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THE FLORIDA STATE UNIVERSITY

THE COLLEGE OF COMMUNICATION AND INFORMATION

TWITTER CONTENT FOR DEPRESSION ADVOCACY AND AWARENESS

By

LEAH RAMSIER

A Thesis submitted to the

School of Information

in partial fulfillment of the requirements for graduation with

Honors in the Major

Degree Awarded:

Spring, 2017

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TABLEOFCONTENTSAbstract.......................................................................................................................................................................3Introduction...............................................................................................................................................................3LiteratureReview....................................................................................................................................................4DepressionandMentalHealthLiteracy........................................................................................................................4Twitter......................................................................................................................................................................................4Hashtags...................................................................................................................................................................................5ThemesofTweets.................................................................................................................................................................7UserInteraction....................................................................................................................................................................7ChallengesforSocialMediaandHealthCampaigns.................................................................................................8ImplicationsforResearch..................................................................................................................................................9

Methods....................................................................................................................................................................11SocialNetworkAnalysis..................................................................................................................................................11SummaryofMethods........................................................................................................................................................13

Results.......................................................................................................................................................................13Discussion................................................................................................................................................................23Conclusion...............................................................................................................................................................26References...............................................................................................................................................................28AppendixA..............................................................................................................................................................32AppendixB..............................................................................................................................................................38

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ABSTRACTDepression is a mental health issue that negatively impacts quality of life. Social media are

a popular way to discuss topics with a broad audience, and they can be used to raise awareness

and spread information. This study focuses on the platform Twitter, researching the content in the

hashtag #depression from October 2nd, 2016 to January 10th, 2017. It used exploratory social

network analysis to find patterns in the social networks within the hashtag. It also used a survey to

find which co-occurring hashtags Twitter users are most interested in and are perceived as being

most related to depression. The study found hashtags co-occurring with #depression and

visualized the structure of the hashtag, mention, and retweet social networks within #depression.

It also found which popular co-occurring hashtags were most interesting to Twitter users (#love,

#exercise, and #treatment) and which popular co-occurring hashtags were perceived by Twitter

users as most relevant to #depression (#depression, #dépression, #mentalhealth, #mentalillness,

and #mindfulness).

INTRODUCTIONDepression is a debilitating mental illness that has negative implications for quality of life

(The National Institute of Mental Health, 2016). Raising awareness for such a health problem is an

important task, and there are many platforms through which to achieve that. Social media, like

Twitter, are a relevant way to and spread information, especially due to the widespread nature of

these technologies. Mass media, grassroots (users who are not widely followed), and evangelists

all come together as part of the information flow on Twitter, disseminating it throughout the

network (Cha, Benevenuto, Haddadi, & Gummadi, 2012). Alexa’s website traffic rankings rank

Twitter as the 8th most popular website in the United States and the 16th most popular website in

the world, making it clear that Twitter is an influential platform (“Twitter.com Traffic Statistics”,

n.d.). As such, it will be useful to research the way users are discussing depression on Twitter.

This thesis seeks to better understand depression-related content on Twitter through exploration

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of the social networks existing within #depression, and the attitudes Twitter users have toward

depression-related content.

LITERATUREREVIEW

DEPRESSIONANDMENTALHEALTHLITERACY Depression is a mental illness with a large impact on society. Affecting more than 27 million

Americans and most likely the cause behind 30,000 suicides per year, it has widespread negative

implications for quality of life (De Choudhury, Counts, & Horvitz, 2013). As such, it is important

that the public understands how to recognize and seek treatment for depression.

A lack of mental health literacy can lead to a lack of trust in evidence-based mental health

treatments recommended by professionals (Jorm, 2000). This could lead to depressed individuals

not seeking appropriate treatment. Another consequence of a mental health illiterate society is a

lack of ability to help identify and connect people with mental illnesses to the appropriate

resources and self-help (Jorm, 2000). As these consequences can have a very real effect on the

mental health of individuals, it follows that helping individuals to develop these skills can have

positive outcomes for quality of life.

TWITTER Among the many social media platforms, Twitter is a platform characterized by short

(limited to 140 characters) and frequent updates by users (Java, Song, Finin, & Tseng, 2007).

These characteristics make Twitter a microblogging service, a type of blogging characterized by

short, frequent updates (Java, Song, Finin, & Tseng, 2007). Hashtags (#) are a feature on Twitter

users utilize to converse with others about a particular topic (Calvin, Bellmore, Xu, & Zhu, 2015).

Retweeting (where a user can post a tweet from a different account to their own timeline) is a

feature of Twitter that allows for the wide spread of information within a social network (Kwak, Lee,

Park, & Moon, 2010).

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Twitter updates can be categorized into four different categories: daily chatter,

conversations, sharing information/urls, and reporting news (Java et. al, 2007). These categories

offer insight into the different ways users employ Twitter, and show one way of distinguishing

between types of content, as well as contributing to an understanding of how and why users use

microblogging platforms (Java et. al, 2007). Identifying these different categories within mental

health information on Twitter can be helpful to understanding the nature of the discourse on the

platform.

Twenty-three percent of adult Internet users in the United States use Twitter (Duggan,

2015). Twitter has been the focus of academic studies on a range of health topics (Reavley &

Pilkington, 2014), offering a chance to analyze the thoughts and reactions of a large group of

people. Techniques such as topic modeling, text mining, and sentiment analysis can be used to

analyze large sets of data produced by the social networks that exist on social media (Yesha &

Gangopadhyay, 2015). Social media like Twitter can also be used to target certain demographics

(i.e., young people), and has strength in its ability to allow users to engage with the content

(Goodman, Wennerstrom, & Springgate, 2011).

