Emergence, Evolution and Scaling of Online Social Networks

This work was partially supported by AFOSR under Grant No. FA9550-10-1-0083, NSF under Grant No. CDI-1026710, NSF of China under Grants Nos. 61473060 and 11275003, and NBRPC under Grant No. 2010CB731403. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

USING AUDIENCE-CENTRIC DESIGN AND COMMUNITY FEEDBACK TO MANAGE COMPLEX PRIVACY SETTINGS

Today, technology is enabling people to share information on an unprecedented scale. Although much of this information is intended to be shared with a large group of people or even the public, some disclosure is intended for smaller audiences—a subset of a larger group. People may want to limit information visibility because the information is private or sensitive, or they may feel others would not be interested in the content. When people want to selectively share to different audiences, many technologies fail to provide usable mechanisms to manage these more complex sharing situations. In many cases, people lack understanding about which audiences are able to see what items of information. Additionally, the effort to manage audiences and control access to information adds some extra physical and cognitive burden. This research suggests two methods to help people better understand and control sharing. The first examines audience-centric design: using mechanisms that integrate with the primary task and allow sharing to multiple audiences to improve understanding of how information flows to multiple groups of people. The second method examines using community feedback to enhance privacy/sharing default settings thereby lessening the user’s configuration burden. This knowledge contributes to existing research by understanding the extent of how users share information to multiple audiences and react to community feedback mechanisms designed to ease configuration burden

A Promising Practice: Using Facebook as a Communication and Social Networking Tool

Individuals with autism often face barriers to social interaction. Residing in a rural environment can compound these difficulties for individuals diagnosed with autism. Some of the reasons include transportation problems and small social networks, in addition to the characteristics of autism. This article discusses a promising practice for supporting the communication and social opportunities for individuals with autism. The authors examined how Facebook supported the social interaction of Jacob, a 28-year old with High Functioning Autism. The findings suggested that, through Facebook, Jacob increased the quantity and quality of social ties he had with others. The authors argue that although online social networking has limitations, with supervision, tools such as Facebook hold potential for developing and increasing social interaction for individuals with High Functioning Autism /Asperger Syndrome

Assessing the Online Social Environment for Surveillance of Obesity Prevalence

Background: Understanding the social environmental around obesity has been limited by available data. One promising approach used to bridge similar gaps elsewhere is to use passively generated digital data. Purpose This article explores the relationship between online social environment via web-based social networks and population obesity prevalence. Methods: We performed a cross-sectional study using linear regression and cross validation to measure the relationship and predictive performance of user interests on the online social network Facebook to obesity prevalence in metros across the United States of America (USA) and neighborhoods within New York City (NYC). The outcomes, proportion of obese and/or overweight population in USA metros and NYC neighborhoods, were obtained via the Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance and NYC EpiQuery systems. Predictors were geographically specific proportion of users with activity-related and sedentary-related interests on Facebook. Results: Higher proportion of the population with activity-related interests on Facebook was associated with a significant 12.0% (95% Confidence Interval (CI) 11.9 to 12.1) lower predicted prevalence of obese and/or overweight people across USA metros and 7.2% (95% CI: 6.8 to 7.7) across NYC neighborhoods. Conversely, greater proportion of the population with interest in television was associated with higher prevalence of obese and/or overweight people of 3.9% (95% CI: 3.7 to 4.0) (USA) and 27.5% (95% CI: 27.1 to 27.9, significant) (NYC). For activity-interests and national obesity outcomes, the average root mean square prediction error from 10-fold cross validation was comparable to the average root mean square error of a model developed using the entire data set. Conclusions: Activity-related interests across the USA and sedentary-related interests across NYC were significantly associated with obesity prevalence. Further research is needed to understand how the online social environment relates to health outcomes and how it can be used to identify or target interventions

The structure of online activism

Despite the tremendous amount of attention that has been paid to the internet as a tool for civic engagement, we still have little idea how “active” is the average online activist or how social networks matter in facilitating electronic protest. In this paper, we use complete records on the donation and recruitment activity of 1.2 million members of the Save Darfur “Cause” on Facebook to provide a detailed first look at a massive online social movement. While both donation and recruitment behavior are socially patterned, the vast majority of Cause members recruited no one else into the Cause and contributed no money to it-suggesting that in the case of the Save Darfur campaign, Facebook conjured an illusion of activism rather than facilitating the real thing

