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Session 3E [clear filter]
Tuesday, July 12


Session 3E: Health & Wellness
Location: PSH (Professor Stuart Hall Building) - 326, 
Goldsmiths, University of London, Building 2
Campus Map 

Tuesday July 12, 2016 14:45 - 16:15
PSH (Professor Stuart Hall Building) - 326 Goldsmiths University, Building 2


(Re-)Appropriating Instagram for Social Research on Obesity
Location: PSH (Professor Stuart Hall Building) - 326, 
Goldsmiths, University of London, Building 2
Campus Map 

  • Anders Kristian Munk, Aalborg University Copenhagen, Denmark
  • Mette Simonsen Abildgaard, Aalborg University Copenhagen, Denmark
  • Morten Krogh Petersen, Aalborg University Copenhagen, Denmark
  • Andreas Birkbak, Aalborg University Copenhagen, Denmark

This paper presents three ways of appropriating Instagram for social research through the case of obesity. We draw on the notion obesogenic environment, in which obesity is understood as related to a wide range of cultural, social and physical factors. In a data sprint, digital methods and obesity researchers together explored a dataset of 82,449 instagrams tagged with location from the 5 most and the 5 least overweight local authorities in the UK. These geo-located instagrams from low and high-BMI areas were studied in three distinct approaches to the data; each drawing on interrelated conceptualizations about what is the obesogenic environment, Instagram and cultural analysis. The first appropriation values Instagram as a repository of images that can be coded and counted, while the second asks about the everyday practices of Instagram users. In a third appropriation, we view Instagram as an analytical tool in itself that produces a media-specific version of phenomena such as obesity. Following this third appropriation, we conclude that Instagram, to unfold its potential for social research, must be considered as more than a collection of user-tagged images, but as an analytical context in its own right. 

Tuesday July 12, 2016 14:46 - 16:15
PSH (Professor Stuart Hall Building) - 326 Goldsmiths University, Building 2


Feasibility Study of Social Media for Public Health Behaviour Changes
Location: PSH (Professor Stuart Hall Building) - 326, 
Goldsmiths, University of London, Building 2
Campus Map 

  • Oluwaseun Ajao, Queen's University Belfast, United Kingdom
  • Anna Jurek, Queen's University, United Kingdom
  • Aisling Gough,  Queen's University Belfast, United Kingdom
  • Ruth Hunter,  Queen's University Belfast, United Kingdom
  • Eimear Barrett,  Queen's University Belfast, United Kingdom
  • Gary Mckeown,  Queen's University Belfast, United Kingdom
  • Jun Hong,  Queen's University Belfast, United Kingdom
  • Frank Kee, Queen's University Belfast, United Kingdom

Social networking sites such as Twitter and Facebook have been shown to function as effective social sensors that can “feel the pulse” of a community. The aim of the current study is to test the feasibility of designing, implementing and evaluating a bespoke social media-enabled intervention that can be effective for sharing and changing knowledge, attitudes and behaviours in meaningful ways to promote public health, specifically with regards to prevention of skin cancer. We present the design and implementation details of the campaign followed by summary findings and analysis.


Research has shown that social networks can mediate the transmission of healthy and unhealthy behaviors in populations; either through selection (Centola, 2010, 2011) or influence (Cha et al, 2010) Social Media (SM) platforms have also been shown to transmit moods, feeling and behaviours (Naveed et al, 2011). There are several studies that have shown the effectiveness of social media in terms of behavioural changes in public health interventions such as in physical activity (Cavallo et al, 2012), sexual health (Bull et al, 2012) and risky sexual behaviours (Jones, Baldwin, & Lewis, 2012). To the best of our knowledge our study is one of the first to use Twitter and Facebook social networking platforms to study public health behaviour while raising awareness about skin cancer and its prevention.


The study aimed to address the following research questions to support the feasibility assessment: (1) Does SM constitute an acceptable means for delivering public health information in the target population? (2) Are people willing to share personal issues (e.g. health behaviours or attitudes) across a SM platform? (3) What type of SM communication would attract the attention of the target population? (4) Are individuals, organizations, celebrities more likely to tweet or re-tweet messages related to the public health campaign? (5) What are the key factors that motivate users to share messages amongst themselves? 


