Assessment of Provider Attitudes Toward #Naloxone on Twitter

BACKGROUND:

As opioid overdose rates continue to pose a major public health crisis, the need for naloxone treatment by emergency first responders is critical. Little is known about the views of those who administer naloxone. The current study examines attitudes of health professionals on the social media platform Twitter to better understand their perceptions of opioid users, the role of naloxone and potential training needs.

METHODS:

Public comments on Twitter regarding naloxone were collected for a period of three consecutive months. The occupations of individuals who posted tweets were identified through Twitter profiles or hashtags. Categories of emergency service first responders and medical personnel were created. Qualitative analysis using a grounded theory approach was used to produce thematic content. The relationships between occupation and each theme were analyzed using Pearson chi-square statistics and post-hoc analyses.

RESULTS:

A total of 368 individuals posted 467 naloxone-related tweets. Occupations consisted of professional first responders such as emergency medical technicians (EMTs), firefighters, and paramedics (n = 122); law enforcement officers (n = 70); nurses (n = 62); physicians (n = 48); other health professionals including pharmacists, pharmacy technicians, counselors, social workers (n = 31); naloxone-trained individuals (n = 12); and students (n = 23). Primary themes included burnout, education and training, information-seeking, news updates, optimism, policy and economics, stigma, and treatment. The highest levels of burnout, fatigue and stigma regarding naloxone and opioid overdose were among nurses, EMTs, other health care providers and physicians. In contrast, individuals who self-identified as "naloxone-trained" had the highest optimism and the lowest amount of burnout and stigma.

CONCLUSIONS:

Provider training and refinement of naloxone administration procedures is needed to improve treatment outcomes and reduce provider stigma. Social networking sites such as Twitter may have potential for offering psychoeducation to health care providers.

Purchase full article at:   http://goo.gl/Ha5SlI

By:  Haug NA1, Bielenberg J1, Linder SH2, Lembke A3.

• 1 PGSP-Stanford University Psy.D. Consortium, Palo Alto University , Los Altos , CA , USA.
• 2 VA Palo Alto Health Care System , Palo Alto , CA , USA.
• 3 Department of Psychiatry and Behavioral Sciences , Stanford University School of Medicine , Stanford , CA , USA.
• Subst Abus. 2016 Feb 9:0.  

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“Hey Everyone, I’m Drunk.” An Evaluation of Drinking-Related Twitter Chatter

Objective:

The promotion of drinking behaviors correlates with increased drinking behaviors and intent to drink, especially when peers are the promotion source. Similarly, online displays of peer drinking behaviors have been described as a potential type of peer pressure that might lead to alcohol misuse when the peers to whom individuals feel attached value such behaviors. Social media messages about drinking behaviors on Twitter (a popular social media platform among young people) are common but understudied. In response, and given that drinking alcohol is a widespread activity among young people, we examined Twitter chatter about drinking.

Method:

Tweets containing alcohol- or drinking-related keywords were collected from March 13 to April 11, 2014. We assessed a random sample (n = 5,000) of the most influential Tweets for sentiment, theme, and source.

Results:

Most alcohol-related Tweets reflected a positive sentiment toward alcohol use, with pro-alcohol Tweets outnumbering anti-alcohol Tweets by a factor of more than 10. The most common themes of pro-drinking Tweets included references to frequent or heavy drinking behaviors and wanting/needing/planning to drink alcohol. The most common sources of pro-alcohol Tweets were organic (i.e., noncommercial).

Conclusions:

Our findings highlight the need for online prevention messages about drinking to counter the strong pro-alcohol presence on Twitter. However, to enhance the impact of anti-drinking messages on Twitter, it may be prudent for such Tweets to be sent by individuals who are widely followed on Twitter and during times when heavy drinking is more likely to occur (i.e., weekends, holidays).

Purchase full article at:   http://goo.gl/kgO0kX

By:   Patricia A. Cavazos-Rehg, Ph.D.,^a,* Melissa J. Krauss, M.P.H.,^a Shaina J. Sowles, M.P.H.,^a & Laura J. Bierut, M.D.^a

Affiliations
^aDepartment of Psychiatry, Washington University School of Medicine, St. Louis, Missouri

*Correspondence may be sent to Patricia A. Cavazos-Rehg at the Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, Box 8134, St. Louis, MO 63110, or via email at:rehgp@psychiatry.wustl.edu.

