smsociety15

Authors:  Linnea Laestadius, Megan Meyer and Young Cho

Recent research has made it clear that people are sharing information about their tobacco use on social media (Bromberg, Augustson, & Backinger, 2012; Luo, Zheng, Zeng, & Leischow, 2014; Seidenberg, Rodgers, Rees, & Connolly, 2012). The growth of mobile applications for smart phones has further enabled the sharing of tobacco use opinions and experiences in real time. Since perceptions of peer substance use rates and norms play an important role in tobacco use patterns (Bertholet, et al, 2013), the self-disclosure of, and the exposure to, smoking on social media inherently poses a challenge to tobacco use control campaigns. The ability to interact over social media services may also foster communities built around substance use (Bromberg et al., 2012). Further complicating the social media landscape, corporations have also taken note of the rising popularity of social media services (Ciolli, 2007; Luo et al., 2014). 

Objective: 
We offer a descriptive study of the electronic cigarette (“e-cigarette”) content found on the visual mobile social media platform Instagram in order to highlight: 1) the public health challenge created by this content and 2) the opportunity this content provides for researchers to understand substance use behaviours. For the purposes of this work, e-cigarettes include cigalikes, ego vape-pens, and mod vape devices. We focus on these devices in particular due to their lack of government regulation, rapidly growing popularity, and the role the internet has played in this growth (Emery, Vera, Huang, & Szczypka, 2014). 

Methods: 
We conducted a qualitative content analysis to understand the types of user- and business-generated content related to e-cigarettes on Instagram. First, an overarching count of e-cigarette hashtags was performed on Instagram to determine growth in the volume of this content. Once popular hashtags were identified, “ecig” and the most popular affiliated hashtag “vape” were chosen for sampling. The 60 most recent posts with each hashtag posted on October 17, 2014 were captured for analysis. After excluding images that were removed or made private by January 2015, the remaining posts were coded in terms of user and post type, key themes, and community/identity related hashtags. 

Results: 
Between March 12, 2014 and March 12, 2015, #ecig posts increased from 291,284 to 824,857, while #vape posts increased from 827,445 to 2,859,946. Initial analysis of our sample (n=85) indicated that content was dominated by small e-cigarette brands, vendors, and representatives (n=50). These businesses made strategic use of Instagram to promote their products. Among non-business users (n=33), posts frequently displayed e-cigarette devices and e-juice. Many posts related to technologically advanced vaping concepts (e.g. dripping, sub-ohming) which were previously undocumented in the public health literature. Business and personal users also frequently (n=69) made use of a number of community- and identity-themed hashtags related to e-cigarettes (e.g. #vapelife, #cloudchaser). Additionally, many users utilized hashtags to emphasize the status and significance of the e-cigarette devices themselves, with #VapePorn being particularly common. 

Future Work: 
Further analysis of the data is planned, with a focus on the implications of social media-based communities forming around e-cigarette use. 

References: 
Bertholet, N., Faouzi, M., Studer, J., Daeppen, J.-B., & Gmel, G. (2013). Perception of tobacco, cannabis, and alcohol use of others is associated with one’s own use. Addiction Science & Clinical Practice, 8(1), 15. 
Bromberg, J. E., Augustson, E. M., & Backinger, C. L. (2012). Portrayal of Smokeless Tobacco in YouTube Videos. Nicotine & Tobacco Research : Official Journal of the Society for Research on Nicotine and Tobacco, 14(4), 455–462. doi:10.1093/ntr/ntr235 
Ciolli, A. (2007). Joe Camel meets YouTube: Cigarette advertising regulations and user-generated marketing. U. Tol. L. Rev., 39, 121. 
Emery, S. L., Vera, L., Huang, J., & Szczypka, G. (2014). Wanna know about vaping? Patterns of message exposure, seeking and sharing information about e-cigarettes across media platforms. Tobacco Control, 23 Suppl 3, iii17–25. doi:10.1136/tobaccocontrol-2014-051648 
Luo, C., Zheng, X., Zeng, D., & Leischow, S. (2014). Portrayal of electronic cigarettes on YouTube. BMC Public Health, 14(1), 1028. doi:10.1136/bmj.e8412 
Seidenberg, A. B., Rodgers, E. J., Rees, V. W., & Connolly, G. N. (2012). Youth access, creation, and content of smokeless tobacco (“dip”) videos in social media. The Journal of Adolescent Health : Official Publication of the Society for Adolescent Medicine, 50(4), 334–338. doi:10.1016/j.jadohealth.2011.09.003 

Author: Shaohai Jiang

Recent years has witnessed an increasing tendency of social media use for health-related activities. However, research on such usage is still poor. So far, there has been little discussion about what types of factors might influence social media use for health. This study described users and nonusers of social media for health activities in terms of their sociodemographic, technological and health-related variables and tested whether these factors could also serve as predictors of using social media for health activities. The study consisted of a telephone survey of 949 adults who reported experience of using the Internet for health information. Results showed that users and nonusers differed significantly in their education level, race, digital literacy level, Internet use frequency and perceived benefit of seeking online health information. In terms of causal relationships, age, gender, race, digital literacy and perceived benefit were significant predictors of social media use for health activities. Education, income, and Internet use frequency could predict one type of social media use respectively. Health status and perceived risk had no significant relationship with any type of social media use for health. The limitation and implication of this study was also discussed.

Authors: Arif Khan, Shahadat Uddin and Nazim Choudhury

Ebola outbreak, one of the major events of 2014, has claimed over 10,000 lives (CDC, 2015) so far. Although started earlier, it gained worldwide attention from September, 2014 when the first case was confirmed in United States (Patwardhan, 2014). Over the next few months’ window, the topic kept dominating in social networks (Househ, 2015) with varying responses. Apart from sharing knowledge and awareness, there was also widespread sentiment of panic, criticism and satire that propagated throughout the platform. Given the complex and longitudinal nature of data, it is therefore important to explore the dynamics of this particular event to understand how people’s sentiments evolve as they share and re-share information in times of crisis like Ebola. 

