsens their depression and a terrible cycle begins. One of the best ways out of this cycle is to reveal the depression to a doctor or psychologist, and to ask them for guidance. However, many people don't see or realize this excellent option is open to them, and will continue to suffer with depression for far longer than needed. This thesis describes an idea called the Active Listening Journal Interaction, or ALJI. ALJI acts just like someone's personal journal or diary, but it also has some protections from illnesses like depression. First, ALJI searches a journal entry for indicators about the author's health, then ALJI asks the author a few questions to better understand the author, and finally ALJI gives that author information and guidance on improving their health. We are starting to create a computer program of ALJI by first building and testing the detector for the author's health. Instead of making the detector directly, we show the computer some examples of the health indicators from journals we know very well, and then let the computer focus on finding the pattern that would reveal those health indicators from any journal. This is called machine learning, and in our case, ALJI's machine learning is going to be difficult because we have very few example journals where we know all of the health indicators. However, we believe that fixing this issue would solve the first step of ALJI. The end of this thesis also discusses the next steps going forward with ALJI.

Due to online social media, access to information is becoming easier and easier. Meanwhile, the truthfulness of online information is often not guaranteed. Incorrect information, often called misinformation, can have several modalities, and it can spread to multiple social media platforms in different languages, which can be destructive to society. However, academia and industry do not have automated ways to assess the impact of misinformation on social media, preventing the adoption of productive strategies to curb the prevalence of misinformation. In this dissertation, I present my research to build computational pipelines that help measuring and detecting misinformation on social media. My work can be divided into three parts. The first part focuses on processing misinformation in text form. I first show how to group political news articles from both trustworthy and untrustworthy news outlets into stories. Then I present a measurement analysis on the spread of stories to characterize how mainstream and fringe Web communities influence each other. The second part is related to analyzing image-based misinformation. It can be further divided into two parts: fauxtography and generic image misinformation. Fauxtography is a special type of image misinformation, where images are manipulated or used out-of-context. In this research, I present how to identify fauxtography on social media by using a fact-checking website (Snopes.com), and I also develop a computational pipeline to facilitate the measurement of these images at scale. I next focus on generic misinformation images related to COVID-19. During the pandemic, text misinformation has been studied in many aspects. However, very little research has covered image misinformation during the COVID-19 pandemic. In this research, I develop a technique to cluster visually similar images together, facilitating manual annotation, to make subsequent analysis possible. The last part is about the detection of misinformation in text form following a multi-language perspective. This research aims to detect textual COVID-19 related misinformation and what stances Twitter users have towards such misinformation in both English and Chinese. To achieve this goal, I experiment on several natural language processing (NLP) models to investigate their performance on misinformation detection and stance detection in both monolingual and multi-lingual manners. The results show that two models: COVID-Tweet-BERT v2 and BERTweet are generally effective in detecting misinformation and stance in the two above manners. These two models are promising to be applied to misinformation moderation on social media platforms, which heavily depends on identifying misinformation and stance of the author towards this piece of misinformation. Overall, the results of this dissertation shed light on understanding of online misinformation, and my proposed computational tools are applicable to moderation of social media, potentially benefitting for a more wholesome online ecosystem.

No description available.

Communicative acts constitute organizations as social entities. I build upon the most structured previous analysis of this process, the four flows framework, by introducing a complex systems model of how organization emerges along a continuum, thereby enabling measurement of the growth and decline of partial organizations. I validate my approach using simulated data from two stochastic agent-based models and 30 historical case studies of insurgency. I show that the four flows may be used to assess the historical victor of a conflict, or to track the emergence of an organization from real-time communication network data. My results demonstrate the complex interrelationship of the four flows, and how they relate to social phenomena such as information asymmetry, individual versus group interest, governance, and the development of community structure. I reaffirm the centrality of these flows to the phenomenon of organization, while challenging the minimum requirements for it to begin, by showing that organization spontaneously emerges in a population as a result of markers of affiliation and human cognitive biases. / Doctor of Philosophy / Humans organize collective behavior by communicating. Prior research has shown that all organizations establish the costs and benefits of membership, distinctions from other organizations, enduring protocols, and approaches to short-term coordination. The strength with which organizations define each of these traits emerges on a continuum from a nascent organization to a robust one. My work is the first to place these acts of communication in an engineering model, showing how an organization works as a system to reduce collective uncertainty. I first explore my model in a computer simulation, demonstrating that each of the four processes can be measured. I then quantify the strength of each process in 30 case studies of insurgency, measuring the changing effectiveness of the insurgents and their state opponents at establishing themselves as the governing entity in an area. My technique accurately predicts the outcome of all 30 case studies. Finally, using a second simulation, I demonstrate measures of all four processes in communication records and show that organization may be the result of merely recognizing oneself as part of a group, amid basic patterns in human thinking, rather than evidence of cooperation toward shared objectives.