DOI: http://dx.doi.org/10.15288/jsad.2015.76.635 

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#TestMeEast: A Campaign to Increase HIV Testing in Hospitals & to Reduce Late Diagnosis

Late diagnosis occurs in almost half of those diagnosed in the UK (HIV Prevention England, 2013. Retrieved June 22, 2014, from HIV Prevention England: http://www.hivpreventionengland.org.uk/Campaigns-Current/National-HIV-Testing-Week). Testing occurs mainly in sexual health and antenatal clinics despite recommendations to test more broadly 

We report the findings of an HIV-testing week campaign to offer testing to those who have blood tests as part of routine care within outpatient clinics and emergency departments of six London hospitals. The campaign target was to test 500 patients a day during the 2013 National HIV Testing Week (NHTW). Clinic staff and medical students were trained to offer routine HIV testing. Linkage to care was arranged for those who tested HIV-positive. 

During NHTW we tested 2402 of the planned 2500 test target. 2402/4317 (55.6% 95% CI 54.1-57.1%) of those who had routine blood tests were tested for HIV. There were eight HIV-positive tests; three were new diagnoses (all linked to care). The campaign hashtag #TestMeEast achieved a total Twitter "reach" of 238, 860 and the campaign had widespread news coverage. Our campaign showed that staff and students could be trained and mobilised to do thousands of routine HIV tests during a campaign.

Purchase full article at:   http://goo.gl/BVVLLG

By:   Bath R1, O'Connell R1, Lascar M1, Ferrand R2, Strachan S3, Matin N1, Bassnet I1, Orkin C1,4.

• 1 Barts Health NHS Trust , London , UK.
• 2 London School of Hygiene and Tropical Medicine , London , UK.
• 3 Positively UK , London , UK.
• 4 HIV Medicine, Infection and Immunity , Royal London Hospital , London , UK. 

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Diffusion of Messages from an Electronic Cigarette Brand to Potential Users through Twitter

Objective

This study explores the presence and actions of an electronic cigarette (e-cigarette) brand, Blu, on Twitter to observe how marketing messages are sent and diffused through the retweet (i.e., message forwarding) functionality. Retweet networks enable messages to reach additional Twitter users beyond the sender’s local network. We follow messages from their origin through multiple retweets to identify which messages have more reach, and the different users who are exposed.

Methods

We collected three months of publicly available data from Twitter. A combination of techniques in social network analysis and content analysis were applied to determine the various networks of users who are exposed to e-cigarette messages and how the retweet network can affect which messages spread.

Results

The Blu retweet network expanded during the study period. Analysis of user profiles combined with network cluster analysis showed that messages of certain topics were only circulated within a community of e-cigarette supporters, while other topics spread further, reaching more general Twitter users who may not support or use e-cigarettes.

Conclusions

Retweet networks can serve as proxy filters for marketing messages, as Twitter users decide which messages they will continue to diffuse among their followers. As certain e-cigarette messages extend beyond their point of origin, the audience being exposed expands beyond the e-cigarette community. Potential implications for health education campaigns include utilizing Twitter and targeting important gatekeepers or hubs that would maximize message diffusion.

Below:  Description of the 3-layer retweet network. (A) Layer 0 (Blu) sends the original tweet. (B) This is followed by a Layer 1 user that retweets the message. (C) Finally, a Layer 2 user retweets the retweet.

Below:  The number of users found in each category in Layer 1 and Layer 2 of the retweet network

Below:  The retweet networks of the data collected February to April of 2014.

In the rewteet network, the size of node corresponds to the number of retweets from this particular user and the width of link corresponds to the number of retweets made by the users of the ending node (y) from the users of the starting node (x). Red = Person-Supporter, Blue = Industry-RetailerManufacturer, Yellow = Person-BasicProfile, Cyan = Nonperson, Green = Industry-Other, White = Unknown, Purple = TobaccoControl-Research. (A) Includes users from Layer 1 (i.e., only those who retweeted messages by Blu) and (B) includes all users (i.e. Layer 1 and Layer 2).