The interconnectedness of Twitter also makes it effective as a social awareness tool (Park,

McDonald, Cha, 2013). Neiger et al. (2012) identified five purposes for social media in health

promotion: to offer market insights, establish a brand and create brand awareness, disseminate

critical information, expand reach to more diverse audiences, and foster public engagement and

partnerships. Understanding user responses to these different types of promotion could be

valuable to mental health organizations trying to accomplish specific goals through their Twitter

usage. Social networking sites like Twitter can offer the opportunity for organizations to encourage

communication and interaction surrounding health literacy (Park, Rodgers, & Stemmle, 2013).

HASHTAGS

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Hashtags (a term preceded by the ‘#’ symbol within a tweet that signals the tweet’s topic)

are an essential part of Twitter and Twitter conversation (Calvin, Bellmore, Xu, & Zhu, 2015). One

study looked at the content people posted using the #depression hashtag on Instagram (hashtags

are not a concept exclusive to Twitter), and discovered that people using that hashtag discussed

topics such as self harm, life and death, society, looks, support, relationships, negative emotions,

audience awareness, personal narrative, edibles, social self view, informational, beyond

appearance, illness, seeking, positive emotions, personal self view, and general activities

(Andalibi, Ozturk, & Forte, 2015). They also observed a sense of community identity within the

depression hashtag on Instagram (Andalibi, Ozturk, & Forte, 2015). A Twitter study on bullying

sought to identify, categorize, and determine characteristics of hashtags related to bullying

(Calvin, Bellmore, Xu, & Zhu, 2015). They also found that hashtags used as part of anti-bullying

campaigns were most likely to mention users, increasing the spread of anti-bullying information

through the network (Calvin et al., 2015). These studies were useful for showing types of

information associated with hashtags, by examining co-occurring hashtags, furthering the

understanding of what topics these hashtags are being used to discuss.

A study has been completed on the content of #depression (and #schizophrenia), looking

at the themes and attitudes toward the illness shown in the tweets using the hashtag (Reavley &

Pilkington, 2014). It used content analysis, and was able to determine what percentage of these

tweets were positive, neutral, and stigmatizing and what types of stigmatizations were being

perpetuated, which has implications for how advocacy organizations can fight stigma by targeting

certain misconceptions and attitudes (Reavley & Pilkington, 2014). They found that a majority of

tweets using #depression where either positive or neutral, with less than one percent of tweets

using a stigmatizing attitude (Reavley & Pilkington, 2014). They also found that the content of

most of the tweets in #depression focused on consumer resources and advertising services and

products to people with depression (Reavley & Pilkington, 2014).

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THEMESOFTWEETSThere have been a couple studies that use content analysis to determine the theme of

tweets in certain hashtags. Reavley & Pilkington identified’ the following themes from tweets

using hashtags for #depression and #schizophrenia: “(1) personal experience of mental illness, (2)

awareness promotion, (3) research findings, (4) resources for consumers (5) advertising, (6) news

media, and (7) personal opinion or dyadic interaction” (2014). Xu, Chiu, Chen, & Mukherjee

identified three themes: knowledge sharing, community, and action in a sample of twitter hashtags

pertaining to health (2015). This is a useful way to categorize tweets that share a hashtag

because it distinguishes between purposes for using that particular hashtag.

Martínez-Pérez, Torre-Díez, Bargiela-Flórez, López-Coronado, & Rodrigues (2014)

identified five types of depression groups found on social media, including Twitter: support groups,

informant groups, self-help groups, advocacy and awareness groups, and fund collecting groups

(p.5). Understanding the purpose behind different groups allows for an understanding of what the

content they create is meant to accomplish, which can assist mental health organizations in

targeting their campaigns and information to specific populations on Twitter, and in identifying how

those users interact with these different groups. Categorization of content will create an

understanding of what Twitter users discuss and how the tool is used to disseminate information.

USERINTERACTIONThere are differences in the way depressed and non-depressed people use Twitter.

Depressed people are less likely to interact with other users on Twitter via mentions and replies

than non-depressed people. (Park, McDonald, & Cha, 2013) Depressed users gravitated more

toward consuming positive emotional content on Twitter, while non-depressed users used Twitter

to consume information (Park McDonald, & Cha, 2013). A question raised by gaps in research on

social media health communications is how (or if) content related to health encourages

interactions between users engaging with the content, and what impact that could have on people

suffering from illnesses (Moorhead et al., 2013). Observing reactions to content in hashtags and

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identifying different themes within reactions to the themes observed within a particular hashtag

could be useful to understand how users are actually responding to the different themes of content

found on Twitter.

CHALLENGESFORSOCIALMEDIAANDHEALTHCAMPAIGNS While Twitter is a valuable tool for promoting different causes and sharing information, it is

not a perfect tool, and health campaigns (on social media and beyond) face many challenges.