This thesis utilizes social influence theory and computational tools to examine the disparate impact of positive and negative ties in nuclear weapons proliferation. The thesis is broadly in two sections: a simulation section, which focuses on government stakeholders, and a large-scale data analysis section, which focuses on the public and domestic actor stakeholders. In the simulation section, it demonstrates that the nonproliferation norm is an emergent behavior from political alliance and hostility networks, and that alliances play a role in current day nuclear proliferation. This model is robust and contains second-order effects of extended hostility and alliance relations. In the large-scale data analysis section, the thesis demonstrates the role that context plays in sentiment evaluation and highlights how Twitter collection can provide useful input to policy processes. It first highlights the results of an on-campus study where users demonstrated that context plays a role in sentiment assessment. Then, in an analysis of a Twitter dataset of over 7.5 million messages, it assesses the role of ‘noise’ and biases in online data collection. In a deep dive analyzing the Iranian nuclear agreement, we demonstrate that the middle east is not facing a nuclear arms race, and show that there is a structural hole in online discussion surrounding nuclear proliferation. By combining both approaches, policy analysts have a complete and generalizable set of computational tools to assess and analyze disparate stakeholder roles in nuclear proliferation.

Social identities, the labels we use to describe ourselves and others, carry with them stereotypes that have significant impacts on our social lives. Our stereotypes, sometimes without us knowing, guide our decisions on whom to talk to and whom to stay away from, whom to befriend and whom to bully, whom to treat with reverence and whom to view with disgust. Despite these impacts of identities and stereotypes on our lives, existing methods used to understand them are lacking. In this thesis, I first develop three novel computational tools that further our ability to test and utilize existing social theory on identity and stereotypes. These tools include a method to extract identities from Twitter data, a method to infer affective stereotypes from newspaper data and a method to infer both affective and semantic stereotypes from Twitter data. Case studies using these methods provide insights into Twitter data relevant to the Eric Garner and Michael Brown tragedies and both Twitter and newspaper data from the “Arab Spring”. Results from these case studies motivate the need for not only new methods for existing theory, but new social theory as well. To this end, I develop a new sociotheoretic model of identity labeling - how we choose which label to apply to others in a particular situation. The model combines data, methods and theory from the social sciences and machine learning, providing an important example of the surprisingly rich interconnections between these fields.

Metódos analíticos de investigação são usualmente ineficazes para sistemas computacionais sociais já que apenas algumas iterações do sistema já são suficientes para que o sistema se torne imprevisível. Portanto, uma das principais questões na Computação Social é o desenvolvimento de modelos sociais passíveis de investigação. Assim é possível que se compreenda o relacionamento complexo entre os componentes de sistemas sociais computacionais e o resultado. Este aspectos incluem a modelagem, a estrutura de comunicação e características individuais do agentes envolvidos na resolução dos problemas. do processo social. Esta tese explora sistemas computacionais de resolução de problemas com foco em sistemas artificiais e heterogêneos. Nela é feita uma compilação extensiva da literatura relacionada em sistemas complexos onde as contribuições do candidato são expostas dentro de contextos específicos da área. Entre elas está o estudo de modelos abstratos e gerais de resolução social de problemas, a investigação do impacto da centralidade no resultado individual e coletivo, a análise experimental de modelos heterogêneos de resolução social de problemas. Quando integradas, estas contribuições reforçam o entendimento sobre a importância da rede e das estruturas de comunicação, a composição estratégica do sistema, a estrutura do problema e possíveis padrões gerais na resolução social de problemas. / This thesis reviews and investigates social problem-solving with a particular focus on artificial and heterogeneous systems. More specifically, we not only compile and comprehensively examine recent research results, but also discuss future directions in the study of such heterogeneous complex systems. Given their complex nature, such systems often defy analyses. Even computationally simple models can behave unpredictably after a few iterations. Therefore, one central issue in Social Computing is to devise models of social interaction that are amenable to investigation. This way, one can understand the complex relationships among the components and the outcome of the social process. This thesis surveys scientific inquiries concerned with fundamental aspects in social problemsolving systems and their impact in ability and performance of such systems. These aspects include modeling, communication structure and individual problem-solver traits. This thesis also reports the student endeavour during the period of research and summarizes several already published contributions. Among them there is (i) the study of general frameworks for the study of social problem-solving, (ii) the investigation of the role of centrality in individual and collective outcomes, and (iii) the exploration of heterogeneous models of social problem-solving. These three points, in an integrated perspective underpin the understanding of network and communication structures, adjust the strategic systems’ composition, and exploit problems’ structures and patterns in social problemsolving systems.

What can text corpora tell us about society? How can automatic text analysis algorithms efficiently and reliably analyze the social processes revealed in language production? This work develops statistical text analyses of dynamic social and news media datasets to extract indicators of underlying social phenomena, and to reveal how social factors guide linguistic production. This is illustrated through three case studies: first, examining whether sentiment expressed in social media can track opinion polls on economic and political topics (Chapter 3); second, analyzing how novel online slang terms can be very specific to geographic and demographic communities, and how these social factors affect their transmission over time (Chapters 4 and 5); and third, automatically extracting political events from news articles, to assist analyses of the interactions of international actors over time (Chapter 6). We demonstrate a variety of computational, linguistic, and statistical tools that are employed for these analyses, and also contribute MiTextExplorer, an interactive system for exploratory analysis of text data against document covariates, whose design was informed by the experience of researching these and other similar works (Chapter 2). These case studies illustrate recurring themes toward developing text analysis as a social science methodology: computational and statistical complexity, and domain knowledge and linguistic assumptions.