We began by conducting a survey of 752 households to understand SM usage amongst people in Northern Ireland–the study’s target population. We found Facebook and Twitter to be the two most popular platforms as shown in Table 1. To prepare for the two main phases of the intervention we chose hashtags which broadly differentiated skin cancer awareness from skin surveillance messages respectively. The first Phase which ran from the 1st May – 15th July 2015 contained messages with the #SkinSmartNI, #SkinSavvyNI hashtags. The second Phase ran from 1st August - 30th September 2015 and used the hashtag #KnowYourSkinNI. We chose influencers (including radio, TV weather presenters and celebrities such as music artistes) who we hoped would help diffuse our messages. A coordinated SM event promoting the campaign – a Thunderclap – was designed and then delivered on 1st September 2015 with the aim of creating a trending online meme of the various hashtags used. Figure 1 shows the five message types posted - shocking, story, informative, opportunistic and humorous.

To effectively capture the Twitter data we chose to subscribe to a data provider for the provision of 100% access to the Twitter firehose while Facebook data collected from the analytics dashboard was sufficient for this purpose. However, due to privacy concerns, analysis of Facebook data is limited and beyond the scope of this current paper. JSON data was parsed into CSV and an SQL database for analysis.


In summary, the first phase of the study generated 1,404 interactions comprising tweets, retweets and replies from 366 distinct users while the second phase generated 486 interactions from 217 distinct users. 70% of the messages were sent by users based in the UK. We inferred gender for 65% of the users using “twitterreport” R package. For messages on Twitter we measure message performance in terms of impressions (views) and engagements (clicks). In Table 2 we see the most retweeted messages were “informative” and “humorous” for phases 1 and 2 respectively. We also found no significant difference between promoted and non-promoted messages on both platforms. 

Future work

In our ongoing work we examine diffusion of information based on the message topic and the locations of users who propagate the information. Also, we are assessing how the various message types differ in terms of their diffusion. It would be beneficial for assessing SM enabled public health campaigns if finer granularity were obtained using a location inference algorithm (Ajao, Hong, & Weiru, 2015) which may give more location detail on campaign responses at city-level. In addition it would be interesting if future work could accurately infer more demographic characteristics of responders in platforms such as Facebook especially when response volumes were low. These features are crucial in measuring effectiveness of public health interventions.


[1]    Ajao, O., Hong, J. and Liu, W. (2015) A Survey of Location Inference Techniques on Twitter. Journal of Information Science (Big Social Data Special Issue, Dec 2015) Vol. 41(6) 855–864. DOI: 10.1177/0165551515602847

[2]    Bull, S. S., Levine, D. K., Black, S. R., Schmiege, S. J., & Santelli, J. (2012). Social media–delivered sexual health intervention: a cluster randomized controlled trial. American journal of preventive medicine, 43(5), 467-474. DOI: 10.1016/j.amepre.2012.07.022

[3]    Cavallo, D. N., Tate, D. F., Ries, A. V., Brown, J. D., DeVellis, R. F., & Ammerman, A. S. (2012). A social media–based physical activity intervention: a randomized controlled trial. American journal of preventive medicine, 43(5), 527-532. DOI: 10.1016/j.amepre.2012.07.019.

[4]    Centola, D. (2010) The spread of behavior in an online social network experiment. Science. 329:1194-97. DOI: 10.1126/science.1185231.

[5]    Centola, D. (2011) An experimental study of homophily in the adoption of health behavior. Science; 334:1269-72. DOI: 10.1126/science.1207055

[6]    Cha, M., Haddadi, H., Benevenuto, F. & Gummadi, P.K. (2010), "Measuring User Influence in Twitter: The Million Follower Fallacy.", International Conference on Web & Social Media, vol. 10, no. 10-17, 30.

[7]     Jones, K., Baldwin, K. A., & Lewis, P. R. (2012). The potential influence of a social media intervention on risky sexual behavior and Chlamydia incidence. Journal of community health nursing, 29(2), 106-120. DOI: 10.1080/07370016.2012.670579.

[8]    Naveed, N., Gottron, T., Kunegis, J. & Alhadi, A.C. (2011), "Bad news travel fast: A content-based analysis of interestingness on twitter", Proceedings of the 3rd International Web Science Conference ACM, 8. DOI: 10.1145/2527031.2527052

[9]    Vega Yon, G. (2015). “twitterreport”: Out-of-the-Box Analysis and Reporting Tools for Twitter. R package version http://github.com/gvegayon/twitterreport [Accessed: 14th April, 2016]

Tuesday July 12, 2016 14:46 - 16:15
PSH (Professor Stuart Hall Building) - 326 Goldsmiths University, Building 2


Older adults mobilize social support via digital networks: Initial findings from the fourth East York study
Location: PSH (Professor Stuart Hall Building) - 326, 
Goldsmiths, University of London, Building 2
Campus Map 

