Extraction and Analysis of Facebook Friendship Relations

Online Social Networks (OSNs) are a unique Web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of Online Social Networks both from the point of view of marketing and other of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (online) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem).\ud However, OSN analysis poses novel challenges both to Computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations, is restricted; thus, we acquired the necessary information directly from the front-end of the Web site, in order to reconstruct a sub-graph representing anonymous interconnections among a significant subset of users. We describe our ad-hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a specific tool for analyzing quantitative and qualitative properties of social networks, adopting and improving existing Social Network Analysis (SNA) techniques and algorithms

Opening up to big data: computer-assisted analysis of textual data in social sciences

"Two developments in computational text analysis may change the way qualitative data analysis in social sciences is performed: 1. the availability of digital text worth to investigate is growing rapidly, and 2. the improvement of algorithmic information extraction approaches, also called text mining, allows for further bridging the gap between qualitative and quantitative text analysis. The key factor hereby is the inclusion of context into computational linguistic models which extends conventional computational content analysis towards the extraction of meaning. To clarify methodological differences of various computer-assisted text analysis approaches the article suggests a typology from the perspective of a qualitative researcher. This typology shows compatibilities between manual qualitative data analysis methods and computational, rather quantitative approaches for large scale mixed method text analysis designs." (author's abstract

Canary:an Interactive and Query-Based Approach to Extract Requirements from Online Forums

Interactions among stakeholders and engineers is key to Requirements engineering (RE). Increasingly, such interactions take place online, producing large quantities of qualitative (natural language) and quantitative (e.g., votes) data. Although a rich source of requirements-related information, extracting such information from online forums can be nontrivial.We propose Canary, a tool-assisted approach, to facilitate systematic extraction of requirements-related information from online forums via high-level queries. Canary (1) adds structure to natural language content on online forums using an annotation schema combining requirements and argumentation ontologies, (2) stores the structured data in a relational database, and (3) compiles high-level queries in Canary syntax to SQL queries that can be run on the relational database.We demonstrate key steps in Canary workflow, including (1) extracting raw data from online forums, (2) applying annotations to the raw data, and (3) compiling and running interesting Canary queries that leverage the social aspect of the data

Urban Knowledge Extraction, Representation and Reasoning as a Bridge from Data City towards Smart City

Urban Data management represents a major challenge in the field of Smart Cities. Its understanding is essential for the development of better smart services, which are a persistent demand in urban policies. From all the sources of data available, those that involve a collective processing of urban information (by the citizens or other collectives) deliver in fact, useful insights into social perception. Such is the case, for example, of data collected from mobile networks. Prior to the design of sociotechnical artifacts in cities, it seems important to extract the qualitative and quantitative opinions, sentiment and feedbacks present in these data. In this paper we present three solutions for mining these contents through Knowledge Extraction methods, as a previous step to the prospection of new smart services.Ministerio de Economía y Competitividad TIN2013-41086-

Qualitative Environmental Health Research: An Analysis of the Literature, 1991-2008

BACKGROUND. Recent articles have advocated for the use of qualitative methods in environmental health research. Qualitative research uses nonnumeric data to understand people's opinions, motives, understanding, and beliefs about events or phenomena. OBJECTIVE. In this analysis of the literature, I report the use of qualitative methods and data in the study of the relationship between environmental exposures and human health. DATA SOURCES. A primary search on ISI Web of Knowledge/Web of Science for peer-reviewed journal articles dated from 1991 through 2008 included the following three terms: qualitative, environ*, and health. Inclusion and exclusion criteria are described. DATA EXTRACTION. Searches resulted in 3,155 records. Data were extracted and findings of articles analyzed to determine where and by whom qualitative environmental health research is conducted and published, the types of methods and analyses used in qualitative studies of environmental health, and the types of information qualitative data contribute to environmental health. DATA SYNTHESIS. Ninety-one articles met inclusion criteria. These articles were published in 58 different journals, with a maximum of eight for a single journal. The results highlight a diversity of disciplines and techniques among researchers who used qualitative methods to study environmental health, with most studies relying on one-on-one interviews. Details of the analyses were absent from a large number of studies. Nearly all of the studies identified increased scientific understanding of lay perceptions of environmental health exposures. DISCUSSION AND CONCLUSIONS. Qualitative data are published in traditionally quantitative environmental health studies to a limited extent. However, this analysis demonstrates the potential of qualitative data to improve understanding of complex exposure pathways, including the influence of social factors on environmental health, and health outcomes.National Institute of Environmental Health Sciences (R25 ES012084, P42ES007381

Understanding the Emotional and Informational Influence on Customer Knowledge Contribution through Quantitative Content Analysis

