I'm me, an individual, and one of many. Fascinated by the science behind the rise of social media.
A taste of the science behind social media. Research into the social, data and health implications.
Abstract: The use of social media applications by citizens, public authorities, and humanitarian organisations generates vast quantities of data. Research predominantly focuses on the use of social media and associated analysis tools during the short-term response phase of a disaster. As such, the use of social media analysis tools to harness social media data for preparedness purposes is currently unclear. This research uses a combination of semi-structured interviews with 20 Red Cross Red Crescent and humanitarian actors, an online survey, two workshops and desk-based research to examine the opportunities and barriers faced by humanitarian actors in using social media analysis tools to analyse social media data for disaster preparedness. Whilst social media analysis tools provide humanitarian actors with an opportunity to understand the effectiveness of their preparedness communication on social media, to monitor risks and disasters, and to build community preparedness networks, this study identified a limited use of social media analysis tools by research participants. This (non) use of social media analysis tools was influenced by the interaction between seven categories of barriers relating to the user of the tool or the tool itself: language, culture, value, financial, human resources, technology, and data. In discussing these barriers, the authors highlight the key role that context plays in determining the significance of each barrier on the selection and use of social media analysis tools for preparedness.
Pub.: 24 Nov '16, Pinned: 09 Feb '17
Abstract: Health threats due to infectious diseases used to be a major public health concerns around the globe till mid of twentieth century when effective public health interventions helped in eradicating a number of infectious diseases around the world. Over the past 15 years, there has been a rise in the number of emerging and reemerging infectious diseases being reported such as the Acute Respiratory Syndrome (SARS) in 2002, HINI in 2009, Middle East Respiratory Syndrome (MERS) in 2012, Ebola in 2014, and Zika in 2016. These emerging viral infectious diseases have led to serious public health concerns leading to death and causing fear and anxiety among the public. More importantly, at the moment, the prevention and control of viral infectious diseases is difficult due to a lack of effective vaccines. Thus having real-time reporting tools are paramount to alert relevant public health surveillance systems and authorities about taking the right and necessary actions to control and minimize the potential harmful effects of viral infectious diseases. Social media and Internet-based data can play a major role in real-time reporting to empower active public health surveillance systems for controlling and fighting infectious diseases.
Pub.: 23 Dec '16, Pinned: 09 Feb '17
Abstract: "Social big data" from technologies such as social media, wearable devices, and online searches continue to grow and can be used as tools for HIV research. Although researchers can uncover patterns and insights associated with HIV trends and transmission, the review process is time consuming and resource intensive. Machine learning methods derived from computer science might be used to assist HIV domain experts by learning how to rapidly and accurately identify patterns associated with HIV from a large set of social data.Using an existing social media data set that was associated with HIV and coded by an HIV domain expert, we tested whether 4 commonly used machine learning methods could learn the patterns associated with HIV risk behavior. We used the 10-fold cross-validation method to examine the speed and accuracy of these models in applying that knowledge to detect HIV content in social media data.Logistic regression and random forest resulted in the highest accuracy in detecting HIV-related social data (85.3%), whereas the Ridge Regression Classifier resulted in the lowest accuracy. Logistic regression yielded the fastest processing time (16.98 seconds).Machine learning can enable social big data to become a new and important tool in HIV research, helping to create a new field of "digital HIV epidemiology." If a domain expert can identify patterns in social data associated with HIV risk or HIV transmission, machine learning models could quickly and accurately learn those associations and identify potential HIV patterns in large social data sets.
Pub.: 13 Jan '17, Pinned: 09 Feb '17
Abstract: Publication date: February 2017 Source:Transportation Research Part C: Emerging Technologies, Volume 75 Author(s): Taha H. Rashidi, Alireza Abbasi, Mojtaba Maghrebi, Samiul Hasan, Travis S. Waller In the past few years, the social science literature has shown significance attention to extracting information from social media to track and analyse human movements. In this paper the transportation aspect of social media is investigated and reviewed. A detailed discussion is provided about how social media data from different sources can be used to indirectly and with minimal cost extract travel attributes such as trip purpose, mode of transport, activity duration and destination choice, as well as land use variables such as home, job and school location and socio-demographic attributes including gender, age and income. The evolution of the field of transport and travel behaviour around applications of social media over the last few years is studied. Further, this paper presents results of a qualitative survey from travel demand modelling experts around the world on applicability of social media data for modelling daily travel behaviour. The result of the survey reveals positive view of the experts about usefulness of such data sources.