  • Anabel Quan-Haase, University of Western Ontario, Canada
  • Barry Wellman, University of Toronto, Canada
  • Kim Martin, UWO, Canada
  • Christian Beermann, University of Toronto, Canada
  • Meghan Miller, University of Western Ontario, Canada

Building on three previous studies of East York, we employ detailed qualitative analyses to examine how senior residents of this Toronto area find social support via the internet, their phones, and in-person. Focusing on residents who are 65+, as they comprise nearly half of our sample, we can see that the analytic categories employed in the second East York study remain useful despite advances in digital media. Through this we are able to determine that social support is widely available, with these older adults using the internet, particularly email and Facebook, as well as their phones, and in-person contacts to mobilize their social networks. Many of our participants rely heavily on the assistance of relatives and some on friends for technical support, they continue to learn how to best access their social support via digital networks. 


Many researchers and pundits have claimed that social life has eroded, pointing to different prime causes including industrialization, capitalism, socialism, urbanization, colonialism, and bureaucratization. Recently, some have blamed technology, especially the diffusion of trains, cars, telephones, radios, televisions from diminishing involvement in formally organized groups of parents, veterans, social clubs, and the like (Putnam, 2000), while others have pointed to a supposed lack of authentic connections engendered by digital media (Turkle, 2011; Livingstone, 2008). At the center of this debate is the assumption that ties sustained via computer-mediated communication do not support the mobilization of social support as well as in-person ties (Turkle, 2011; Livingston, 2008). Even if individuals are more connected, it is argued that this increase in ties does not translate into greater networks of social support. Contrary to these claims, our evidence shows that while things are not what they used to be, they have not fallen apart either and social support is exchanged among networks of older Torontonians both on and offline. 


Much work in the area of social capital suggests that resources can indeed flow through social media such as Facebook (Ellison, Steinfield, & Lampe, 2007) and Twitter (Quan-Haase, Martin, & McCay-Peet, 2015). However, much of this work has collected data from university students and young adults, who have grown up with the internet and mobile devices, the so-called "digital natives" (Prensky, 2001). This study by contrast aims to understand how social support is mobilized within the context of older Canadians’ everyday lives by examining what types of social support older residents of East York exchange with their networks, from whom they receive social support, as well as whom they supply with the same, and finally what role social media plays in facilitating or hindering the mobilization of social support in these networks? 


The present study represents the fourth wave of data collection that has taken place in East York since 1968 (Coates, Moyer, & Wellman, 1969; Wellman, 1979; Wellman & Wortley, 1990; Wellman et al. 2006) , taking place from November, 2012 to June 2013. The sample frame consisted of 2,321 residents , of which 304 were randomly contacted and 101 agreed to participate. Of these, 41 respondents ranged from 65 to 93 years of age and have been included in this analysis. Employing these participants we investigated the types of social support exchanged, ranging from companionship and the exchange of small and large services, to emotional and financial aid. 


Residents of East York continue to exchange the same types of social support witnessed in previous waves of data collection ranging from emotional aid, small services, large services, and companionship (Wellman, 1979; Wellman & Wortley, 1989; Wellman & Wortley, 1990; Wellman et al. 2006). In contrast, major financial aid was hardly discussed by participants as a type of social support exchanged. Uniquely, we did find that communication is a type of social support that has not been captured in previous typologies and was central to our study, suggesting that for this population of older residents, communication via mobile phones, email, and social media is a kind of social support that is received and exchanged.
As long as the older residents of East York surveyed possessed the necessary skills and means to utilize information and communications technologies (ICTs), they employed them to further connect with their social networks near and far to mobilize social support, maintain ties, plan face-to-face activities, ask for expertise, or engage in casual conversation. Thus ICTs are adding another layer to the mobilization of social support within personal social networks, and therefore potentially increasing happiness and situational satisfaction.

At the same time, this age group shows great appreciation for face-to-face exchanges and consider communication via email and social media an add-on, instead of a substitute. Here email was the most prominent medium employed for communication, while using Facebook was also common, even if respondents did not actively post their opinions online but followed and interacted with friends and family.

For others it brings frustration and feelings of segregation. These respondents often felt a lack of confidence with technology and their low digital skills block them from taking full advantages of the possibilities afforded by these digital technologies. Thus, the older residents of East York could benefit from further support in learning how to make digital media work for them, for their needs. 