One issue mental health campaigns face is uncertainty of the outcome of the campaign and the

tangible effect it will have on the people it targets. A survey on a Canadian mental health

campaign geared toward young people, particularly focusing on social media, but also employing

traditional media, found that the strength of media campaigns may be in increasing awareness

and educating people on an issue, and that media campaigns may be ineffective for changing

people’s attitudes toward mental illness (Livingston, Tugwell, Korf-Uzan, Cianfrone, & Coniglio,

2012). A review of depression and suicide campaigns (focused on traditional campaign methods)

suggested that they generally improved public awareness and acceptance of depression, though it

was unclear if they reduced suicide (Dumesnil & Verger, 2009). A study focusing on improving the

attitudes of young people toward mental illness suggested that mental health campaigns were

more effective for increasing awareness and literacy, but had less of an effect on de-stigmatizing

mental illness (Livingston et al., 2012). A Twitter study on breast cancer awareness during breast

cancer awareness month noted that the majority of the tweets in the campaign did not actually

promote specific preventative measures for breast cancer (Thackeray, Burton, Giraud-Carrier,

Rollins, & Draper, 2013). An additional challenge of using social media for health campaigns is

how to promote content to individuals that could benefit from it: how should the managers of these

accounts target individuals in need of depression support in a way that does not alienate them yet

still provides encouragement to take difficult actions that may aid in recovery (Munson, Cavusoglu,

Frisch, & Fels, 2013)?

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Together, this research suggests that there is uncertainty about the effectiveness of social

media for changing attitudes and behaviors regarding health. More research is necessary to

identify what factors make social media both effective and ineffective for spreading mental health

information, in order for mental health advocates to better understand how to maximize their

impact on social media, and make their campaigns effective for their platform and audience.

IMPLICATIONSFORRESEARCH Considering the negative health implications of depression, the prevalence and usefulness

of Twitter as a platform, and the uncertainty surrounding Twitter’s effectiveness as a means of

spreading mental health information, there are a plethora of research questions that could be

answered to further Twitter’s usefulness as a source of mental health information. With the

negative societal impact of depression, researching the hashtag used to discuss depression

(#depression) is beneficial for analyzing how Twitter is used to communicate information regarding

that particular mental illness. This could have implications for groups promoting awareness and

advocating for individuals with depression in that it would offer them insight on how to most

effectively use Twitter to assist in accomplishing their goals.

One recurring theme between studies was a need to understand what effect raising

awareness and campaigning online has on people and their behavior (Calvin et al., 2015;

Livingston et al., 2012). Twitter has a large user base, and an effective network for sharing

information, but that information needs to be targeted toward the right audiences and trigger

tangible change in that audience to be effective. In order for organizations to accomplish that goal,

it will be useful to understand how the information they tweet is received by their current audience.

By measuring audience attitudes toward the types of content these organizations tweet, mental

health advocacy groups can better understand the effects of their content.

In order to understand how to make Twitter content the most effective at achieving its

purpose, it first is important to understand what type of content exists in the #depression hashtag.

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By building on previous research identifying themes within hashtags, the most prevalent themes in

#depression can be identified, honing in on the most popular information-sharing purposes of the

hashtag. It will also be useful to identify other hashtags used within #depression, because

hashtags are way to indicate the topic of a tweet, thus offering insight into subtopics within the

hashtag #depression, further illuminating the purposes behind users employment of the hashtag

#depression (Calvin et al., 2015). This study thus asks the following research question:

RQ1: What hashtags co-occur with the #depression hashtag?

Synthesizing the work done in hashtag studies with the work done in studies focused on

the effectiveness of health information campaigns would help create a better understanding of

how users react to information within a hashtag like #depression. What themes gain the most

responses from users? How do users respond differently to different themes? What hashtags are

most commonly used with hashtag #depression, and what might that imply about the purposes

behind users conversing on Twitter about depression? This information would be invaluable to

mental health advocates seeking to use Twitter to spread beneficial information about depression

in that it would help them better understand the existing conversation and how they could target

their information to get a response from within that conversation. In order to fill in this gap the

literature leaves regarding user responses, this study poses the following research question:

RQ2: Which hashtags co-occuring with #depression are Twitter users most

interested in, and which hashtags co-occurring with #depression do they perceive as most

relevant to the topic of depression?

These two research questions guided the study I conducted, seeking to further the

understanding of the structure of the social network of the #depression hashtag on Twitter. This

includes examining the structure of the hashtag, mention, and retweet social networks in order to

find patterns in how Twitter users communicate with each other within the hashtag. It also includes

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observing how users respond to popular hashtags co-occurring with #depression, based on

interest and perceived relevance to depression.

METHODS

SOCIALNETWORKANALYSIS In order to examine the themes that emerge from Twitter accounts raising awareness for

mental health and depression, I used a data-driven collection and analysis procedure to study my

research questions, using established practices in mixed-methods social scientific analysis of

social media trace data.

Social network analysis is useful for finding and making sense of social ties among actors,

and exploratory social network analysis has no set hypothesis about the patterns found in a social

network, deriving meaning from patterns and structures found between the social ties (De Nooy,

Mrvar, & Batagelj, 2011). Exploratory social network analysis involves four elements: network

definition, manipulation of the network, calculations to quantify the structure of the network, and

visualization of the network (De Nooy, Mrvar, & Batagelj, 2011). Using this technique helped

identify the structure of the network created between hashtags used with the hashtag

#depression. It helped identify the structure of networks between users who employed this

hashtag (and the other hashtags with it) to communicate their thoughts on depression to other

users. Through exploratory network analysis, I observed how users communicated with each

other within the hashtag #depression.

