Inducing Behavioral Change in Seekers of Pro-Anorexia Content Using Internet Advertisements: Randomized Controlled Trial

Mind the gap: Exploring differences in suicide literacy between cybersuicide and offline suicide

Introduction

The highly public nature of cybersuicide contradicts long-held beliefs of offline suicide, which may cause differences in the way people perceive and respond to both of them. However, knowledge of whether and how suicide literacy differs between cybersuicide and offline suicide is limited.

Methods

By analyzing social media data, this paper focused on livestreamed suicide and aimed to compare suicide literacy between cybersuicide and offline suicide on three aspects, including false knowledge structure, extent of association with stigma, and linguistic expression pattern. 7,236 Sina Weibo posts with relevant keywords were downloaded and analyzed. First, a content analysis was performed by human coders to determine whether each post reflected suicide-related false knowledge and stigma. Second, a text analysis was conducted using the Simplified Chinese version of LIWC software to automatically extract psycholinguistic features from each post. Third, based on selected features, classification models were developed using machine learning techniques to differentiate false knowledge of cybersuicide from that of offline suicide.

Results

Results showed that, first, cybersuicide-related posts generally reflected more false knowledge than offline suicide-related posts ( χ 1 2 = 255.13, p < 0.001). Significant differences were also observed in seven false knowledge types. Second, among posts reflecting false knowledge, cybersuicide-related posts generally carried more stigma than offline suicide-related posts ( χ 1 2 = 116.77, p < 0.001). Significant differences were also observed in three false knowledge types. Third, among established classification models, the highest F1 value reached 0.70.

Discussion

The findings provide evidence of differences in suicide literacy between cybersuicide and offline suicide, and indicate the need for public awareness campaigns that specifically target cybersuicide.

Stigmatizing Attitudes Across Cybersuicides and Offline Suicides: Content Analysis of Sina Weibo

Background

The new reality of cybersuicide raises challenges to ideologies about the traditional form of suicide that does not involve the internet (offline suicide), which may lead to changes in audience’s attitudes. However, knowledge on whether stigmatizing attitudes differ between cybersuicides and offline suicides remains limited.

Objective

This study aims to consider livestreamed suicide as a typical representative of cybersuicide and use social media data (Sina Weibo) to investigate the differences in stigmatizing attitudes across cybersuicides and offline suicides in terms of attitude types and linguistic characteristics.

Methods

A total of 4393 cybersuicide-related and 2843 offline suicide-related Weibo posts were collected and analyzed. First, human coders were recruited and trained to perform a content analysis on the collected posts to determine whether each of them reflected stigma. Second, a text analysis tool was used to automatically extract a number of psycholinguistic features from each post. Subsequently, based on the selected features, a series of classification models were constructed for different purposes: differentiating the general stigma of cybersuicide from that of offline suicide and differentiating the negative stereotypes of cybersuicide from that of offline suicide.

Results

In terms of attitude types, cybersuicide was observed to carry more stigma than offline suicide (χ²₁=179.8; P<.001). Between cybersuicides and offline suicides, there were significant differences in the proportion of posts associated with five different negative stereotypes, including stupid and shallow (χ²₁=28.9; P<.001), false representation (χ²₁=144.4; P<.001), weak and pathetic (χ²₁=20.4; P<.001), glorified and normalized (χ²₁=177.6; P<.001), and immoral (χ²₁=11.8; P=.001). Similar results were also found for different genders and regions. In terms of linguistic characteristics, the F-measure values of the classification models ranged from 0.81 to 0.85.

Conclusions

The way people perceive cybersuicide differs from how they perceive offline suicide. The results of this study have implications for reducing the stigma against suicide.

Unique Internet Search Strategies of Individuals With Self-Stated Autism: Quantitative Analysis of Search Engine Users' Investigative Behaviors

Background

Although autism is often characterized in literature by the presence of repetitive behavior, in structured decision tasks, individuals with autism spectrum disorder (ASD) have been found to examine more options in a given time period than controls.

Objective

We aimed to examine whether this investigative tendency emerges in information searches conducted via the internet.

Methods

In total, 1746 search engine users stated that they had ASD in 2019. This group’s naturally occurring responses following 1491 unique general queries and 78 image queries were compared to those of all other users of the search engine. The main dependent measure was scrolled distance, which denoted the extent to which additional results were scanned beyond the initial results presented on-screen. Additionally, we examined the number of clicks on search results as an indicator of the degree of search outcome exploitation and assessed whether there was a trade-off between increased search range and the time invested in viewing initial search results.

Results

After issuing general queries, individuals with self-stated ASD scanned more results than controls. The scrolled distance in the results page of general queries was 45% larger for the group of individuals with ASD (P<.001; d=0.45). The group of individuals with ASD also made the first scroll faster than the controls (P<.001; d=0.51). The differences in scrolled distance were larger for popular queries. No group differences in scrolled distance emerged for image queries, suggesting that visual load impeded the investigative behavior of individuals with ASD. No differences emerged in the number of clicks on search results.

Conclusions

Individuals who self-stated that they had ASD scrutinized more general search results and fewer image search results than the controls. Thus, our results at least partially support the notion that individuals with ASD exhibit investigative behaviors and suggest that textual searches are an important context for expressing such tendencies.