Full article at:   http://goo.gl/EFCgno

By:   

Kar-Hai Chu, Jennifer B. Unger, Jon-Patrick Allem, Monica Pattarroyo, Daniel Soto, Tess Boley Cruz

Department of Preventive Medicine, University of Southern California, Los Angeles, California, United States of America

Haodong Yang, Ling Jiang, Christopher C. Yang

College of Computing and Informatics, Drexel University, Philadelphia, Pennsylvania, United States of America

 

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Action Tweets Linked to Reduced County-Level HIV Prevalence in the United States: Online Messages and Structural Determinants

HIV is uncommon in most US counties but travels quickly through vulnerable communities when it strikes. Tracking behavior through social media may provide an unobtrusive, naturalistic means of predicting HIV outbreaks and understanding the behavioral and psychological factors that increase communities' risk. 

General action goals, or the motivation to engage in cognitive and motor activity, may support protective health behavior (e.g., using condoms) or encourage activity indiscriminately (e.g., risky sex), resulting in mixed health effects. We explored these opposing hypotheses by regressing county-level HIV prevalence on action language (e.g., work, plan) in over 150 million tweets mapped to US counties. Controlling for demographic and structural predictors of HIV, more active language was associated with lower HIV rates. 

By leveraging language used on social media to improve existing predictive models of geographic variation in HIV, future targeted HIV-prevention interventions may have a better chance of reaching high-risk communities before outbreaks occur.

Purchase full article at:    http://goo.gl/dLz3OU

By:   Ireland ME1,2, Chen Q3,4, Schwartz HA3,4,5, Ungar LH3,4, Albarracin D3,4.

• 1Department of Psychological Sciences, Texas Tech University, MS 2051, Lubbock, TX, 79409, USA. molly.ireland@ttu.edu.
• 2University of Illinois at Urbana-Champaign, Champaign, IL, USA. molly.ireland@ttu.edu.
• 3University of Pennsylvania, Philadelphia, PA, USA.
• 4University of Illinois at Urbana-Champaign, Champaign, IL, USA.
• 5Department of Computer Sciences, Stony Brook University, Stony Brook, NY, USA. 

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Sentiment of Emojis

There is a new generation of emoticons, called emojis, that is increasingly being used in mobile communications and social media. In the past two years, over ten billion emojis were used on Twitter. Emojis are Unicode graphic symbols, used as a shorthand to express concepts and ideas. In contrast to the small number of well-known emoticons that carry clear emotional contents, there are hundreds of emojis. But what are their emotional contents? 

We provide the first emoji sentiment lexicon, called the Emoji Sentiment Ranking, and draw a sentiment map of the 751 most frequently used emojis. The sentiment of the emojis is computed from the sentiment of the tweets in which they occur. We engaged 83 human annotators to label over 1.6 million tweets in 13 European languages by the sentiment polarity (negative, neutral, or positive). About 4% of the annotated tweets contain emojis. 

The sentiment analysis of the emojis allows us to draw several interesting conclusions. It turns out that most of the emojis are positive, especially the most popular ones. The sentiment distribution of the tweets with and without emojis is significantly different. The inter-annotator agreement on the tweets with emojis is higher. Emojis tend to occur at the end of the tweets, and their sentiment polarity increases with the distance. 

We observe no significant differences in the emoji rankings between the 13 languages and the Emoji Sentiment Ranking. Consequently, we propose our Emoji Sentiment Ranking as a European language-independent resource for automated sentiment analysis. 

Finally, the paper provides a formalization of sentiment and a novel visualization in the form of a sentiment bar.

Below:  Sentiment map of the 751 emojis. Left: negative (red), right: positive (green), top: neutral (yellow). Bubble size is proportional to log₁₀ of the emoji occurrences in the Emoji Sentiment Ranking. Sections A, B, and C are references to the zoomed-in panels in Fig 3.

Below:  Average positions of the 751 emojis in tweets. Bubble size is proportional to log₁₀ of the emoji occurrences in the Emoji Sentiment Ranking. Left: the beginning of tweets, right: the end of tweets, bottom: negative (red), top: positive (green).