Pub.: 01 Jan '17, Pinned: 09 Feb '17
Abstract: Adverse events associated with medications are under-reported in postmarketing surveillance systems. A systematic review of published data from 37 studies worldwide (including Canada) found the median under-reporting rate of adverse events to be 94% in spontaneous reporting systems. This scoping review aims to assess the utility of social media and crowd-sourced data to detect and monitor adverse events related to health products including pharmaceuticals, medical devices, biologics and natural health products.Our review conduct will follow the Joanna Briggs Institute scoping review methods manual. Literature searches were conducted in MEDLINE, EMBASE and the Cochrane Library from inception to 13 May 2016. Additional sources included searches of study registries, conference abstracts, dissertations, as well as websites of international regulatory authorities (eg, Food and Drug Administration (FDA), the WHO, European Medicines Agency). Search results will be supplemented by scanning the references of relevant reviews. We will include all publication types including published articles, editorials, websites and book sections that describe use of social media and crowd-sourced data for surveillance of adverse events associated with health products. Two reviewers will perform study selection and data abstraction independently, and discrepancies will be resolved through discussion. Data analysis will involve quantitative (eg, frequencies) and qualitative (eg, content analysis) methods.The summary of results will be sent to Health Canada, who commissioned the review, and other relevant policymakers involved with the Drug Safety and Effectiveness Network. We will compile and circulate a 1-page policy brief and host a 1-day stakeholder meeting to discuss the implications, key messages and finalise the knowledge translation strategy. Findings from this review will ultimately inform the design and development of a data analytics platform for social media and crowd-sourced data for pharmacovigilance in Canada and internationally.Our protocol was registered prospectively with the Open Science Framework (https://osf.io/kv9hu/).
Pub.: 21 Jan '17, Pinned: 09 Feb '17
Abstract: Authors: Emmanuel Mogaji ; Temitope Farinloye ; Stella Aririguzoh Article URL: http://www.tandfonline.com/doi/full/10.1080/23311975.2016.1223389?af=R Citation: Cogent Business & Management Publication Date: 2016-08-29T02:24:26Z Journal: Cogent Business & Management
Pub.: 29 Aug '16, Pinned: 11 Nov '16
Abstract: One of the main challenges of emergency management lies in communicating risks to the public. On some occasions, risk communicators might seek to increase awareness over emerging risks, while on others the aim might be to avoid escalation of public reactions. Social media accounts offer an opportunity to rapidly distribute critical information and in doing so to mitigate the impact of emergencies by influencing public reactions. This article draws on theories of risk and emergency communication in order to consider the impact of Twitter as a tool for communicating risks to the public. We analyse 10,020 Twitter messages posted by the official accounts of UK local government authorities (councils) in the context of two major emergencies: the heavy snow of December 2010 and the riots of August 2011. Twitter was used in a variety of ways to communicate and manage associated risks including messages to provide official updates, encourage protective behaviour, increase awareness and guide public attention to mitigating actions. We discuss the importance of social media as means of increasing confidence in emergency management institutions.
Pub.: 01 Jul '16, Pinned: 11 Nov '16
Abstract: Publication date: September 2016 Source:Journal of Behavioral and Experimental Finance, Volume 11 Author(s): Jaroslav Bukovina A growing body of research and practical applications employ social media data as the proxy for a complex behavior of a society. This paper provides an overview of academic research related to a link between social media and capital markets. The theoretical rationale of this relationship is predominantly defined by behavioral finance. Behavioral finance augments the standard model of efficient markets and considers less rational factors like investors’ sentiment or public mood as influential for asset pricing and capital market volatility. In this context, social media is a novel tool enabling the collection of data about such less rational factors at the level of a society. The paper introduces social media data from a technical and economic point of view. In addition, it contributes to the theoretical construction of the transmission mechanism between social media and capital markets currently missing in the literature. Subsequently, the paper summarizes the main findings in this field and outlines future challenges in this research.
