From Causality to Emergence: re-evaluating social media’s role in the 2011 English riots

This paper is an attempt to re-evaluate the role of social media on the riots. It draws upon interviews and qualitative analysis of tweets posted during the riots to examine how digital modalities reconfigure power relations between vulnerable and invulnerable populations as collectives seek to enact social change. The importance of social media for understanding collective action, I argue, lies in its relevance for conveying what one could call the performativity of public space. My thesis emerges in response to the rise of big data analytics as a means to predict and respond to political unrest, exploring the limits of predictive analyses with regard to issues of trust, power, memory and emotions. My claim is that understanding the power of the digital requires a more sophisticated understanding of emotions. To this end, I emphasize the need to employ multi-method approaches to study new forms of “mediated crowd” membership that combine digital methods with more traditional approaches to emotions research

The power of prediction with social media

Social media provide an impressive amount of data about users and their interactions, thereby offering computer and social scientists, economists, and statisticians – among others – new opportunities for research. Arguably, one of the most interesting lines of work is that of predicting future events and developments from social media data. However, current work is fragmented and lacks of widely accepted evaluation approaches. Moreover, since the first techniques emerged rather recently, little is known about their overall potential, limitations and general applicability to different domains. Therefore, better understanding the predictive power and limitations of social media is of utmost importanc

Does Social Media Sentiment Predict Bitcoin Trading Volume?

Social media sentiment is proven to be an important feature in financial forecasting. While the effect of sentiment is complex and time-varying for traditional financial assets, its role in cryptocurrency markets is unclear. This research explores the predictive power of public sentiment on Bitcoin trading volume. We develop a novel sentiment analysis pipeline for processing Bitcoin-related tweets and achieve state-of-the-art accuracy on a benchmark dataset. Our pipeline also leverages information gain theory to incorporate the impact of textual and non-textual features. We use such features to discern a non-linear relationship between public sentiment and Bitcoin trading volume and discover the optimal predictive horizon for Bitcoin. This research provides a useful module and a foundation for future studies and understanding of Bitcoin market dynamics, and its interaction with social media buzzing

Resident Sentiment: Preliminary Conceptualization and Measurement

Understanding how residents view and react to tourism development is an important topic in tourism literature. To date, most studies focused on the formation and change of locals’ attitudes, whose predictive power to behaviors remains controversial. This study proposes “resident sentiment” as a more encompassing concept to describe local residents’ overall views of tourism development, with attitude as a constituent part. Further, the research team suggests two levels of sentiment: individual sentiment being an internal disposition shaped mainly by private encounters, and public sentiment being shared feelings and reactions resulted from dynamic, multilateral interactions among people. Guided by social exchange and social representations theories, personal experience, social interactions, and destination characteristics are proposed as potential sources of individual sentiment, and mass and social media as a proxy of a community’s public sentiment. A model is proposed to illustrate the determinants and consequences of resident sentiment and interrelationships among key variables

Rethinking Privacy and Freedom of Expression in the Digital Era: An Interview with Mark Andrejevic

Mark Andrejevic, Professor of Media Studies at the Pomona College in Claremont, California, is a distinguished critical theorist exploring issues around surveillance from pop culture to the logic of automated, predictive surveillance practices. In an interview with WPCC issue co-editor Pinelopi Troullinou, Andrejevic responds to pressing questions emanating from the surveillant society looking to shift the conversation to concepts of data holders’ accountability. He insists on the need to retain awareness of power relations in a data driven society highlighting the emerging challenge, ‘to provide ways of understanding the long and short term consequences of data driven social sorting’. Within the context of Snowden’s revelations and policy responses worldwide he recommends a shift of focus from discourses surrounding ‘pre-emption’ to those of ‘prevention’ also questioning the notion that citizens might only need to be concerned, ‘if we are doing something “wrong”’ as this is dependent on a utopian notion of the state and commercial processes, ‘that have been purged of any forms of discrimination’. He warns of multiple concerns of misuse of data in a context where ‘a total surveillance society looks all but inevitable’. However, the academy may be in a unique position to provide ways of reframing the terms of discussions over privacy and surveillance via the analysis of ‘the long and short term consequences of data driven social sorting (and its automation)’ and in particular of algorithmic accountability