Full article at:  http://goo.gl/mj7Ufx

By:  Petra Kralj Novak, Jasmina Smailović, Borut Sluban, Igor Mozetič

Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia

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Impact of Repeated Exposures on Information Spreading in Social Networks

Clustered structure of social networks provides the chances of repeated exposures to carriers with similar information. It is commonly believed that the impact of repeated exposures on the spreading of information is nontrivial. Does this effect increase the probability that an individual forwards a message in social networks? If so, to what extent does this effect influence people’s decisions on whether or not to spread information? Based on a large-scale microblogging data set, which logs the message spreading processes and users’ forwarding activities, we conduct a data-driven analysis to explore the answer to the above questions. 

The results show that an overwhelming majority of message samples are more probable to be forwarded under repeated exposures, compared to those under only a single exposure. For those message samples that cover various topics, we observe a relatively fixed, topic-independent multiplier of the willingness of spreading when repeated exposures occur, regardless of the differences in network structure. We believe that this finding reflects average people’s intrinsic psychological gain under repeated stimuli. Hence, it makes sense that the gain is associated with personal response behavior, rather than network structure. 

Moreover, we find that the gain is robust against the change of message popularity. This finding supports that there exists a relatively fixed gain brought by repeated exposures. Based on the above findings, we propose a parsimonious model to predict the saturated numbers of forwarding activities of messages. Our work could contribute to better understandings of behavioral psychology and social media analytics.

Below:  Forwarding dynamics in a direct follower network. The figure in the left shows a simple direct follower network of root user 0. When user 0 posts a message, all of his followers will be exposed to this message once (the numbers in braces). In the second step, user 1 forwards this message (dark colour), all the followers of user 1, which is user 2, 3 and 4 (in the shaded area) are exposed to this message one more time. Then user 7 forwards this message. Same thing happens except that now user 4 has been exposed to the message three times since he follows both user 1 and user 7.

Below:  Number of users who forward messages under certain number of exposures. For the top 10 most active root users, we count the number of their followers who have forwarded their messages under certain number of exposures, respectively. In this figure, we show the median of these counts with respect to exposure number. In order to clearly show the results, the y-axis is transformed into a logarithmic scale. There are no remarkable forwarding activities when exposure number exceeds 5.

Below:  Forwarding probability under 1 to 8 exposures. Each value is the median of all the forwarding probabilities under the corresponding number of exposures. It is due to the rareness of data that the values under 7 and 8 exposures are zero.

Full article at: http://goo.gl/lOhwPd

By: Cangqi Zhou, Qianchuan Zhao,* and Wenbo Lu

Center for Intelligent and Networked Systems (CFINS), Department of Automation and TNList, Tsinghua University, Beijing, 100084, China

Beijing University of Posts and Telecommunications, CHINA

Conceived and designed the experiments: CZ QZ. Performed the experiments: CZ. Analyzed the data: CZ QZ WL. Contributed reagents/materials/analysis tools: CZ. Wrote the paper: CZ QZ WL.

* E-mail: nc.ude.auhgnist@cqoahz

  

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Associations Between Exposure to and Expression of Negative Opinions About Human Papillomavirus Vaccines on Social Media: An Observational Study

Below:  The network of 30,621 users that tweeted about HPV vaccines during the period between October 2013 and April 2014 organized via heuristic so that users are closer to other users with whom they are connected. The sizes of the nodes are proportional to the number of followers within the network. Users are colored according to information exposure (orange: those exposed to a majority of negative opinions; cyan: users that were exposed to mostly neutral/positive tweets; gray: users not exposed to HPV vaccine tweets).

During the 6-month period, 25.13% (20,994/83,551) of tweets were classified as negative; among the 30,621 users that tweeted about HPV vaccines, 9046 (29.54%) were exposed to a majority of negative tweets. The likelihood of a user posting a negative tweet after exposure to a majority of negative opinions was 37.78% (2780/7361) compared to 10.92% (1234/11,296) for users who were exposed to a majority of positive and neutral tweets corresponding to a relative risk of 3.46 (95% CI 3.25-3.67, P<.001).

The heterogeneous community structure on Twitter appears to skew the information to which users are exposed in relation to HPV vaccines. We found that among users that tweeted about HPV vaccines, those who were more often exposed to negative opinions were more likely to subsequently post negative opinions. Although this research may be useful for identifying individuals and groups currently at risk of disproportionate exposure to misinformation about HPV vaccines, there is a clear need for studies capable of determining the factors that affect the formation and adoption of beliefs about public health interventions.

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