Pub.: 04 Jul '16, Pinned: 11 Nov '16
Abstract: To report the findings from a unique analysis of naturally occurring data regarding self-harm behaviour generated through the global social media site, Twitter.Self-harm behaviours are of global concern for health and social care practice. However, little is known about the experiences of those who harm and the attitudes of the general public towards such behaviours. A deeper, richer and more organic understanding of this is vital to informing global approaches to supporting individuals through treatment and recovery.Exploratory, qualitative design.Three hundred and sixty two Twitter messages were subject to inductive thematic analysis.Five themes were identified: 1) celebrity influence, 2) self-harm is not a joke (with sub-themes of you wouldn't laugh if you loved me and you think it's funny, I think it's cruel), 3) support for and from others, 4) eating disorders and self-harm and 5) videos and personal stories.The findings indicate that self-harm behaviour continues to be largely misunderstood by the general public and is often the source of ridicule which may contribute to delays in accessing treatment. Whilst Twitter may also provide a source of valuable support for those who self-harm, the sense of community, relatedness and understanding generated by such support may contribute to normalising self-harm and perpetuating the behaviours.Our understanding of the complexity of, aetiology and most effective treatment options for self-harm behaviours are still unclear. The findings demonstrate that there is a critical opportunity to conduct further qualitative research to better understand self-harm and to utilise this valuable and internationally relevant data to support the development of effective public education campaigns and personally tailored treatment options. This article is protected by copyright. All rights reserved.
Pub.: 08 Sep '16, Pinned: 11 Nov '16
Abstract: Studies examining representations of college drinking on social media have almost exclusively focused on Facebook. However, recent research suggests college students may be more influenced by peers' alcohol-related posts on Instagram and Snapchat, two image-based platforms popular among this demographic. One potential explanation for this differential influence is that qualitative distinctions in the types of alcohol-related content posted by students on these three platforms may exist. Informed by undergraduate focus groups, this study examined the hypothesis that, of the three platforms, students tend to use Instagram most often for photos glamourizing drinking and Snapchat for incriminating photos of alcohol misuse and negative consequences. Undergraduate research assistants aided investigators in developing hypothetical vignettes and photographic examples of posts both glamorizing and depicting negative consequences associated with college drinking. In an online survey, vignette and photo stimuli were followed by counterbalanced paired comparisons that presented each possible pair of social media platforms. Undergraduates (N=196) selected the platform from each pair on which they would be more likely to see each post. Generalized Bradley-Terry models examined the probabilities of platform selections. As predicted, Instagram was seen as the most probable destination (and Facebook least probable) for photos depicting alcohol use as attractive and glamorous. Conversely, Snapchat was selected as the most probable destination (and Facebook least probable) for items depicting negative consequences associated with heavy drinking. Results suggest researchers aiming to mitigate the potential influences associated with college students' glamorous and consequential alcohol-related photos posted social media posts should shift their focus from Facebook to Instagram and Snapchat.
Pub.: 25 Oct '16, Pinned: 11 Nov '16
Abstract: Social media data can provide valuable information regarding people's behaviors and health outcomes. Previous studies have shown that social media data can be extracted to monitor and predict infectious disease outbreaks. These same approaches can be applied to other fields including physical activity research and forensic science. Social media data have the potential to provide real-time monitoring and prediction of physical activity level in a given region. This tool can be valuable to public health organizations as it can overcome the time lag in the reporting of physical activity epidemiology data faced by traditional research methods (e.g. surveys, observational studies). As a result, this tool could help public health organizations better mobilize and target physical activity interventions. The first part of this paper aims to describe current approaches (e.g. topic modeling, sentiment analysis and social network analysis) that could be used to analyze social media data to provide real-time monitoring of physical activity level. The second aim of this paper was to discuss ways to apply social media analysis to other fields such as forensic sciences and provide recommendations to further social media research.
