What Twitter Profile and Posted Images Reveal About Depression and Anxiety

Previous work has found strong links between the choice of social media images and users' emotions, demographics and personality traits. In this study, we examine which attributes of profile and posted images are associated with depression and anxiety of Twitter users. We used a sample of 28,749 Facebook users to build a language prediction model of survey-reported depression and anxiety, and validated it on Twitter on a sample of 887 users who had taken anxiety and depression surveys. We then applied it to a different set of 4,132 Twitter users to impute language-based depression and anxiety labels, and extracted interpretable features of posted and profile pictures to uncover the associations with users' depression and anxiety, controlling for demographics. For depression, we find that profile pictures suppress positive emotions rather than display more negative emotions, likely because of social media self-presentation biases. They also tend to show the single face of the user (rather than show her in groups of friends), marking increased focus on the self, emblematic for depression. Posted images are dominated by grayscale and low aesthetic cohesion across a variety of image features. Profile images of anxious users are similarly marked by grayscale and low aesthetic cohesion, but less so than those of depressed users. Finally, we show that image features can be used to predict depression and anxiety, and that multitask learning that includes a joint modeling of demographics improves prediction performance. Overall, we find that the image attributes that mark depression and anxiety offer a rich lens into these conditions largely congruent with the psychological literature, and that images on Twitter allow inferences about the mental health status of users.Comment: ICWSM 201

Dynamics of Information Distribution on Social Media Platforms during Disasters

abstract: When preparing for and responding to disasters, humanitarian organizations must run effective and efficient supply chains to deliver the resources needed by the affected population. The management of humanitarian supply chains include coordinating the flows of goods, finances, and information. This dissertation examines how humanitarian organizations can improve the distribution of information, which is critical for the planning and coordination of the other two flows. Specifically, I study the diffusion of information on social media platforms since such platforms have emerged as useful communication tools for humanitarian organizations during times of crisis. In the first chapter, I identify several factors that affect how quickly information spreads on social media platforms. I utilized Twitter data from Hurricane Sandy, and the results indicate that the timing of information release and the influence of the content’s author determine information diffusion speed. The second chapter of this dissertation builds directly on the first study by also evaluating the rate at which social media content diffuses. A piece of content does not diffuse in isolation but, rather, coexists with other content on the same social media platform. After analyzing Twitter data from four distinct crises, the results indicate that other content’s diffusion often dampens a specific post’s diffusion speed. This is important for humanitarian organizations to recognize and carries implications for how they can coordinate with other organizations to avoid inhibiting the propagation of each other’s social media content. Finally, a user’s followers on social media platforms represent the user’s direct audience. The larger the user’s follower base, the more easily the same user can extensively broadcast information. Therefore, I study what drives the growth of humanitarian organizations’ follower bases during times of normalcy and emergency using Twitter data from one week before and one week after the 2016 Ecuador earthquake.Dissertation/ThesisDoctoral Dissertation Business Administration 201

Citizen Science and Geospatial Capacity Building

This book is a collection of the articles published the Special Issue of ISPRS International Journal of Geo-Information on “Citizen Science and Geospatial Capacity Building”. The articles cover a wide range of topics regarding the applications of citizen science from a geospatial technology perspective. Several applications show the importance of Citizen Science (CitSci) and volunteered geographic information (VGI) in various stages of geodata collection, processing, analysis and visualization; and for demonstrating the capabilities, which are covered in the book. Particular emphasis is given to various problems encountered in the CitSci and VGI projects with a geospatial aspect, such as platform, tool and interface design, ontology development, spatial analysis and data quality assessment. The book also points out the needs and future research directions in these subjects, such as; (a) data quality issues especially in the light of big data; (b) ontology studies for geospatial data suited for diverse user backgrounds, data integration, and sharing; (c) development of machine learning and artificial intelligence based online tools for pattern recognition and object identification using existing repositories of CitSci and VGI projects; and (d) open science and open data practices for increasing the efficiency, decreasing the redundancy, and acknowledgement of all stakeholders

The Endothelial Cell Response to Inflammation, the Functional Role of the Endothelial-enriched Protein KANK3 and the Adipose Tissue Transcriptome