Data was collected from the Twitter API using the python programming language to collect

186,469 tweets posted with the #depression hashtag from October 2nd, 2016 to January 10th,

2017. One restriction on Twitter data collection is that amount of tweets collected from the API is

limited to a randomized sample of the full stream of public Tweets (Twitter Developer

Documentation, n.d.). I decided to keep non-English tweets and hashtags in the dataset in order

to accurately understand the conversation taking place within #depression. To format and analyze

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the data, I used and wrote python code in order to select elements (described below) that were

significant in understanding the pattern of the network. When a user only used a hashtag once,

mentioned a user once, or retweeted a user once that particular connection was excluded from

the social network because it is not relevant to identifying patterns within the network. This

prepared the data for analysis and visualization.

I used the exploratory social network analysis research methodology in order to interpret

the structure of interactions (who replied to whom, who retweeted whom, who mentioned whom,

etc.) in the data using the NetworkX python package and the program Cytoscape for social

network analysis and visualization. I used Exploratory Social Network Analysis with Pajek as a

reference to accomplish this (2011).

SURVEY

I also developed a qualtrics survey to see how interested Twitter users were in the 20 most

popular hashtags co-occurring with the hashtag #depression, as well as how related to the topic of

depression they thought those 20 co-occurring hashtags were. This was for the purpose of

determining user attitudes toward hashtags. I administered the survey to a population of Twitter

users (N = 117). I recruited student participants via social media, Florida State University-related

Facebook groups, classes at Florida State University, and an Association of Information

Technology Professionals student meeting. Survey respondents were asked to rank a list of ten1

randomly selected hashtags from the top twenty most popular hashtags found within the network

in order of personal interest and in order of relatedness to depression. The top 20 hashtags in this

survey differ slightly from the top 20 hashtags in Table 1 in Appendix A because the survey drew

from the top 20 overall, while the list in Table 1 excluded certain hashtags. I gave each Twitter

1Onlytenhashtagswereshowntorespondentsbecauserankingtwolistsoftwentyhashtagswouldputaheavycognitiveloadontherespondents,makingitmoredifficultforthemtoremainwillingtocompletethesurvey.

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user participant a $5 Amazon gift card for completing the survey. See Appendix B for the

demographics of the survey respondents.

SUMMARYOFMETHODSResearch Question Method Expected Results RQ1: What hashtags co-occur with the #depression hashtag?

Exploratory Social Network Analysis A list of hashtags co-occurring with #depression, a list of users mentioning each other within #depression, a list of users retweeting each other within #depression, visualizations of the hashtag, mention, and retweet social networks

RQ2: Which hashtags co-occuring with #depression are Twitter users most interested in, and which hashtags co-occurring with #depression do they perceive as most relevant to the topic of depression?

Survey asking Twitter users to rank hashtags co-occurring with #depression

Average rankings for the top 20 hashtags used within #depression for interest and relevance to depression

RESULTS This study observed data from the #depression hashtag. Within the data, the top co-

occurring hashtags with #depression were observed. The most mentioned and retweeted

accounts were also identified, along with the accounts doing the most mentioning and retweeting.

A survey was conducted to identify how the top twenty hashtags in the dataset were perceived by

Twitter users. The survey asked Twitter users to rank a random selection of 10 of the top 20

hashtags from what thought was the most interesting hashtag to what they thought was the least

interesting hashtag. They were also asked to rank a random selection of 10 of the top 20 hashtags

from what they thought was the most relevant hashtag to depression to what they thought was the

least relevant hashtag to depression.

The averages of these rankings were measured to get an idea of which hashtags were

found to be more relevant or interesting or less relevant or interesting. #love, #exercise, and

#treatment were found to be the most interesting hashtags to the Twitter users who took the

survey. For the category of relatedness to depression, #depression, #dépression, #mentalhealth,

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#mentalillness, and #mindfulness were found to be the hashtags perceived as most relevant to

depression to the Twitter users who took the survey.

RQ1: What hashtags co-occur with the #depression hashtag?

Figure 1 is a visualization of the hashtag social network found within the dataset. This

looks at hashtags used by Twitter users with #depression in their tweets. The nodes represent the

hashtags in the data set and the users in the dataset. As observed in Figure 1, the majority of the

hashtags are located in the main component of the network, with very few outside the main

component within smaller components. The main component shows the majority of interactions

within the network and is the focus of the analysis since it can be explored for patterns more

readily than a component consisting of a single user using a single hashtag twice. Table 1 (found

within Appendix A) describes the indegree of each hashtag within this network, describing the top

50 hashtags co-occurring with #depression. Table 2 (found within Appendix A) describes the

quantity of hashtags used by the top 50 users.

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Figure 1

Table 3 (found within Appendix A) lists the top 51 twitter users in order of indegree for the

mention social network. The indegree represents the number of times the top users are mentioned

by other users. The outdegree represents the number of times they mention other users. The

edge count is the total number of times they are mentioned by other users and that they mention

other users.

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Figure 2 - Visualization of mention social network with node size corresponding to indegree

Table 4 (in Appendix A) lists the top 50 Twitter accounts in order of outdegree. The

outdegree represents the number of times an account mentioned another user. The indegree

represents the number of times they were mentioned by another user. The edge count represents

the combined total of the two metrics.

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Figure 3 - Mention social network with node size corresponding to outdegree

Table 5 (found within Appendix A) lists the top 51 users with the highest indegree in the

retweet network. The top 51 users are listed instead of the top 50 due to a tie for the 50th highest

indegree. The indegree represents the number of times a user was retweeted. The outdegree

represents the number of times they retweeted other users. The edge count represents the total

number of times they were retweeted and the number of times they retweeted other users.