Pub.: 30 Oct '16, Pinned: 11 Nov '16
Abstract: Waterpipe tobacco is among the most rapidly growing trends in tobacco smoking, the growing use and acceptance of which are taking place at a time when cigarette smoking is regarded in an increasingly negative manner (Martinasek, McDermott, & Martini, 2011). Given the health risks and the misperceptions associated with waterpipe smoking, this study focuses on how waterpipe smoking is portrayed and represented on the social media platform Pinterest. In total, 800 Pinterest pins were content analyzed. The vast majority of the sampled pins were primarily image based. More than half of the pins linked to a website external to Pinterest that often redirected the pinner to a commercial website. Waterpipe-focused pins portrayed waterpipe smoking more in a positive light than in a negative light, and conveyed a sense of pleasure, aesthetic, and relaxation, which tended to trigger more repins, likes, and higher levels of engagement. Overall, the risks of waterpipe smoking were not represented on Pinterest, indicating that the social media portrayal of waterpipe smoking needs improvement and could benefit from a public health perspective.
Pub.: 30 Oct '15, Pinned: 11 Nov '16
Abstract: Corpus-based word frequencies are one of the most important predictors in language processing tasks. Frequencies based on conversational corpora (such as movie subtitles) are shown to better capture the variance in lexical decision tasks compared to traditional corpora. In this study, we show that frequencies computed from social media are currently the best frequency-based estimators of lexical decision reaction times (up to 3.6% increase in explained variance). The results are robust (observed for Twitter- and Facebook-based frequencies on American English and British English datasets) and are still substantial when we control for corpus size.
Pub.: 01 Jul '16, Pinned: 11 Nov '16
Abstract: Social media use by health practitioners helps articulate a subculture-centered approach to public health communication. This article explores public health practitioners’ communication strategies with one subculture: young (18- to 29-year-old) men who have sex with men (MSM). Interviews with staff at a public health clinic reveal the use of a variety of social media systems to engage potential MSM clients (e.g. Facebook, Twitter, Instagram, Grindr, and Google+). Public health communicators’ social media use reflected the esthetic, behavioral, and media preferences of a high-risk target group and the ethical risks associated with publicly viewable communication. Social media became a platform for integrated multi-channel communication that comprised both top-down (i.e. institution centric) and bottom-up (i.e. subculture centric) communication flows that work together in a complementary fashion. Ultimately, subculture-centered social media use in this setting helps minimize cultural barriers between members of a public health subculture and the institutions that provide critical care.
Pub.: 19 Aug '16, Pinned: 21 Sep '16
Abstract: Pinterest research is beginning to emerge, in part due to the importance of visually stimulating photos within hospitality and tourism. Photos are popular with many chefs for marketing, with some actively using Pinterest and other social media. Some of the world’s top celebrity chefs are a growing phenomenon as influential leaders of their personal brand as well as other goods, services, and causes. This research reports on a pilot study of how celebrity chefs have adopted and implemented social media, especially Pinterest, using the Diffusion of Innovations as a theoretical framework. The study found that of the top 48 chefs, all adopted Facebook, closely followed by 47 adopting Twitter; just 17 adopted Pinterest. The chefs’ social media and Pinterest implementation varied widely. This study sheds insights about social media, particularly Pinterest, extends innovation diffusion research, and serves as a base for future research of both celebrity chefs and social media.
Pub.: 04 Jul '16, Pinned: 11 Nov '16
Abstract: Abstract Recent studies examine politicians’ decisions to use social media, as well as the content of the messages that these political actors disseminate on social media platforms. We contribute to this literature by examining how race competitiveness and a candidate’s position in the race relative to her opponent affect their decisions to issue attacks. Through content analysis of nearly 15,000 Facebook posts for tone (positive or negative), we find that while competitive races encourage both candidates to issue more negative posts, candidates in less competitive races embrace attack messages with more or less frequency depending on whether they trail or lead their opponent. We find that social media negativity is much more likely to be a desperation strategy employed by underdog candidates in less competitive races. We also run separate models examining the factors that drive policy and personal attacks. While underdog candidates are more likely to engage in issue attacks, candidates in competitive races are significantly more likely to use Facebook to make personal attacks.AbstractRecent studies examine politicians’ decisions to use social media, as well as the content of the messages that these political actors disseminate on social media platforms. We contribute to this literature by examining how race competitiveness and a candidate’s position in the race relative to her opponent affect their decisions to issue attacks. Through content analysis of nearly 15,000 Facebook posts for tone (positive or negative), we find that while competitive races encourage both candidates to issue more negative posts, candidates in less competitive races embrace attack messages with more or less frequency depending on whether they trail or lead their opponent. We find that social media negativity is much more likely to be a desperation strategy employed by underdog candidates in less competitive races. We also run separate models examining the factors that drive policy and personal attacks. While underdog candidates are more likely to engage in issue attacks, candidates in competitive races are significantly more likely to use Facebook to make personal attacks.