A compilation of three complementary projects explores various facets of endothelial cell biology and transcriptomics, illuminating the intricate dynamics underlying cellular responses to specific stimuli across different tissues. The first project examines how endothelial cells react to the inflammatory molecule tumour necrosis factor (TNF), by studying these cells over time after TNF exposure. We identified distinct gene expression patterns and revealed two central temporal phases of gene upregulation in the endothelial response. The induction of interferon response genes, without de novo interferon production, was further investigated. An online resource was developed for comprehensive data exploration (www.endothelial-response.org). The second project analysed adipose tissue to define cell type enriched transcripts and differences between the sexes and depot types. We found mesothelial cells to be the main driver for heterogeneity between subcutaneous and visceral adipose tissue. This data is accessible through the Human Protein Atlas. The third project focuses on KANK3, which was predicted to be an endothelial enriched gene in the previous study, and others from the group. Our findings show that KANK3 is endothelial specific in multiple tissues through the body, inhibition of KANK3 in endothelial cells affects cell motility, expression of blood clotting proteins on gene and protein level, and thrombin generation. Together, these projects enhance our understanding of endothelial cell responses to inflammation and detail the functional investigation of an uncharacterised endothelial protein. Each project offers a different perspective, by examining temporal responses, functional changes, and tissue-wide patterns. This multifaceted approach deepens our insights into cell biology and furthers our understanding of critical health processes

Estimating poverty maps from aggregated mobile communication networks

Governments and other organisations often rely on data collected by household surveys and censuses to provide estimates of household poverty and identify areas in most need of regeneration and development investment. However, due to the high cost associated with manual data collection and processing, many developing countries conduct such surveys very infrequently, if at all, and only at a coarse level of spatial granularity. Consequently, it becomes difficult for governments and NGOs to determine where and when to intervene. This thesis addresses this problem by examining the feasibility of deriving up to date and high resolution proxy measurements of poverty from an alternative source of data, namely, Call Detail Records (CDRs), which can be used by organisations to help in decision making. Specifically, we contribute the following: 1. A detailed spatial analysis of economic wealth in two sub-Saharan countries, Senegal and Cote d’Ivoire from which we derive two baseline poverty esti- ˆ mators grounded on concrete usage scenarios. 2. We establish a link between communication patterns and wealth through a simulation-based analysis of information diffusion. We further examine the influence of contextual factors, including data quality issues and economic volatility, on the strength of this relationship. 3. An approach to building wealth prediction models based on features of aggregated CDRs. Features include static and simulation based measures of information access, activity based metrics and econometric inspired metrics. We further perform a comparative analysis of the results of several models in relation to the baseline predictors. We conclude that it is possible to produce proxy poverty or wealth indicators from aggregated CDRs that provide a good level of accuracy, particularly where geographical coverage of the mobile phone network is sufficient. The final outcome of this thesis is a method for developing aggregated CDR-based poverty or wealth models that can be readily implemented anywhere in which there is a need for more up to date and/or finer resolution poverty estimates

Modularity-based approach for tracking communities in dynamic social networks

Community detection is a crucial task to unravel the intricate dynamics of online social networks. The emergence of these networks has dramatically increased the volume and speed of interactions among users, presenting researchers with unprecedented opportunities to explore and analyze the underlying structure of social communities. Despite a growing interest in tracking the evolution of groups of users in real-world social networks, the predominant focus of community detection efforts has been on communities within static networks. In this paper, we introduce a novel framework for tracking communities over time in a dynamic network, where a series of significant events is identified for each community. Our framework adopts a modularity-based strategy and does not require a predefined threshold, leading to a more accurate and robust tracking of dynamic communities. We validated the efficacy of our framework through extensive experiments on synthetic networks featuring embedded events. The results indicate that our framework can outperform the state-of-the-art methods. Furthermore, we utilized the proposed approach on a Twitter network comprising over 60,000 users and 5 million tweets throughout 2020, showcasing its potential in identifying dynamic communities in real-world scenarios. The proposed framework can be applied to different social networks and provides a valuable tool to gain deeper insights into the evolution of communities in dynamic social networks

Verifying big data topologies by-design: a semi-automated approach

Big data architectures have been gaining momentum in recent years. For instance, Twitter uses stream processing frameworks like Apache Storm to analyse billions of tweets per minute and learn the trending topics. However, architectures that process big data involve many different components interconnected via semantically different connectors. Such complex architectures make possible refactoring of the applications a difficult task for software architects, as applications might be very different with respect to the initial designs. As an aid to designers and developers, we developed OSTIA (Ordinary Static Topology Inference Analysis) that allows detecting the occurrence of common anti-patterns across big data architectures and exploiting software verification techniques on the elicited architectural models. This paper illustrates OSTIA and evaluates its uses and benefits on three industrial-scale case-studies

Computational socioeconomics

Uncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development. The understanding of socioeconomic processes provides foundations to quantify global economic development, to map regional industrial structure, and to infer individual socioeconomic status. In this review, we will make a brief manifesto about a new interdisciplinary research field named Computational Socioeconomics, followed by detailed introduction about data resources, computational tools, data-driven methods, theoretical models and novel applications at multiple resolutions, including the quantification of global economic inequality and complexity, the map of regional industrial structure and urban perception, the estimation of individual socioeconomic status and demographic, and the real-time monitoring of emergent events. This review, together with pioneering works we have highlighted, will draw increasing interdisciplinary attentions and induce a methodological shift in future socioeconomic studies