Adapting Behavioral Interventions for Social Media Delivery

Patients are increasingly using online social networks (ie, social media) to connect with other patients and health care professionals--a trend called peer-to-peer health care. Because online social networks provide a means for health care professionals to communicate with patients, and for patients to communicate with each other, an opportunity exists to use social media as a modality to deliver behavioral interventions. Social media-delivered behavioral interventions have the potential to reduce the expense of behavioral interventions by eliminating visits, as well as increase our access to patients by becoming embedded in their social media feeds. Trials of online social network-delivered behavioral interventions have shown promise, but much is unknown about intervention development and methodology. In this paper, we discuss the process by which investigators can translate behavioral interventions for social media delivery. We present a model that describes the steps and decision points in this process, including the necessary training and reporting requirements. We also discuss issues pertinent to social media-delivered interventions, including cost, scalability, and privacy. Finally, we identify areas of research that are needed to optimize this emerging behavioral intervention modality

What makes people talk about antibiotics on social media? A retrospective analysis of Twitter use.

OBJECTIVES: Social media has reshaped individual and institutional communication. The unrestricted access to spontaneous views and opinions of society can enrich the evaluation of healthcare interventions. Antimicrobial resistance has been identified as a global threat to health requiring collaboration between clinicians and healthcare users. We sought to explore events and individuals influencing the discourse about antibiotics on Twitter. METHODS: A web-based tool (www.topsy.com) was used to detect daily occurrences of the word 'antibiotic' from 24 September 2012 to 23 September 2013 in worldwide Tweets. Activity peaks (message frequency over three times that of baseline) were analysed to identify events leading to the increase. RESULTS: Of 135 billion messages posted during the study period, 243000 (0.000002%) referred to 'antibiotic'. The greatest activity increases appeared after: (i) the UK Chief Medical Officer's (CMO's) declaration of antimicrobial resistance as a national risk (January 2013 and March 2013); (ii) the release of the US CDC's report on antimicrobial resistance (September 2013); and (iii) the US FDA announcement on azithromycin safety concerns (March 2013). The CMO report in March reached an estimated worldwide audience of 20 million users in a single day. However, the frequency of antibiotic Tweets returned to basal levels within 48 h of all four peaks in activity. CONCLUSIONS: Institutional events can rapidly amplify antibiotic discussions on social media, but their short lifespan may hinder their public impact. Multipronged strategies may be required to prolong responses. Developing methods to refine social media monitoring to evaluate the impact and sustainability of societal engagement in the antimicrobial resistance agenda remains essential

Vulnerabilities to Online Social Network Identity Deception Detection Research and Recommendations for Mitigation

Identity deception in online social networks is a pervasive problem. Ongoing research is developing methods for identity deception detection. However, the real-world efficacy of these methods is currently unknown because they have been evaluated largely through laboratory experiments. We present a review of representative state-of-the-art results on identity deception detection. Based on this analysis, we identify common methodological weaknesses for these approaches, and we propose recommendations that can increase their effectiveness for when they are applied in real-world environments

Are black friday deals worth it? Mining twitter users' sentiment and behavior response

The Black Friday event has become a global opportunity for marketing and companies’ strategies aimed at increasing sales. The present study aims to understand consumer behavior through the analysis of user-generated content (UGC) on social media with respect to the Black Friday 2018 offers published by the 23 largest technology companies in Spain. To this end, we analyzed Twitter-based UGC about companies’ offers using a three-step data text mining process. First, a Latent Dirichlet Allocation Model (LDA) was used to divide the sample into topics related to Black Friday. In the next step, sentiment analysis (SA) using Python was carried out to determine the feelings towards the identified topics and offers published by the companies on Twitter. Thirdly and finally, a data-text mining process called textual analysis (TA) was performed to identify insights that could help companies to improve their promotion and marketing strategies as well as to better understand the customer behavior on social media. The results show that consumers had positive perceptions of such topics as exclusive promotions (EP) and smartphones (SM); by contrast, topics such as fraud (FA), insults and noise (IN), and customer support (CS) were negatively perceived by customers. Based on these results, we offer guidelines to practitioners to improve their social media communication. Our results also have theoretical implications that can promote further research in this area