Pub.: 01 Dec '16, Pinned: 11 Nov '16
Abstract: Authors: Marcus Messner ; Vivian Medina-Messner ; Jeanine Guidry Article URL: http://www.tandfonline.com/doi/full/10.1080/17404622.2016.1219042?ai=z4&mi=3fqos0&af=R Citation: Communication Teacher Publication Date: 2016-09-09T11:53:00Z Journal: Communication Teacher
Pub.: 09 Sep '16, Pinned: 21 Sep '16
Abstract: Increasingly, people are using Facebook (FB) to share health information. However, little is known about the type of information sharing and its potential health consequences in the Arabic speaking world. This study attempts to fill this knowledge gap for diabetes mellitus (DM).We conducted a retrospective qualitative FB content analysis using predefined eligibility criteria. The analysis was restricted to diabetes related groups in the Arabic speaking world. The data were collected between June 2010 and December 2015. A total of 55 groups were screened of which seven met the eligibility criteria.We found 6107 posts in Arabic related to DM of which 1551 posts were included for further analysis. There were 458 (30%) FB posts from Egypt with no posts from Somalia, Yemen, Comoros, and Djibouti. The majority of the posts, 863 (56%), were from females. The focus of the posts was on sharing personal experiences (n=423, 27%), raising awareness (n=210, 3.5%), providing spiritual support (n=162, 10.4%), sharing latest research (n=147, 9.5%), and providing education (n=110, 7.1%) on DM. A large number of the posts by people in 40-60 year age group were around finding out diagnosis related information due to limited access to care in their home countries.Patients with DM are increasingly sharing their health information with other FB users. This study will help inform future research with regard to health information sharing and designing appropriate interventions to harness the power of social media in improving public health.
Pub.: 13 Sep '16, Pinned: 21 Sep '16
Abstract: This paper has two objectives. First, it categorizes the Twitter handles tweeted flu related information based on the amount of replies and mentions within the Twitter network. The collected Twitter accounts are categorized as media, health related individuals, organizations, government, individuals with no background with media or medical field, in order to test the relationship between centrality measures of the accounts and their categories. The second objective is to examine the relationship between the importance of the Twitter accounts in the network, centrality measures, and specific characteristics of each account, including the number of tweets and followers as well as the number of accounts followed and liked.
Pub.: 22 Apr '16, Pinned: 11 Nov '16
Abstract: Community-engaged research is defined by the Institute of Medicine as the process of working collaboratively with groups of people affiliated by geographic proximity, special interests, or similar situations with respect to issues affecting their well-being. Traditional face-to-face community-engaged research is limited by geographic location, limited in resources, and/or uses one-way communications. Web 2.0 technologies including social media are novel communication channels for community-engaged research because these tools can reach a broader audience while promoting bidirectional dialogs.This paper reports on a preliminary program evaluation of the use of social media platforms for promoting engagement of researchers and community representatives in dialogs about community-engaged research.For this pilot program evaluation, the Clinical and Translational Science Office for Community Engagement in Research partnered with the Social Media Network at our institution to create a WordPress blog and Twitter account. Both social media platforms were facilitated by a social media manager. We used descriptive analytics for measuring engagement with WordPress and Twitter over an 18-month implementation period during 2014-2016. For the blog, we examined type of user (researcher, community representative, other) and used content analysis to generate the major themes from blog postings. For use of Twitter, we examined selected demographics and impressions among followers.There were 76 blog postings observed from researchers (48/76, 64%), community representatives (23/76, 32%) and funders (5/76, 8%). The predominant themes of the blog content were research awareness and dissemination of community-engaged research (35/76, 46%) and best practices (23/76, 30%). For Twitter, we obtained 411 followers at the end of the 18-month evaluation period, with an increase of 42% (from 280 to 411) over the final 6 months. Followers reported varied geographic location (321/411, 78%, resided in the United States); 99% (407/411) spoke English; and about half (218/411, 53%) were female. Followers produced 132,000 Twitter impressions.Researchers and community stakeholders use social medial platforms for dialogs related to community-engaged research. This preliminary work is novel because we used Web 2.0 social media platforms to engage these stakeholders whereas prior work used face-to-face formats. Future research is needed to explore additional social media platforms; expanded reach to other diverse stakeholders including patients, providers, and payers; and additional outcomes related to engagement.