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Figure 4 - The retweet social network with node size corresponding to indegree

Table 6 (found within Appendix A) organizes the top 49 users by outdegree. The top 49

users are listed instead of the top 50 due to a tie for the 50th highest outdegree bringing the

number of users listed well over 50. Outdegree represents the number of times a user retweeted

another user. Indegree represents the number of times that user was retweeted by another user.

Edge count represents the total number of times a user was both retweeted by another user and

retweeted other users.

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Figure 5 - This shows the retweet social network with node size corresponding to outdegree

RQ2: Which hashtags co-occuring with #depression are Twitter users most interested in,

and which hashtags co-occurring with #depression do they perceive as most relevant to

the topic of depression?

“Are you a Twitter user?” was the first question asked in the survey. Table 7 shows the

responses to this question. It was intended to make sure that all survey respondents were Twitter

users. The two respondents who were not were automatically taken to the end of the survey.

Refer to Appendix B for more information on the demographics of the survey.

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Table 7

Answer % Count

Yes 98.29% 115

No 1.71% 2

Total 100% 117

Survey Question 1: Rank these hashtags in order of which topics you would be most

interested in seeing on Twitter (1 being most interested and 10 being least interested).

Twitter users found #love, #exercise, and #treatment to be the most interesting hashtags

out of the top twenty. #braininjury and #cte were ranked as the least interesting hashtags.

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Table 8

Hashtag Minimum Maximum Mean Count #depression 1 9 5.3 50 #anxiety 1 10 5.59 51 #mentalhealth 1 10 5.17 58 #ptsd 1 10 6.14 58 #stress 1 10 5.46 52 #suicide 1 10 6.47 60 #mentalillness 2 10 5.93 60 #health 1 10 5.38 47 #bipolar 1 10 6.02 51 #braininjury 1 10 6.66 59 #mentalhealthmatters 1 10 5.25 52 #mindfulness 1 10 5.36 59 #letstalk 1 10 5.23 57 #exercise 1 10 4.66 50 #pain 1 10 5.65 62 #love 1 10 3.22 55 #therapy 1 10 5.2 55 #cte 1 10 6.73 59 #dépression 1 10 5.43 46 #treatment 1 10 4.9 69

Table 9

Rank Hashtag Average Rank 1 #love 3.22 2 #exercise 4.66 3 #treatment 4.9 4 #mentalhealth 5.17 5 #therapy 5.2 6 #letstalk 5.23 7 #mentalhealthmatters 5.25 8 #depression 5.3 9 #mindfulness 5.36

10 #health 5.38 11 #dépression 5.43 12 #stress 5.46 13 #anxiety 5.59 14 #pain 5.65 15 #mentalillness 5.93 16 #bipolar 6.02 17 #ptsd 6.14 18 #suicide 6.47 19 #braininjury 6.66 20 #cte 6.73

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Survey Question 2: Rank these hashtags in order of which topics are most relevant to the

subject of depression (1 being most relevant and 10 being least relevant).

#depression, #dépression, #mentalhealth, #mentalillness, and #mindfulness were ranked

as the most relevant hashtags to the subject of depression. #cte, #braininjury, and #love were

ranked as the least relevant hashtags to depression.

Table 10

Hashtag Minimum Maximum Mean Count #depression 1 10 3.14 63 #anxiety 1 10 5.6 55 #mentalhealth 1 10 3.67 60 #ptsd 1 10 6.52 52 #stress 1 10 5.6 57 #suicide 1 10 6.04 53 #mentalillness 1 10 4.05 57 #health 1 10 5.36 53 #bipolar 2 10 6.33 58 #braininjury 2 10 7.02 55 #mentalhealthmatters 1 10 5.41 54 #mindfulness 1 10 4.84 50 #letstalk 1 10 6.31 59 #exercise 1 10 6.17 59 #pain 2 10 6.66 61 #love 1 10 7.11 45 #therapy 1 10 5.38 52 #cte 1 10 6.92 59 #dépression 1 10 3.32 60 #treatment 1 10 5.21 48

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Table 11

Rank Hashtag Average Ranking 1 #depression 3.14 2 #dépression 3.32 3 #mentalhealth 3.67 4 #mentalillness 4.05 5 #mindfulness 4.84 6 #treatment 5.21 7 #health 5.36 8 #therapy 5.38 9 #mentalhealthmatters 5.41 10 #anxiety 5.6 11 #stress 5.6 12 #suicide 6.04 13 #exercise 6.17 14 #letstalk 6.31 15 #bipolar 6.33 16 #ptsd 6.52 17 #pain 6.66 18 #cte 6.92 19 #braininjury 7.02 20 #love 7.11

DISCUSSION Based on the results, several conclusions can be drawn from the data. It is apparent that

the social network that exists within #depression covers a variety of topics, such as other mental

illness, health conditions related to depression, activities and strategies for coping with

depression, raising awareness about mental health, and several other miscellaneous hashtags.

There are also a variety of accounts interacting within the hashtag. Personal accounts, mental

health organizations, businesses, and other miscellaneous categories all use #depression,

showing that diverse types of users take part in the conversation within #depression. From the 20

most popular hashtags within the network, the results of the survey show which terms are most

interesting to Twitter users, and which terms Twitter users perceive as most relevant to

depression. This can help determine what subjects relevant to mental health Twitter users may be

interested in, and can also help determine what hashtags Twitter users would associate with

depression and look to for depression-related information.

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RQ1: What hashtags co-occur with the #depression hashtag?

As shown in Figure 1, the main hashtag component does not show much stratification

along category lines. Users of #depression have varied interests in the content they tweet about.