Future Work:

Respondents considered computer-mediated communication (CMC) to be a form of social support, suggesting that increases in digital communication also increase the exchange of overall social support. Future work can further shed light on ICT use by seniors and their potential reliance on both traditional sources of social support as well as their adoption of social media and social networking platforms. Simultaneously, investigations of the overall social network makeup of all networks within the sample using similar methods will enable researchers to suggest methods to enhance digital literacy, change the features of particular media platforms, and understand the motivations that propel usage by the elderly so as to enable their usage of potential affordances. Likewise an investigation of individual views of privacy, both interpersonal and institutional, alongside further study of technology usage within the sample on the whole may uncover peculiarities of the senior population not yet revealed. 


Coates, D. B., Moyer, S., & Wellman, B. (1969). Yorklea study: Symptoms, problems and life events. Canadian Journal of Public Health 60(12), 471-481.

Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook “Friends:” Social capital and college students’ use of online social setwork sites. Journal of Computer- Mediated Communication, 12(4), 1143−1168.

Livingstone, S. (2008). Taking risky opportunities in youthful content creation: Teenagers' use of social networking sites for intimacy, privacy and self-expression. New Media and Society 10(3), 393-411.

Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon 9(5). http://www.marcprensky.com/writing/Prensky - Digital Natives, Digital Immigrants - Part1.pdf

Putnam, R. (2000). Bowling alone: The collapse and revival of american community. New York, NY: Simon and Schuster.

Quan-Haase, A., Martin, K., & McCay-Peet, L. (2015). Networks of digital humanities scholars: The informational and social uses and gratifications of twitter. Big Data & Society 2(1). http://arxiv.org/abs/1507.02994

Turkle, S. (2011). Alone together. New York, NY: Basic Books.
Wellman, B. (1979). The community question: The intimate networks of East Yorkers. American Journal of Sociology 84(5), 1201-1231.

Wellman, B. & Wortley, S. (1990). Different strokes from different folks: Community ties and social support. American Journal of Sociology 96(3), 558-588.

Wellman, B., & Wortley, S. (1989). Brothers’ keepers: Situating kinship relations in broader networks of social support. Sociological Perspectives, 32(3), 273-306. Wellman, B., Hogan, B., Berg, K., Boase, J., Carrasco, J. A., Côté, R., Kayahara, J., Kennedy, T. L. M., & Tran, P. (2006). Connected lives: The project. In P. Purcell (Ed.), Networked neighborhoods: The online community in context (pp. 157-211). Guildford, UK: Springer. 



Tuesday July 12, 2016 14:46 - 16:15
PSH (Professor Stuart Hall Building) - 326 Goldsmiths University, Building 2


Quantifying Self-Reported Adverse Drug Events on Twitter: Signal and Topic Analysis
Location: PSH (Professor Stuart Hall Building) - 326, 
Goldsmiths, University of London, Building 2
Campus Map 

  • Vassilis Plachouras, Thomson Reuters, United Kingdom
  • Jochen Leidner, Thomson Reuters, United Kingdom
  • Andrew Garrow, Thomson Reuters, United Kingdom

A well-functioning ecosystem of drug suppliers includes responsive regulators and pharmaceutical companies when a sold drug exhibits side effects. Existing systems for monitoring adverse drug events such as the Federal Adverse Events Reporting System (FAERS) in the US have shown limited effectiveness due to the lack of incentives on the side of healthcare professionals and patients. While social media present opportunities to mine adverse events in near real-time, there are still important questions to be answered in order to understand their impact on pharmacovigilance. First, it is not known how many social media posts occur per day on platforms like Twitter, i.e. whether there is "enough signal" for a post-market pharmacovigilance program based on Twitter mining. Second, it is not known what other topics are discussed by users in posts mentioning pharmaceutical drugs.

In this paper, we outline how social media can be used as a human sensor for drug use monitoring. We introduce a large-scale, near real-time system for computational pharmacovigilance, and use our system to estimate the order of magnitude of the volume of daily self-reported pharmaceutical drug side effect tweets. The processing pipeline comprises a set of cascaded filters followed by a supervised machine learning classifier. The cascaded filters quickly reduce the volume to a manageable sub-stream, from which a Support Vector Machine (SVM) based classifier identifies adverse events based on a rich set of features taking into account surface-textual properties as well as domain knowledge about drugs, side effects and the Twitter medium. Using a dataset of 10,000 manually annotated tweets, a SVM classifier achieves F1=60.4% and AUC=0.894. 

The yield of the classifier for a drug universe comprising 2,600 keywords is 721 tweets per day. We also investigate what other topics are discussed in the posts mentioning pharmaceutical drugs. We conclude by suggesting an ecosystem where regulators and pharma companies utillize social media to obtain feedback about consequences of pharmaceutical drug use. 

Tuesday July 12, 2016 14:46 - 16:15
PSH (Professor Stuart Hall Building) - 326 Goldsmiths University, Building 2