Social media data as a lens onto care-seeking behavior among women veterans of the US armed forces (2020)

Abstract: In this article, we examine social media data as a lens onto support-seeking among women veterans of the US armed forces. Social media data hold a great deal of promise as a source of information on needs and support-seeking among individuals who are excluded from or systematically prevented from accessing clinical or other institutions ostensibly designed to support them. We apply natural language processing (NLP) techniques to more than 3 million Tweets collected from 20,000 Twitter users. We find evidence that women veterans are more likely to use social media to seek social and community engagement and to discuss mental health and veterans’ issues significantly more frequently than their male counterparts. By contrast, male veterans tend to use social media to amplify political ideologies or to engage in partisan debate. Our results have implications for how organizations can provide outreach and services to this uniquely vulnerable population, and illustrate the utility of non-traditional observational data sources such as social media to understand the needs of marginalized groups.

## introduction women comprise a small but rapidly growing portion of the us military veteran population. indeed, women are one of the fastest-growing demographics in this population @xcite , and saw a two-fold increase between 1988 and 2008 @xcite . this trend is reflected in the use of department of veterans affairs (va) services: since 2000, the number of women seeking care at the va has increased from around 150,000 to half a million. the rapid increase in the number of women veterans, coupled with increases in the rates at which these women use veteran healthcare resources, represents a profoundly significant development for veteran service organizations (vsos) and veteran healthcare providers. while women veterans face many of the same challenges as their male counterparts during and after deployment, there are also notable differences in the types and rates of specific challenges faced by these two groups. perhaps the most striking example concerns military sexual trauma (mst). a recent study found that, among veterans diagnosed with ptsd, 31% of women reported mst, compared to just 1% of men @xcite . the same study found that mst was, in turn, highly comorbid with depression, anxiety, and eating disorders in women veterans. while the incidence of ptsd, particularly related to combat exposure, and substance use disorder seem to be greater among male veterans, women veterans are overall more likely to experience service-related disabilities than men @xcite , and are also more likely than men to meet diagnostic criteria for depression and anxiety @xcite . finally, according to a recently released report from the va, the suicide rate among women veterans increased by 6.5% between 2005 and 2017, which positions women veterans as the demographic group of veterans with the fastinggrowing suicide rate @xcite . not only do women veterans present a clinical picture that differs in systematic ways from that of their male counterparts, and therefore place distinctive demands on clinicians and other healthcare providers, women veterans are also a remarkably demographically diverse population. relative to the general population, racial, ethnic, and sexual minorities are significantly over-represented among women veterans @xcite @xcite . women in the us armed forces also tend to be younger, on average, and are more likely to belong to racial and ethnic minorities than their male counterparts @xcite . ## topic modeling to begin with, we pursued a bottom-up, datadriven analysis of the data-namely, topic modeling-to assess differences in how men and women veterans use social media. topic modeling is an unsupervised machine learning algorithm that discovers latent semantic structure in collections of text by finding coherent "topics". intuitively, topics can be thought of as bundles of words with high probabilities of co-occurrence. topic modeling is therefore more sensitive to subtle linguistic patterns than simply counting word frequencies. for instance, simple word frequency analyses might reveal that the word "party" occurs frequently in a discourse. this finding, however, would be open to interpretation, owing to the ambiguity of the word "party" and the lack of context of word frequency (sometimes called unigram frequency). by contrast, topic modeling might reveal that "party" frequently co-occurs with "america", "freedom", "liberty". these words would be said to form a "topic". topic modeling assigns a distribution to word-topic combinations, such that for a given word or term, we can estimate the probability that that word came from a given topic with p(term|topic). to prepare the data for topic modeling, we first removed all retweets, urls, hashtags, and mentions. topic modeling using latent dirichlet allocation (lda; @xcite ) was applied to the remaining 2.1 million posts that remained after data cleaning. posts were then assigned a topic by finding the topic which maximizes the probability of all words in the post. next, we computed the rate at which each user mentioned each topic by simply dividing the number of posts about each topic authored by the user by the total number of posts from the user; we will refer to this quotient as mention rate. we then computed an average topic mention rate for men and women by averaging by-user mention rates separately for the two groups. for ease of exposition, we asked human annotators to assign humanreadable labels to the topics generated by our analysis. creating such topic labels is often as much art as science, and runs the risk of entrenching the biases of the annotators. in this context, we'll note first that we created these labels before conducting the analyses reported below. second, the goal of this work is ultimately to inform and educate non-technical stakeholders and empower them to use methods such as these to, for example, programmatically identify women veterans on social media who may benefit from the services of particular vsos. we feel that it's important, in early phases of this work especially, to