There were several types of words found in the hashtag dataset, including conditions related to

mental illness, other mental illnesses, words related to treatment, positive sentiments, and

hashtags designed to raise awareness. This suggests the users of #depression have a variety of

interests when it comes to depression discussion.

Within the top five accounts that used the most hashtags, three were personal and two

were organizational. Within the top five most-mentioned accounts, one was a personal/celebrity

account, three were organizational, and one was for a website/blog. Within the top five accounts

for most mentioning, one account was for a treatment center, one was for a business that sells

supplements, and the other three were personal. Within the top five most retweeted accounts, one

was a personal/celebrity account, three were organizational accounts, and one was an account for

a website/blog. Within the top five accounts doing the most retweeting, one was a treatment

center’s account, one was a business’s account, and the other three were personal.

RQ2: Which hashtags co-occuring with #depression are Twitter users most interested in, and

which hashtags co-occurring with #depression do they perceive as most relevant to the topic of

depression?

113 users ranked a random selection of 10 of the 20 most used hashtags from the

#depression dataset in order of what they were most interested in and what they thought was

most related to depression. The rankings were placed on a scale 1-10, 1 being most interesting or

related to depression and 10 being least interesting or related to depression. The average rank of

each hashtag can be referenced in Table 9 and Table 11.

For the category of interest, #love, #exercise, and #treatment were the only three rankings

with an average ranking less than 5. All of these words are relatively positive in connotation. #love

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was particularly favored, with an average ranking of 3.22. Love may be perceived as a positive or

enjoyable topic to discuss, appealing more to Twitter users.

For the category of relatedness to depression, #depression, #dépression, #mentalhealth,

#mentalillness, and #mindfulness were the hashtags perceived as most relevant to depression.

This suggests that depression is indeed the term that Twitter users would seek out when looking

for information related to depression. The hashtags #mentalhealth and #mentalillness also had

perceived relevance, perhaps because they are broader terms that include depression and

concepts related to depression. Twitter users may expect that these broader terms might include

information relevant to depression.

The hashtags #braininjury and #cte (an abbreviation forchronic traumatic encephalopathy)

were the ranked both as less interesting to Twitter users and less relevant to the subject of

depression two hashtags on both lists, suggesting that they are not what Twitter users are

interested in, nor are they perceived as relevant to depression. These hashtags may be targeting

a more niche audience, but are not the most effective hashtags for reaching an audience of

college-aged Twitter users.

The hashtag #love had an average ranking of 3.22 in the category of interest, but only had

an average ranking of 7.11 in the category of relatedness to depression. This suggests that while

Twitter users may be most interested in #love, they do not perceive #love as relevant to

depression, and therefore are interested in the hashtag for other reasons.

More research is necessary to understand which hashtags and accounts are most effective

for reaching specific users. This study is limited in that the sample is not representative of the

population of Twitter users as a whole, nor is it representative of any particular group mental

health advocates may want to reach. In a future study, it would be helpful to understand the why

Twitter users find a particular hashtag more interesting or relevant. It would also be helpful to

specifically survey a Twitter population interested in depression-related content to see which

26

hashtags they found most relevant to depression. A sample more targeted toward the interests of

mental health advocates would allow for an understanding of the audiences they are trying to

reach. It would also be beneficial to research what effect, if any, different depression-related

hashtags have on user awareness and behavior. The effect that these hashtags have on Twitter

users was beyond the scope of this study, but that information would be helpful in maximizing

Twitter’s effectiveness as a tool for raising awareness about depression.

CONCLUSION In conclusion, the data and network visualizations created from that data represent the

current understanding of the structure of the social networks for hashtags, mentions, and retweets

within the hashtag #depression. The hashtags within the main component of the network are not

separated into clusters along category lines. Positive terms like #love and #exercise are most

interesting to the sample of Twitter users surveyed, though not necessarily for depression related

reasons. Terms explicitly related to depression, like #depression and #dépression, are perceived

as being most related to depression, suggesting that using the most direct hashtag may be the

best way to connect Twitter users to depression-related tweets, if they are seeking that out.

This study has taken a look at the hashtag content found within #depression. It identified

hashtags co-occurring with #depression, useful information to determining what topics emerge in

the Twitter conversation within #depression. This study also identified hashtags frequently used

with #depression that Twitter users may be interested in (#love, #exercise, #treatment),

information that can be useful for raising awareness about depression. This study also identified

hashtags related to topics that Twitter users might expect to find with depression-related

information on Twitter (#depression, #dépression, #mentalhealth, #mentalillness, and

#mindfulness). This is helpful for understanding what topics Twitter users perceive as relevant to

depression, information that can be useful for raising awareness about depression. Ultimately, this

study contributed to the understanding of the topics of discussion within the #depression hashtag

27

and the understanding of user perceptions of those hashtags. This understanding can benefit

mental health advocates as they use Twitter as a platform for their advocacy.

Moving forward, there are many directions future studies could move in. It would be

beneficial to get a more representative sample of Twitter users to take the survey to understand

what the target audience of mental health advocates on Twitter may think. A similar study could

be conducted for hashtags related to different mental illnesses, such as #anxiety or #bipolar, and

the results could be compared and contrasted to #depression. It could also be beneficial to

observe the content of the tweets beyond the hashtags to get an even better understanding of the

discourse within #depression. It would also be beneficial to research how both the hashtags and

the content of tweets within #depression affect user awareness about depression.