What about mood swings? Identifying depression on Twitter with temporal measures of emotions

Depression is among the most commonly diagnosed mental disorders around the world. With the increasing popularity of online social network platforms and the advances in data science, more research efforts have been spent on understanding mental disorders through social media by analysing linguistic style, sentiment, online social networks and other activity traces. However, the role of basic emotions and their changes over time, have not yet been fully explored in extant work. In this paper, we proposed a novel approach for identifying users with or at risk of depression by incorporating measures of eight basic emotions as features from Twitter posts over time, including a temporal analysis of these features. The results showed that emotion-related expressions can reveal insights of individuals’ psychological states and emotions measured from such expressions show predictive power of identifying depression on Twitter. We also demonstrated that the changes in an individual’s emotions as measured over time bear additional information and can further improve the effectiveness of emotions as features, hence, improve the performance of our proposed model in this task

Learning Personalized Privacy Preference From Public Data

Understanding consumers’ privacy preferences is crucial for firms and policymakers to establish trust and encourage innovation and competition. With the widespread use of digital technologies, individuals generate and share vast amounts of data about themselves in the public domain. Even without knowing a person’s private information, the psychosocial traits revealed in public data can provide valuable insights into their privacy preferences. In this study, we aim to predict personalized privacy preferences using social media posts. Our prediction model shows that psychosocial traits such as personality, lifestyles, risk preference, economic thinking, emotions, etc extracted from posts provide significantly greater predictive power than demographic characteristics. Furthermore, we demonstrate the practical value and impact of our model for business and society through a simulation analysis. Our tool can help platforms and policymakers estimate the impact of privacy policies and prevent potential harms such as discrimination

Streetscore -- Predicting the Perceived Safety of One Million Streetscapes

Social science literature has shown a strong connection between the visual appearance of a city's neighborhoods and the behavior and health of its citizens. Yet, this research is limited by the lack of methods that can be used to quantify the appearance of streetscapes across cities or at high enough spatial resolutions. In this paper, we describe 'Streetscore', a scene understanding algorithm that predicts the perceived safety of a streetscape, using training data from an online survey with contributions from more than 7000 participants. We first study the predictive power of commonly used image features using support vector regression, finding that Geometric Texton and Color Histograms along with GIST are the best performers when it comes to predict the perceived safety of a streetscape. Using Streetscore, we create high resolution maps of perceived safety for 21 cities in the Northeast and Midwest of the United States at a resolution of 200 images/square mile, scoring ~1 million images from Google Streetview. These datasets should be useful for urban planners, economists and social scientists looking to explain the social and economic consequences of urban perception.MIT Media Lab ConsortiumGoogle (Firm). Living Labs Tides Foundatio

Ethical Implications of Predictive Risk Intelligence

open access articleThis paper presents a case study on the ethical issues that relate to the use of Smart Information Systems (SIS) in predictive risk intelligence. The case study is based on a company that is using SIS to provide predictive risk intelligence in supply chain management (SCM), insurance, finance and sustainability. The pa-per covers an assessment of how the company recognises ethical concerns related to SIS and the ways it deals with them. Data was collected through a document review and two in-depth semi-structured interviews. Results from the case study indicate that the main ethical concerns with the use of SIS in predictive risk intelli-gence include protection of the data being used in predicting risk, data privacy and consent from those whose data has been collected from data providers such as so-cial media sites. Also, there are issues relating to the transparency and accountabil-ity of processes used in predictive intelligence. The interviews highlighted the issue of bias in using the SIS for making predictions for specific target clients. The last ethical issue was related to trust and accuracy of the predictions of the SIS. In re-sponse to these issues, the company has put in place different mechanisms to ensure responsible innovation through what it calls Responsible Data Science. Under Re-sponsible Data Science, the identified ethical issues are addressed by following a code of ethics, engaging with stakeholders and ethics committees. This paper is important because it provides lessons for the responsible implementation of SIS in industry, particularly for start-ups. The paper acknowledges ethical issues with the use of SIS in predictive risk intelligence and suggests that ethics should be a central consideration for companies and individuals developing SIS to create meaningful positive change for society