Metódos analíticos de investigação são usualmente ineficazes para sistemas computacionais sociais já que apenas algumas iterações do sistema já são suficientes para que o sistema se torne imprevisível. Portanto, uma das principais questões na Computação Social é o desenvolvimento de modelos sociais passíveis de investigação. Assim é possível que se compreenda o relacionamento complexo entre os componentes de sistemas sociais computacionais e o resultado. Este aspectos incluem a modelagem, a estrutura de comunicação e características individuais do agentes envolvidos na resolução dos problemas. do processo social. Esta tese explora sistemas computacionais de resolução de problemas com foco em sistemas artificiais e heterogêneos. Nela é feita uma compilação extensiva da literatura relacionada em sistemas complexos onde as contribuições do candidato são expostas dentro de contextos específicos da área. Entre elas está o estudo de modelos abstratos e gerais de resolução social de problemas, a investigação do impacto da centralidade no resultado individual e coletivo, a análise experimental de modelos heterogêneos de resolução social de problemas. Quando integradas, estas contribuições reforçam o entendimento sobre a importância da rede e das estruturas de comunicação, a composição estratégica do sistema, a estrutura do problema e possíveis padrões gerais na resolução social de problemas. / This thesis reviews and investigates social problem-solving with a particular focus on artificial and heterogeneous systems. More specifically, we not only compile and comprehensively examine recent research results, but also discuss future directions in the study of such heterogeneous complex systems. Given their complex nature, such systems often defy analyses. Even computationally simple models can behave unpredictably after a few iterations. Therefore, one central issue in Social Computing is to devise models of social interaction that are amenable to investigation. This way, one can understand the complex relationships among the components and the outcome of the social process. This thesis surveys scientific inquiries concerned with fundamental aspects in social problemsolving systems and their impact in ability and performance of such systems. These aspects include modeling, communication structure and individual problem-solver traits. This thesis also reports the student endeavour during the period of research and summarizes several already published contributions. Among them there is (i) the study of general frameworks for the study of social problem-solving, (ii) the investigation of the role of centrality in individual and collective outcomes, and (iii) the exploration of heterogeneous models of social problem-solving. These three points, in an integrated perspective underpin the understanding of network and communication structures, adjust the strategic systems’ composition, and exploit problems’ structures and patterns in social problemsolving systems.

Metódos analíticos de investigação são usualmente ineficazes para sistemas computacionais sociais já que apenas algumas iterações do sistema já são suficientes para que o sistema se torne imprevisível. Portanto, uma das principais questões na Computação Social é o desenvolvimento de modelos sociais passíveis de investigação. Assim é possível que se compreenda o relacionamento complexo entre os componentes de sistemas sociais computacionais e o resultado. Este aspectos incluem a modelagem, a estrutura de comunicação e características individuais do agentes envolvidos na resolução dos problemas. do processo social. Esta tese explora sistemas computacionais de resolução de problemas com foco em sistemas artificiais e heterogêneos. Nela é feita uma compilação extensiva da literatura relacionada em sistemas complexos onde as contribuições do candidato são expostas dentro de contextos específicos da área. Entre elas está o estudo de modelos abstratos e gerais de resolução social de problemas, a investigação do impacto da centralidade no resultado individual e coletivo, a análise experimental de modelos heterogêneos de resolução social de problemas. Quando integradas, estas contribuições reforçam o entendimento sobre a importância da rede e das estruturas de comunicação, a composição estratégica do sistema, a estrutura do problema e possíveis padrões gerais na resolução social de problemas. / This thesis reviews and investigates social problem-solving with a particular focus on artificial and heterogeneous systems. More specifically, we not only compile and comprehensively examine recent research results, but also discuss future directions in the study of such heterogeneous complex systems. Given their complex nature, such systems often defy analyses. Even computationally simple models can behave unpredictably after a few iterations. Therefore, one central issue in Social Computing is to devise models of social interaction that are amenable to investigation. This way, one can understand the complex relationships among the components and the outcome of the social process. This thesis surveys scientific inquiries concerned with fundamental aspects in social problemsolving systems and their impact in ability and performance of such systems. These aspects include modeling, communication structure and individual problem-solver traits. This thesis also reports the student endeavour during the period of research and summarizes several already published contributions. Among them there is (i) the study of general frameworks for the study of social problem-solving, (ii) the investigation of the role of centrality in individual and collective outcomes, and (iii) the exploration of heterogeneous models of social problem-solving. These three points, in an integrated perspective underpin the understanding of network and communication structures, adjust the strategic systems’ composition, and exploit problems’ structures and patterns in social problemsolving systems.