28

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APPENDIXATable1

Hashtag Indegree depression 19803 mentalhealth 3514 anxiety 3329 bipolar 548 health 539 mentalillness 530 ptsd 495 braininjury 490 suicide 467 tstress 445 mentalhealthmatters 272 mindfulness 254 pain 243 faradarmani 214 freetaheri 211 iran 211 cte 207 meditation 183 healing 183 letstalk 178 tbi 176 notjustsad 168 turmeric 146 selfconfidence 143 spiritual 139 love 135 hope 134 sad 134 depressed 131 ocd 130 exercise 129 healthtips 120 fear 116 endthestigma 115 psychology 113 healthyliving 109 drink 108 homemade 108 dépression 108 bpd 106 wellness 106 therapy 105 adhd 105 cancer 104 addiction 100 bipolardisorder 96 recovery 95 yoga 94 allergies 92 poetry 91

33

Table2

Twitter Account Outdegree antsared 129 healthyplace 96 dlhampton 90 myeobd2 78 counsellorscafe 65 itssanaaaa 64 bekalombardo 63 theclearingnw 63 250healthyfoods 56 mhtuz 53 buzzurbroadcast 51 zarstardesigns 51 ourmilkmoney 51 voices4changerj 50 emilyspeaks30 49 thelombardos 49 thepokeyhat 47 moodcards 44 mrperfectau 43 healingmb 41 dysthymicdad 40 cynchazen 40 antistigma 40 truehopeempower 39 waltika 39 garyhgoodridge 37 stuartfawcett1 37 powerslave1974 37 schatzieb 36 industrialplay7 36 thefavelakid 35 lbackpainrelief 35 ichliebebillah 35 judehaste_write 35 jeffsnider_aip 35 stuckwitme 35 livinginstigma 34 scwilsonaus 34 livehealingly 34 themoodcards 33 unqualified0119 32 relaxintuit 31 weissjsw819 30 amandagreenuk 30 respectyourself 30 luvplanet_earth 29 meditationcdn 29 organiclivefood 29 joelboggess 29 eat2beatdisease 29

34

Table3

Twitter Account Indegree Outdegree EdgeCount garyhgoodridge 619 8 627 who 306 1 307 charitysane 302 9 311 rethinkdep 296 1 297 healingmb 292 7 299 stuartfawcett1 229 2 231 ahealthblog 223 1 224 spowersincia 179 1 180 followbdt 139 7 146 moodcards 114 3 117 naturalnfit 107 0 107 sadhgurujv 105 0 105 myselfandhealth 85 0 85 vargasstl 78 4 82 healthranger 73 0 73 theoneproj 68 2 70 themightysite 66 3 69 healthyplace 56 7 63 timetochange 55 0 55 namicommunicate 53 1 54 coletta_ldamage 50 0 50 healthwomeninfo 49 1 50 respectyourself 47 20 67 marshawright 44 1 45 neurosciencenew 44 0 44 blurtalerts 41 17 58 look_human 39 0 39 electroboyusa 37 10 47 drdenisemd 35 3 38 natasha_tracy 32 6 38 anxietyaches 31 0 31 depresslonus 31 8 39 counsellorscafe 30 2 32 thebraindriver 29 1 30 dysthymicdad 29 44 73 good_therapy 29 0 29 youtube 28 0 28 affectminds 28 2 30 usycool1 26 1 27 findpeterponce 25 4 29 adele 25 0 25 mooddisordersca 25 3 28 amandagreenuk 25 2 27 bekalombardo 24 10 34 truehopeempower 23 28 51 antsared 23 13 36 mylifenbalance 23 2 25 tlllfoundation 22 0 22 intlbipolar 22 3 25 menningerclinic 22 1 23 allentien 22 1 23

35

Table4

Twitter Account Indegree Outdegree EdgeCount theclearingnw 9 77 86 dysthymicdad 29 44 73 truehopeempower 23 28 51 itssanaaaa 0 28 28 hilaryjhendel 11 23 34 myeobd2 0 22 22 respectyourself 47 20 67 cynchazen 2 18 20 unqualified0119 2 18 20 blurtalerts 41 17 58 mhtuz 7 17 24 medicalbriefza 0 17 17 thepokeyhat 0 15 15 harassnomore 2 14 16 shirleyalexis 0 14 14 antsared 23 13 36 briang61uk 0 13 13 seenandheard2o1 7 12 19 fewingsbj 0 12 12 patriciasinglet 0 12 12 gennextvoice 6 11 17 amessofchaos 0 11 11 electroboyusa 37 10 47 bekalombardo 24 10 34 jbuchana 4 10 14 mondayblogs 1 10 11 antistigma 0 10 10 theramindsb 0 10 10 laural2920 0 10 10 charitysane 302 9 311 voices4changerj 5 9 14 sherrig108 3 9 12 thelombardos 2 9 11 stevesasc 2 9 11 awarenessfairy 1 9 10 grace_durbin 0 9 9 cowboyscifibot 0 9 9 wyp_da 0 9 9 borovay_robert 0 9 9 garyhgoodridge 619 8 627 depresslonus 31 8 39 shawnleek7x7x7 1 8 9 powerslave1974 1 8 9 aprilrhynold 1 8 9 daddypantz1220 0 8 8 birderronindork 0 8 8 healingmb 292 7 299 followbdt 139 7 146 healthyplace 56 7 63 brendaturmel 5 7 12