Pub.: 11 Sep '16, Pinned: 21 Sep '16
Abstract: Pinterest is now the fourth most popular social network site after Facebook, Twitter, and LinkedIn in the United States, offering its own suite of functions. This study investigated why individuals use specific features of Pinterest such as pinning, creating, liking, following, commenting, inviting, sharing, checking, searching, and browsing different categories. An online survey (N = 113) revealed that a brand new set of gratifications (specific to digital media) predicted a large number of user behaviors in Pinterest. The results showcased the predictive value of affordance-based gratifications in shaping specific user behaviors on social-media.
Pub.: 06 Sep '16, Pinned: 21 Sep '16
Abstract: Mining topical experts on social media is a problem that has gained significant attention due to its wide-ranging applications. Here we present the first study that combines data from four major social networks -- Twitter, Facebook, Google+ and LinkedIn, along with the Wikipedia graph and internet webpage text and metadata, to rank topical experts across the global population of users. We perform an in-depth analysis of 37 features derived from various data sources such as message text, user lists, webpages, social graphs and wikipedia. This large-scale study includes more than 12 billion messages over a 90-day sliding window and 58 billion social graph edges. Comparison reveals that features derived from Twitter Lists, Wikipedia, internet webpages and Twitter Followers are especially good indicators of expertise. We train an expertise ranking model using these features on a large ground truth dataset containing almost 90,000 labels. This model is applied within a production system that ranks over 650 million experts in more than 9,000 topical domains on a daily basis. We provide results and examples on the effectiveness of our expert ranking system, along with empirical validation. Finally, we make the topical expertise data available through open REST APIs for wider use.
Pub.: 31 Aug '16, Pinned: 21 Sep '16
Abstract: Do foundations effectively use social media to engage stakeholders? Do usage and engagement vary by foundation type? This article has been written to stimulate discussion and research about social media use and user engagement by foundations beyond measuring social media presence. We analyzed Facebook usage and stakeholder engagement for three types of foundations: community, corporate, and independent grant-making foundations. We found that although community foundations are more likely to have a social media presence, corporate and independent foundations are more likely to use Facebook and to effectively engage stakeholders. Findings illuminate the need to understand social media usage and engagement in addition to presence. We discuss potential benefits of social media use and provide practical communication management recommendations for nonprofit practitioners.
Pub.: 19 Sep '16, Pinned: 21 Sep '16
Abstract: Facebook, the most widely used social media platform, has been adopted by public health organisations for health promotion and behaviour change campaigns and activities. However, limited information is available on the most effective and efficient use of Facebook for this purpose. This study sought to identify the features of Facebook posts that are associated with higher user engagement on Australian public health organisations' Facebook pages. We selected 20 eligible pages through a systematic search and coded 360-days of posts for each page. Posts were coded by: post type (e.g., photo, text only etc.), communication technique employed (e.g. testimonial, informative etc.) and use of marketing elements (e.g., branding, use of mascots). A series of negative binomial regressions were used to assess associations between post characteristics and user engagement as measured by the number of likes, shares and comments. Our results showed that video posts attracted the greatest amount of user engagement, although an analysis of a subset of the data suggested this may be a reflection of the Facebook algorithm, which governs what is and is not shown in user newsfeeds and appear to preference videos over other post types. Posts that featured a positive emotional appeal or provided factual information attracted higher levels of user engagement, while conventional marketing elements, such as sponsorships and the use of persons of authority, generally discouraged user engagement, with the exception of posts that included a celebrity or sportsperson. Our results give insight into post content that maximises user engagement and begins to fill the knowledge gap on effective use of Facebook by public health organisations.
Pub.: 16 Sep '16, Pinned: 21 Sep '16
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