Customer knowledge contribution is a vital source of business value. Existing studies paid limited attention to emotional influence on knowledge contribution. Drawing upon social support theory, this study attempts to elaborate the influence of emotional support and informational support on knowledge contribution of customers in a firm-hosted online community. Through quantitative content analysis including product feature extraction and sentiment analysis, we analyzed content data from 2318 users. A set of research hypotheses were tested via regression analysis of panel data. We found that informational support (information diagnosticity and source credibility) and emotional support (emotional consistency and emotional difference) significantly affect customer knowledge contribution. This study contributes to knowledge contribution literature by showing the emotional and informational influence, and provides insights for community managers

Expectant parents' views of factors influencing infant feeding decisions in the antenatal period: A systematic review

Objective: To explore the factors that influence expectant parents’ infant feeding decisions in the antenatal period. Design: Mixed method systematic review focussing on participant views data. Data sources: CINAHL, Medline, Embase and PsychInfo databases were interrogated using initial keywords and then refined terms to elicit relevant studies. Reference lists were checked and hand-searching was undertaken for 2 journals (‘Midwifery’ and ‘Social Science and Medicine’) covering a 3 year time period (January 2011–March 2014). Key inclusion criteria: studies reflecting expectant parents’ views of the factors influencing their infant feeding decisions in the antenatal period; Studies in the English language published after 1990, from developed countries and of qualitative, quantitative or mixed method design. Review methods: A narrative interpretive synthesis of the views data from studies of qualitative, quantitative and mixed method design. Data were extracted on study characteristics and parents’ views, using the Social Ecological Model to support data extraction and thematic synthesis. Synthesis was influenced by the Evidence for Policy and Practice Information and Co-Ordinating Centre approach to mixed method reviews. Results: Of the 409 studies identified through search methods, 17 studies met the inclusion criteria for the review. Thematic synthesis identified 9 themes: Bonding/Attachment; Body Image; Self Esteem/Confidence; Female Role Models; Family and Support Network; Lifestyle; Formal Information Sources; Knowledge; and Feeding in front of others/Public. The review identified a significant bias in the data towards negative factors relating to the breastfeeding decision, suggesting that infant feeding was not a choice between two feeding options, but rather a process of weighing reasons for and against breastfeeding. Findings reflected the perception of the maternal role as intrinsic to the expectant mothers’ infant feeding decisions. Cultural perceptions permeated personal, familial and social influences on the decision-making process. Expectant mothers were sensitive to the way professionals attempted to support and inform them about infant feeding choices. Conclusions: By taking a Social Ecological perspective, we were able to explore and demonstrate the multiple influences impacting on expectant parents in the decision-making process. A better understanding of expectant parents’ views and experiences in making infant feeding decisions in the prenatal and antenatal periods will inform public health policy and the coordination of service provision to support infant feeding activities

Assessing reporting of narrative synthesis of quantitative data in public health systematic reviews

No abstract available

Synthesizing diverse evidence: the use of primary qualitative data analysis methods and logic models in public health reviews

Objectives: The nature of public health evidence presents challenges for conventional systematic review processes, with increasing recognition of the need to include a broader range of work including observational studies and qualitative research, yet with methods to combine diverse sources remaining underdeveloped. The objective of this paper is to report the application of a new approach for review of evidence in the public health sphere. The method enables a diverse range of evidence types to be synthesized in order to examine potential relationships between a public health environment and outcomes. Study design: The study drew on previous work by the National Institute for Health and Clinical Excellence on conceptual frameworks. It applied and further extended this work to the synthesis of evidence relating to one particular public health area: the enhancement of employee mental well-being in the workplace. Methods: The approach utilized thematic analysis techniques from primary research, together with conceptual modelling, to explore potential relationships between factors and outcomes. Results: The method enabled a logic framework to be built from a diverse document set that illustrates how elements and associations between elements may impact on the well-being of employees. Conclusions: Whilst recognizing potential criticisms of the approach, it is suggested that logic models can be a useful way of examining the complexity of relationships between factors and outcomes in public health, and of highlighting potential areas for interventions and further research. The use of techniques from primary qualitative research may also be helpful in synthesizing diverse document types. (C) 2010 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved

Subverting Privacy-Preserving GANs: Hiding Secrets in Sanitized Images

Unprecedented data collection and sharing have exacerbated privacy concerns and led to increasing interest in privacy-preserving tools that remove sensitive attributes from images while maintaining useful information for other tasks. Currently, state-of-the-art approaches use privacy-preserving generative adversarial networks (PP-GANs) for this purpose, for instance, to enable reliable facial expression recognition without leaking users' identity. However, PP-GANs do not offer formal proofs of privacy and instead rely on experimentally measuring information leakage using classification accuracy on the sensitive attributes of deep learning (DL)-based discriminators. In this work, we question the rigor of such checks by subverting existing privacy-preserving GANs for facial expression recognition. We show that it is possible to hide the sensitive identification data in the sanitized output images of such PP-GANs for later extraction, which can even allow for reconstruction of the entire input images, while satisfying privacy checks. We demonstrate our approach via a PP-GAN-based architecture and provide qualitative and quantitative evaluations using two public datasets. Our experimental results raise fundamental questions about the need for more rigorous privacy checks of PP-GANs, and we provide insights into the social impact of these