present these results in a way that makes them accessible to the intended end users. we began by finding which topics were, overall, most frequent in the posts of women veterans and male veterans. we found that the same five topics were most common among men and women, and these are shown in figure 1 . we found that the most common topic among male veterans was "partisan politics", corresponding to around 3.75% of all posts. for women, the most common topic was "personal reflection", corresponding to about 3.52% of all posts. to identify more subtle differences between the two groups, we then computed the difference in mention rate across men and women (women's mention rate -men's mention rate). in figure 2 , we plot the difference in topic mention rate for the five topics with the largest positive difference (i.e., topics mentioned more by women) as well as the five topics with the largest negative difference (i.e., topics mentioned more by men). ## mental health and veteran keywords in order to more directly examine differences in social media use between women veterans and male veterans and how this might pertain to careseeking, we asked whether there are gender differences in the use of specific keywords related to veteran's issues and health issues of common concern to veterans, such as post-traumatic stress. to identify these keywords, we used word embeddings to find the semantic nearest neighbors of seed terms deemed a priori relevant to these subjects, in collaboration with subject matter experts (see footnote 1). word embeddings encode the words in a collection of text in terms of a numeric vector of all the contexts in which each word occurs. for instance, suppose we had a sample of english that consisted of the sentences "the visitor likes dogs" and "the dog likes visitors". leaving aside singular/plural morphology, we would represent "visitor" and "dogs" in very similar terms, since they seem to occur in very similar types of contexts (after "the" or after "likes"), whereas "the" appears to occur in very different contexts. this is useful because it allows us to precisely define how similar two words are by computing the euclidean distance between those two words in the encoding scheme. here, we used word2vec @xcite to compute embeddings for the words in our corpus. we then identified mental health-related keywords by finding the 50 terms most similar to "depression" and "ptsd". we identified veteran-related keywords by finding the 50 terms (specifically, lemmatized words) most similar to "veteran" and "service". for the sake of illustration, we display the top 15 terms in each group in ## discussion these exploratory analyses provide an initial, rich set of indications that women veterans and male veterans use social media differently, and that these differences are relevant to the work of vsos and the va. specifically, women veterans seem more likely than their male counterparts to use social media for finding community and for discussing or seeking information about healthcare. these findings have specific implications for the vso community and the va in considering opportunities for outreach to women veterans using social media platforms. before proceeding further, however, we acknowledge that the method used to classify users according to gender, in their attempt to identify general patterns, could systematically mis-classify individuals whose social media behavior does not conform to gender norms, or to their own gender identity. this kind of systematic bias falls under the heading of what is often known as "algorithmic bias". we believe that the best remedy for algorithmic bias-or any other form of difficult-to-avoid bias-is to seek converging evidence from multiple methods and data sources, and thus avoid over-interpreting any one particular set of results. analyses like those reported here suggest several avenues for action to veteran organizations such as vsos and mental health organizations that serve veteran populations. specifically, our results suggest that social media-based outreach campaigns intended to promote self-care and treatment-seeking among veterans may be a particularly promising way to engage women veterans. results from modeling efforts such as those reported here could be used to construct tailored audiences on platforms such as facebook, instagram, and twitter. indeed, pilot studies are underway to test the efficacy of constructing social media advertising campaigns for veteran-oriented, inpatient treatment based on the data reported here. moreover, topics, keywords, and n-grams (not explored here) constructed from corpora such as the one described in this paper are particularly well-suited to informing search-based advertising, veteran keywords mental health keywords veteran suicide supporter crime family murder marines medication military pain army ptsd service disability unit stress commitment depression patriot alcoholism honorably addiction leader anxiety sir nightmares doctor mysteriously medical disorder finally, evidence that women veterans may be more likely to use social media for care-and community-seeking suggests the intriguing possibility that telehealth services such as web-based mental health counseling may be particularly welcomed by this community. beyond engagement and outreach in the traditional sense, it is important to recognize that social media constitutes an important outlet for seeking care and information about care for women vet-erans. it should therefore be a priority for vsos and the va to disseminate quality content for these platforms so that those using it for this purpose can find the right information at the right time and ultimately get connected to appropriate resources. ## conclusion in this paper we presented a series of analyses strongly suggesting that women veterans of the us military use social media-specifically, twitterin a qualitatively different way from male veterans. specifically, women veterans appear more likely to use social media platforms to engage in conversations about mental health and veteranspecific issues. these findings suggest that social media may be hugely important for veteran service organizations seeking to reach, connect with, and care for more women.