Psychological Impact of Pro-Anorexia and Pro-Eating Disorder Websites on Adolescent Females: A Systematic Review

(1) Background: Teenagers (in particular, females) suffering from eating disorders report being not satisfied with their physical aspect and they often perceive their body image in a wrong way; they report an excessive use of websites, defined as PRO-ANA and PRO-MIA, that promote an ideal of thinness, providing advice and suggestions about how to obtain super slim bodies. (2) Aim: The aim of this review is to explore the psychological impact of pro-ana and pro-mia websites on female teenagers. (3) Methods: We have carried out a systematic review of the literature on PubMed. The search terms that have been used are: "Pro" AND "Ana" OR "Blogging" AND "Mia". Initially, 161 publications were identified, but in total, in compliance with inclusion and exclusion criteria, 12 studies have been analyzed. (4) Results: The recent scientific literature has identified a growing number of Pro Ana and Pro Mia blogs which play an important role in the etiology of anorexia and bulimia, above all in female teenagers. The feelings of discomfort and dissatisfaction with their physical aspect, therefore, reduce their self-esteem. (5) Conclusion: These websites encourage anorexic and bulimic behaviors, in particular in female teenagers. Attention to healthy eating guidelines and policies during adolescence, focused on correcting eating behavioral aspects, is very important to prevent severe forms of psychopathology with more vulnerability in the perception of body image, social desirability, and negative emotional feedback.

Vaccine advertising: preach to the converted or to the unaware?

Encouraging people to vaccinate is a challenging endeavor, but one which has tremendous public health benefits. Doing so requires overcoming barriers of awareness, availability, and (sometimes) vaccine hesitancy. Here we focus on nudging people to vaccinate through online advertising. We conducted a pre-registered online ads campaign encouraging people to vaccinate against three diseases: influenza, human papillomavirus, and herpes zoster. Ads were shown to ~69,000 people and were compared to similar ads shown to 8.6 million people. Outcome measures were clicks on ads and future searches for relevant terms. We find that ads have two main effects: First, a congruence effect whereby ads increase the likelihood of clicks and future searches by up to 116% in people who express an interest in the disease or the vaccine. Most commercial vaccine advertising is aimed entirely at this population. Second, we observed a priming effect, where ads shown to people who were searching for terms unrelated to the vaccine could be encouraged to click on them (odds ratios of 7.5–33.0) and, more often, search for the vaccine later (hazard ratios of 6.9–157.3). We provide analysis for optimizing vaccine advertising campaign budgets to balance the two populations. These findings demonstrate that digital advertising campaigns should consider not just advertising to direct keywords or to individuals that look exactly like existing customers, but consider tangential keywords that draw a wider target population who are likely earlier in their conversion funnel, thus increasing the number of people who vaccinate and maximizing vaccines uptake.

Predicting eating disorders from Internet activity

OBJECTIVE

Eating disorders (EDs) compromise the health and functioning of affected individuals, but it can often take them several years to acknowledge their illness and seek treatment. Early identification of individuals with EDs is a public health priority, and innovative approaches are needed for such identification and ultimate linkage with evidence-based interventions. This study examined whether Internet activity data can predict ED risk/diagnostic status, potentially informing timely interventions.

METHOD

Participants were 936 women who completed a clinically validated online survey for EDs, and 231 of them (24.7%) contributed their Internet browsing history. A machine learning algorithm used key attributes from participants' Internet activity histories to predict their ED status: clinical/subclinical ED, high risk for an ED, or no ED.

RESULTS

The algorithm reached an accuracy of 52.6% in predicting ED risk/diagnostic status, compared to random decision accuracy of 38.1%, a relative improvement of 38%. The most predictive Internet search history variables were the following: use of keywords related to ED symptoms and websites promoting ED content, participant age, median browsing events per day, and fraction of daily activity at noon.

DISCUSSION

ED risk or clinical status can be predicted via machine learning with moderate accuracy using Internet activity variables. This model, if replicated in larger samples where it demonstrates stronger predictive value, could identify populations where further assessment is merited. Future iterations could also inform tailored digital interventions, timed to be provided when target online behaviors occur, thereby potentially improving the well-being of many individuals who may otherwise remain undetected.

Analyzing big data in social media: Text and network analyses of an eating disorder forum

OBJECTIVE

Social media plays an important role in everyday life of young people. Numerous studies claim negative effects of social media and media in general on eating disorder risk factors. Despite the availability of big data, only few studies have exploited the possibilities so far in the field of eating disorders.

METHOD

Methods for data extraction, computerized content analysis, and network analysis will be introduced. Strategies and methods will be exemplified for an ad-hoc dataset of 4,247 posts and 34,118 comments by 3,029 users of the proed forum on Reddit.

RESULTS

Text analysis with latent Dirichlet allocation identified nine topics related to social support and eating disorder specific content. Social network analysis describes the overall communication patterns, and could identify community structures and most influential users. A linear network autocorrelation model was applied to estimate associations in language among network neighbors. The supplement contains R code for data extraction and analyses.

DISCUSSION

This paper provides an introduction to investigating social media data, and will hopefully stimulate big data social media research in eating disorders. When applied in real-time, the methods presented in this manuscript could contribute to improving the safety of ED-related online communication.