36

Table5

Twitter Account Indegree Outdegree EdgeCount garyhgoodridge 614 5 619 rethinkdep 289 0 289 healingmb 289 5 294 charitysane 285 1 286 who 256 0 256 stuartfawcett1 229 2 231 ahealthblog 217 0 217 spowersincia 146 0 146 followbdt 128 0 128 sadhgurujv 104 0 104 myselfandhealth 83 0 83 vargasstl 74 1 75 healthranger 72 0 72 timetochange 52 0 52 healthwomeninfo 47 1 48 healthyplace 47 4 51 namicommunicate 45 0 45 marshawright 44 1 45 neurosciencenew 42 0 42 moodcards 36 0 36 drdenisemd 33 2 35 electroboyusa 32 5 37 counsellorscafe 28 2 30 anxietyaches 28 0 28 affectminds 28 1 29 dysthymicdad 27 19 46 usycool1 26 1 27 findpeterponce 25 3 28 amandagreenuk 25 2 27 good_therapy 24 0 24 antsared 23 10 33 mooddisordersca 22 2 24 allentien 22 1 23 bekalombardo 21 1 22 natasha_tracy 21 1 22 kellihatha1 21 4 25 jenniferelm 20 2 22 depressionroots 19 0 19 xephyr5 18 0 18 mentalhlthaware 18 0 18 intlbipolar 17 0 17 medscape 16 0 16 ishafoundation 16 0 16 psychdrugskill 16 0 16 stampstigma 15 2 17 dlhampton 15 7 22 dbsalliance 15 0 15 bibliosblood 14 0 14 bell_letstalk 14 0 14 adamweitz 14 0 14 depresslonus 14 5 19

37

Table6

Twitter Account Indegree Outdegree EdgeCount itssanaaaa 0 32 32 truehopeempower 13 22 35 dysthymicdad 27 19 46 myeobd2 0 19 19 cynchazen 2 15 17 thepokeyhat 0 14 14 respectyourself 0 14 14 blurtalerts 0 13 13 unqualified0119 1 12 13 harassnomore 2 11 13 amessofchaos 0 11 11 briang61uk 0 11 11 antsared 23 10 33 theramindsb 0 10 10 fewingsbj 0 10 10 antistigma 0 8 8 laural2920 0 8 8 grace_durbin 0 8 8 borovay_robert 0 8 8 patriciasinglet 0 8 8 dlhampton 15 7 22 awarenessfairy 1 7 8 shopindiatimes 0 7 7 daddypantz1220 0 7 7 laynielouxu 0 7 7 shirleyalexis 0 7 7 cowboyscifibot 0 7 7 sherrig108 0 7 7 mhealthmatters 4 6 10 seenandheard2o1 0 6 6 mondayblogs 0 6 6 rjber15 0 6 6 wyp_da 0 6 6 garyhgoodridge 614 5 619 healingmb 289 5 294 electroboyusa 32 5 37 depresslonus 14 5 19 gennextvoice 6 5 11 kipperny42 2 5 7 aprilrhynold 1 5 6 rcrockett 0 5 5 richings50 0 5 5 mekhappyface 0 5 5 mymntlhealth 0 5 5 silenciomusic 0 5 5 wishowdownhero 0 5 5 samosamaniac 0 5 5 claudioalpalice 0 5 5 joannetan_mbacp 0 5 5

38

APPENDIXBWhat gender do you identify with?

Gender % Count

Male 65.18% 73

Female 34.82% 39

Other 0.00% 0

Total 100% 112

39

What is your ethnicity?

Race % Count White 85.71% 96 Black or African American 7.14% 8 American Indian or Alaska Native 1.79% 2 Asian 1.79% 2 Native Hawaiian or Pacific Islander 0.00% 0 Hispanic or Latino 5.36% 6 Other 0.89% 1 Total 100% 112

What is your age?

Field Minimum Maximum Mean Std Deviation

Variance Count

Age 18 37 21.97 2.43 5.88 112

40

What is your major?

Major

Finance

Accounting

Civil Engineering

Biological Systems Engineering

Biological Sciences

Biochemistry

Athletic Training

Architecture

Architectural Engineering

Applied Science

Applied Climate Science

Anthropology

Animal Science

Agronomy

Agricultural Engineering

Agribusiness

Actuarial Science

Software Engineering

Plant Biology

Nutrition

Microbiology

Mechanical Engineering

Integrated Science

Insect Science

Grazing Livestock Systems

Geography

Geology

Food Technology for Companion Animals

Food Science

41

Fisheries

Environmental Studies

Electronics Engineering

Electrical Engineering

Construction Engineering

Computer Science

Computer Engineering

Communication Studies

Civil Engineering

Biological Systems Engineering

Biological Sciences

Biochemistry

Athletic Training

Architecture

Architectural Engineering

Applied Science

Applied Climate Science

Anthropology

Animal Science

Agronomy

Agricultural Engineering

Agribusiness

Actuarial Science

manager

Computational Sciences

computer science

Microbiology

Botany

computer science

computer science

Finance

Chemical engineering

42

Biology

Computer science

Accounting

Finance

Chemistry

Communications

Communications

Economics

Mathematics

Chemistry

Biology

Economics

Computer Science

Finance

Chemistry

IT

Editing Writing and Media

Media and Communication Studies

Information technology

ICT

Marketing

Communication disorders

ICT

Computer Engineering

humanities

Information Technology

Media Communication Studies

Media

Information Technology

Computer Science

Editing Writing and Media

ICT

43

editing writing and media

Communications and Sociology

Computational Science

ICT

psychology

ICT and EWM

Management

ICT

Media communication studies

Psychology

Spanish

Social work

Editing Writing and Media

history

Psychology

Theatre

Prebiological Science