Objective: This research focuses on understanding the evolution of sentiments following the events of Ebola crisis. We aim to explore this dynamics from three perspectives. Firstly, from actor’s perspective where we like to identify major contributors (e.g., news agency, health organizations and individuals) of information from social network perspectives as well as their structural position within the network. Secondly, we are interested in the longitudinal analysis of sentiment propagation i.e., how fast different sentiments diffuse through the network and the time lag between an actual event occurrence and the time when that reaches most of the followers. Finally, we want to explore spatial impact on sentiments e.g., how they vary within different states or countries. 

Methods: We chose Twitter as social network platform. Related tweets were downloaded with custom software that utilizes web mining and Twitter APIs. We chose three months’ window (September – November, 2014) when related events and responses (i.e., confirmed cases, patient transfers, quarantine and deaths in Europe and United States) were at the peak outside of western Africa (Times, 2015). Data were segmented into entities e.g., text, hashtags, user, geolocation etc. and saved into database. As an ongoing work, we are using sentiment analysis software to identify subjective information. Next, we will use social network analysis methods like centrality and clustering algorithms to find prominent groups or actors within the network. We also applied a set of measures proposed by Uddin et al. (2015) to understand the actor level dynamics of longitudinal network. These measures can identify dynamicity of different sentiments over time. 

Results: The database consists of approximately 1.56 million tweets from 0.6 million users over 3 months. Half of the tweets are from United States followed by U.K., Brazil and Spain. Significant amount of information originated from dedicated accounts on Ebola crisis followed by general media agencies, healthcare organizations and individuals. Although sentiment analysis is still undergoing, initial result suggests that significant proportion of popular tweets have witty or criticizing (related to politics or celebrities) tone rather than sharing fact or information. 

Future Work: We are still applying sentiment analysis to quantify different polarities of tweets. After that, social network based structural and dynamicity measures will be applied. Furthermore, the result should be normalized to remove any bias because not every tweet is made available via Twitter API.

Authors: Dongho Choi and Chirag Shah

A variety of social media helps individuals now produce and share their information and knowledge over the online platform much easily. While, as a result, the influence of “word of mouth” is increasing faster, the shared information is not perceived equally by other people. For example, ‘most popular’ news that is highly ranked by previous viewers’ reactions and preference makes influences to following readers a lot more than rarely written news. Previous research indicated that disclosing prior collective opinions affect individual’s decision-making and as well as their perceptions of information, which is called herding effect. 

In the meantime, social media are promoting people to create high quality and quantity information for the stock of knowledge in their communities, such as Elite reviewers of Yelp.com. The assumption in this kind of user classification is that so-called Elite reviewers make substantially greater influences on other users’ information behaviours. 

Objective: 
We aim to to see if there exists the difference in herding effect by different groups of people, that and if so, to explore how does the herding behaviour look like in different communities and/or societies. We have three research questions as follows: (1) To what extent, if any, do users in different societies show different patterns of herding effect?; (2) To what extent, if any, do different product/service categories affect the herding effect?; and (3) In terms of temporal dynamics, has herding effect been changing over time? 

Methods: 
Yelp’s recently publicized data set, which includes more than 1.6 million reviews about businesses in 10 cities across 4 countries, is used to observe the rating histories of particular businesses. In addition to the rating data, other factors of reviews written by different groups of people will be extracted. More specifically, the factors extracted from reviews of elite and non-elite reviewers are trained and tested to predict current rating based on the previous history. Various classification model are used to compare the performance of our model, during which different weights are assigned to two review sets to find out the optimal weight distribution over different locations and time for consideration. 

Results: 
From the preliminary study, we have found that one of the classifiers, nearest neighbor classifier show the best prediction results when we weigh more influences on elites’ reviews than non-elites’ reviews (i.e., 0.7 for elites’ reviews vs. 0.3 for non-elites’), for the sample reviews from particular businesses. We are analyzing data from popular businesses in selected categories, and in different cities in the dataset. We expect to see another societal and temporal differences from this. 

Future Work: 
We will consider more factors, such as textual data and social network data, as well as time and location data into the model in order to understand how the influential people, or their influences work differently in different societies. Also, we will validate the findings with other customer reviews data sets or different types of social media, such as Twitter, to see if the locational and temporal differences still exist. 

Author: Debao Xiang

The study uses an empirical methodology to analyze the characteristics and influence of Twitter users who form public opinions about China. The paper finds that with respect to individual users, organizations are dominant forces on Twitter that are generating public opinions about China. Interest groups, media, enterprises and non-governmental organizations are the main organizations. Media organizations, particularly commercial media, play a large role in generating public opinions on Twitter about China. Users involved in Chinese-related discussion topics mostly come from the USA, Canada, China, Japan and other countries. In terms of influence, individuals are higher than organizations. Among individual users, media practitioners have a high and active degree of influence. Traditional media hold the highest influence among users from organizations.

Authors: Taraneh Khazaei, Lu Xiao and Robert Mercer

It has been long established that there is a correlation between the dialog behaviour of participants in communicative settings and whether they are perceived persuasive by other participants. Due to the relatively recent explosion of social media and the lack of annotated data, there is no established body of theoretical foundations for dialog behaviour and persuasiveness in such settings. In addition, there only exist a few analytical studies investigating such potential links. In this project, we aim to explore the linguistic characteristics of a communication that make a piece of text to be perceived persuasive. We draw on theories that have been developed to study language and persuasion in monologues as well as small group and one-to-one spoken conversations and extend them for online large-scale deliberations.