Seasonal variation in collective mood via Twitter content and medical purchases

The analysis of sentiment contained in vast amounts of Twitter messages has reliably shown seasonal patterns of variation in multiple studies, a finding that can have great importance in the understanding of seasonal affective disorders, particularly if related with known seasonal variations in certain hormones. An important question, however, is that of directly linking the signals coming from Twitter with other sources of evidence about average mood changes. Specifically we compare Twitter signals relative to anxiety, sadness, anger, and fatigue with purchase of items related to anxiety, stress and fatigue at a major UK Health and Beauty retailer. Results show that all of these signals are highly correlated and strongly seasonal, being under-expressed in the summer and over-expressed in the other seasons, with interesting differences and similarities across them. Anxiety signals, extracted from both Twitter and from Health product purchases, peak in spring and autumn, and correlate also with the purchase of stress remedies, while Twitter sadness has a peak in the Winter, along with Twitter anger and remedies for fatigue. Surprisingly, purchase of remedies for fatigue do not match the Twitter fatigue, suggesting that perhaps the names we give to these indicators are only approximate indications of what they actually measure. This study contributes both to the clarification of the mood signals contained in social media, and more generally to our understanding of seasonal cycles in collective mood

Measuring objective and subjective well-being: dimensions and data sources

AbstractWell-being is an important value for people's lives, and it could be considered as an index of societal progress. Researchers have suggested two main approaches for the overall measurement of well-being, the objective and the subjective well-being. Both approaches, as well as their relevant dimensions, have been traditionally captured with surveys. During the last decades, new data sources have been suggested as an alternative or complement to traditional data. This paper aims to present the theoretical background of well-being, by distinguishing between objective and subjective approaches, their relevant dimensions, the new data sources used for their measurement and relevant studies. We also intend to shed light on still barely unexplored dimensions and data sources that could potentially contribute as a key for public policing and social development

Diurnal patterns in Twitter sentiment in Italy and United Kingdom are correlated

Diurnal variations in indicators of emotion have been reliably observed in Twitter content, but confirmation of their circadian nature has not been possible due to the many confounding factors present in the data. We report on correlations between those indicators in Twitter content obtained from 9 cities of Italy and 54 cities in the United Kingdom, sampled hourly at the time of the 2020 national lockdowns. This experimental setting aims at minimizing synchronization effects related to television, eating habits, or other cultural factors. This correlation supports a circadian origin for these diurnal variations, although it does not exclude the possibility that similar zeitgebers exist in both countries including during lockdowns

A treatise on Web 2.0 with a case study from the financial markets

There has been much hype in vocational and academic circles surrounding the emergence of web 2.0 or social media; however, relatively little work was dedicated to substantiating the actual concept of web 2.0. Many have dismissed it as not deserving of this new title, since the term web 2.0 assumes a certain interpretation of web history, including enough progress in certain direction to trigger a succession [i.e. web 1.0 → web 2.0]. Others provided arguments in support of this development, and there has been a considerable amount of enthusiasm in the literature. Much research has been busy evaluating current use of web 2.0, and analysis of the user generated content, but an objective and thorough assessment of what web 2.0 really stands for has been to a large extent overlooked. More recently the idea of collective intelligence facilitated via web 2.0, and its potential applications have raised interest with researchers, yet a more unified approach and work in the area of collective intelligence is needed. This thesis identifies and critically evaluates a wider context for the web 2.0 environment, and what caused it to emerge; providing a rich literature review on the topic, a review of existing taxonomies, a quantitative and qualitative evaluation of the concept itself, an investigation of the collective intelligence potential that emerges from application usage. Finally, a framework for harnessing collective intelligence in a more systematic manner is proposed. In addition to the presented results, novel methodologies are also introduced throughout this work. In order to provide interesting insight but also to illustrate analysis, a case study of the recent financial crisis is considered. Some interesting results relating to the crisis are revealed within user generated content data, and relevant issues are discussed where appropriate

Public attitudes towards sharing loyalty card data for academic health research: a qualitative study

Background: A growing number of studies show the potential of loyalty card data for use in health research. However , research into public perceptions of using this data is limited. This study aimed to investigate public attitudes towards donating loyalty card data for academic health research, and the safeguards the public would want to see implemented. The way in which participant attitudes varied according to whether loyalty card data would be used for either cancer or COVID-19 research was also examined. Methods: Participants (N = 40) were recruited via Prolific Academic to take part in semi-structured telephone interviews , with questions focused on data sharing related to either COVID-19 or ovarian/bowel cancer as the proposed health condition to be researched. Content analysis was used to identify sub-themes corresponding to the two a priori themes, attitudes and safeguards. Results: Participant attitudes were found to fall into two categories, either rational or emotional. Under rational, most participants were in favour of sharing loyalty card data. Support of health research was seen as an important reason to donate such data, with loyalty card logs being considered as already within the public domain. With increased understanding of research purpose, participants expressed higher willingness to donate data. Within the emotional category, participants shared fears about revealing location information and of third parties obtaining their data. With regards to safeguards, participants described the importance of anonymisation and the level of data detail; the control, convenience and choice they desired in sharing data; and the need for transparency and data security. The change in hypothetical purpose of the data sharing, from Covid-19 to cancer research, had no impact on participants' decision to donate, although did affect their understanding of how loyalty card data could be used. Conclusions: Based on interviews with the public, this study contributes recommendations for those researchers and the wider policy community seeking to obtain loyalty card data for health research. Whilst participants were largely in favour of donating loyalty card data for academic health research, information, choice and appropriate safeguards are all exposed as prerequisites upon which decisions are made

Data-driven Computational Social Science: A Survey

Social science concerns issues on individuals, relationships, and the whole society. The complexity of research topics in social science makes it the amalgamation of multiple disciplines, such as economics, political science, and sociology, etc. For centuries, scientists have conducted many studies to understand the mechanisms of the society. However, due to the limitations of traditional research methods, there exist many critical social issues to be explored. To solve those issues, computational social science emerges due to the rapid advancements of computation technologies and the profound studies on social science. With the aids of the advanced research techniques, various kinds of data from diverse areas can be acquired nowadays, and they can help us look into social problems with a new eye. As a result, utilizing various data to reveal issues derived from computational social science area has attracted more and more attentions. In this paper, to the best of our knowledge, we present a survey on data-driven computational social science for the first time which primarily focuses on reviewing application domains involving human dynamics. The state-of-the-art research on human dynamics is reviewed from three aspects: individuals, relationships, and collectives. Specifically, the research methodologies used to address research challenges in aforementioned application domains are summarized. In addition, some important open challenges with respect to both emerging research topics and research methods are discussed.Comment: 28 pages, 8 figure

Mapping collective behavior--beware of looping

We discuss ambiguities of the two main dimensions of the map proposed by Bentley and colleagues that relate to the degree of self-reflection the observed agents have upon their behavior. This self-reflection is a variant of the "looping effect" which denotes that, in social research, the product of investigation influences the object of investigation. We outline how this can be understood as a dimension of "height" in the map of Bentley et a

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

Culture and Social Media

博士(文学)神戸市外国語大

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse