Proceedings of the 5th Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pages 70–81
Barcelona, Spain (Online), December 12, 2020.
Exploring Online Depression Forums via Text Mining: A Comparison of
Reddit and a Curated Online Forum
Media Informatics Group
University of Regensburg
Media Informatics Group
University of Regensburg
Media Informatics Group
University of Regensburg
Media Informatics Group
University of Regensburg
We present a study employing various techniques of text mining to explore and compare two
different online forums focusing on depression: (1) the subreddit r/depression (over 60 million
tokens), a large, open social media platform and (2) Beyond Blue (almost 5 million tokens),
a professionally curated and moderated depression forum from Australia. We are interested in
how the language and the content on these platforms differ from each other. We scrape both
forums for a speciﬁc period. Next to general methods of computational text analysis, we focus
on sentiment analysis, topic modeling and the distribution of word categories to analyze these
forums. Our results indicate that Beyond Blue is generally more positive and that the users
are more supportive to each other. Topic modeling shows that Beyond Blue’s users talk more
about adult topics like ﬁnance and work while topics shaped by school or college terms are more
prevalent on r/depression. Based on our ﬁndings we hypothesize that the professional curation
and moderation of a depression forum is beneﬁcial for the discussion in it.
In recent years, online forums and communities have become an important outlet for growing numbers
of people who struggle with depression. For example, the subreddit r/depression on Reddit had a growth
from around 100,000 members in 2015 to over 570,000 in 20201. While a few decades ago, affected
would have had to ﬁnd support or self-help groups in their local area, they are now able to talk about
depression and share their stories with thousands of like minded individuals from the comfort of their
own home. Besides r/depression, there are traditional forums focusing on mental health that offer people
a place to talk about their condition. Some of these are professionally curated as well, providing support
hotlines, ties to experts in psychiatry and further articles on important topics.
In the following paper, we want to investigate how language and content of a platform like r/depression
differs from a traditional and professionally accompanied forum and how we can explore these questions
with the support of text mining methods. Speciﬁcally, we compare content and language through various
text mining methods, using word frequencies, topic modeling, word categories as well as sentiment
analysis. We regard the project at the moment as descriptive and explorative, therefore we do not want
to validate concrete hypothesis but rather explore methods and data to formulate potential hypotheses for
future work. While there is research applying computational text analysis on depression forums, we are
not aware of similar research focusing on the comparison of a curated and a non-curated forum.
This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http://
2 Related Work
First, we shortly describe the main text mining methods we apply in this study: Sentiment analysis is the
computational method to analyze the sentiment expressed towards entities, mostly in written text (Liu,
2016). The main application area of sentiment analysis is user generated content on the web like social
media (Hutto and Gilbert, 2014; Schmidt et al., 2020b) or movie reviews (Kennedy and Inkpen, 2006).
Next to sophisticated machine learning approaches, there are also rule-based approaches working with
lexical resources and simple rules (Taboada et al., 2011; Schmidt and Burghardt, 2018). Unsurprisingly,
sentiment analysis is a popular method to explore depression and social media. Wang et al. (2013) utilize
lexicon-based sentiment analysis to calculate the chance for depression on micro-blogs. IBirjali et al.
(2017) predict suicidal ideation in Twitter data via machine learning based sentiment analysis. Davcheva
et al. (2019) explore three English-language mental health forums via sentiment analysis. Sentiment
scores of users develop depending on several conditions (e.g. how active the user is).
To analyze linguistic and semantic word categories, we use the Linguistic Inquiry and Word Count
(LIWC) dictionary (Pennebaker et al., 2015). It consists of multiple linguistic categories like 1st person
singular, pronouns or adjectives as well as rather semantic and psychological categories like anxiety,
female/male language or spirituality and a corresponding list of words that have been demonstrated to be
connoted with this category. LIWC is an established and trusted resource in psychological investigations
(Lee et al., 2015) but is also applied in areas outside of psychology (Schmidt et al., 2020a). Findings
with LIWC include that people with depression use ﬁrst person singular and negatively biased words
more frequently than a control group (Lee et al., 2007).
Topic modeling is a method to create topical categories based on text documents without a priori
subject deﬁnitions (Jockers, 2013). We apply Latent Dirichlet Allocation (LDA) topic modeling (Blei et
al., 2003), which is one of the most established topic modeling approaches (Jockers, 2013). A topic is
a list of words that frequently occur with each other in a set of documents. Given a number of expected
topics, a LDA model produces lists of such words as a result. LDA models have also already been utilized
to examine the use of language of depressive individuals. For example, Resnik et al. (2015) examined
the use of supervised topic models in the analysis of linguistic signals for detecting depression.
There is numerous research examining depression and depression communities in social media cf.
(Conway and O’Connor, 2016). De Choudhury and De (2014) apply various techniques and examine
multiple mental health communities on Reddit. Among other, they characterize mental health social
support into the categories ”emotional”, ”informational”, ”prescriptive” and ”instrumental”, of which
”emotional” and ”prescriptive” are more likely to occur. De Choudhury and Kiciman (2017) continued
their work by focusing on how the language of comments inﬂuences risk to suicidal ideation. Park
and Conway (2017) use the LIWC dictionary to analyze r/depression and other health related forums
to investigate user based longitudinal changes. They show that users with a long-term participation
shifted to the use of more positive language and indicated that this leads to positive effects. Fraga et al.
(2018) explore multiple mental health subreddits via discourse pattern analysis and found that the longest
discussions are initiated by threads asking for help and that encouragement words are a frequent pattern.
3 Methods and Results
3.1 Data Acquisition and Corpora
For more information and access to parts of the used corpus, visit
3.1.1 Subreddit r/depression
r/depression2is an English-speaking subreddit and consist of submissions, also called threads in other
forums. Such submissions include a title and the initial content written by the author. Answers to a
submission are called comments. Scraping of /r/depression was done using the Pushshift.io API Wrapper
Metric Beyond Blue r/depression
Threads/Submissions 3,922 131,073
Answers/Comments 24,500 715,128
Posts (Threads+ Answers) 28,422 846,201
Tokens 4,982,391 60,632,208
Tokens per post 175.3 71.7
Tokens per initial post 229.2 186.0
Tokens per answers 166.7 50.7
Tokens per sentence 12.6 11.3
Comment/submission ratio 6.2 5.5
Table 1: Corpus Metrics
psaw 3and the Python Reddit API Wrapper praw 4.
1,007,134 posts (submissions and comments) from all submissions created in 2019 were gathered
between 02.04.2020 and 02.20.2020. Due to the much smaller corpus available for Beyond Blue, we
decided to limit the extraction to a single year, to keep the corpora comparable to some extent but still
decided to include an entire year to avoid inﬂuences due to seasonal changes. 2,295 posts were ﬁltered
out due to lack of content, like empty posts or ones that only contain a link. Another 158,638 posts were
not considered because they were already deleted by the author himself, moderators or spam ﬁlter. This
leads to 846,201 posts consisting of 131,073 submissions (15.5%) and 715,128 comments (85.5%).
3.1.2 Beyond Blue
”Beyond Blue” is a non-proﬁt organization funded by Australian governments and states (Jorm et al.,
2005) and offers an English-speaking forum speciﬁcally for depression5. Beyond Blue offers support
ranging from their website featuring many up to date articles on mental wellbeing and a welcoming
atmosphere, to a 24-hour hotline, online chat, several different well-tailored forums and special informa-
tion on current topics. We acquired permission by the Beyond Blue research team to scrape the content
of their forum.
Beyond Blue was scraped using Python with the standard libraries lxml and urllib. The basic forum ar-
chitecture is similar to Reddit, however, submissions are called threads and posts (equivalent to Reddit’s
comments) and cannot be nested, but may have a note referring to which post they are an answer. The
only metadata available in the forum are the threads title and, respectively for every post, text, date and
user information. Opposing to r/depression, we gathered all available threads and metadata in Beyond
Blue, which still results in a noticeably smaller corpus, that contains posts from April 3rd 2013 until
January 12th 2020. This results in 28,422 posts, of which 3,922 are initial posts (13.8%) of threads and
24,500 are answers inside threads (86.2%).
3.2 General Corpus Analysis
We used SpaCy6as central tool for the general corpus analysis. Table 1 illustrates general corpus statis-
The Beyond Blue corpus consists of 4,982,391 tokens divided among 395,544 sentences. Considering
the Beyond Blue corpus contains a total of 28,422 posts, this results in 175.3 tokens per post on average.
There is a notable difference between the average token counts of initial posts of a thread (229.2) and
answers (166.7). The average token count of a sentence is 12.6. Combining these results with the distri-
bution of all posts into submissions and comments, the submissions (13.8% of all posts) are responsible
for 18.0% of the tokens. r/depression contains a total of 60,632,208 tokens within 5,369,000 sentences.
Figure 1: Twenty most frequent words of Beyond Blue
A post on r/depression contains 71.7 tokens on average. Splitting the posts into submissions and com-
ments, the average token count is 186.0 for a submission and 50.7 for a comment. An average sentence
contains 11.3 tokens. The submissions of r/depression, which make up 15.5% of all posts, contain 40.2%
of all tokens.
Looking at the amount of submissions and comments, the comment/submission-ratios of 6.2 (Beyond
Blue) and 5.5 (r/depression) are similar. The length of the posts makes the difference. By average, a post
on Beyond Blue contains 144.5% more relevant tokens than a post on r/depression. The reason for this
is that there are a lot of very short comments on r/depression as one can see by the very large difference
concerning the tokens per answers. In addition, comparing the average token count of submissions, the
token count on Beyond Blue is still a notable 20% larger than that of r/depression. These results lead to
the assumption that users and moderators engage in much more length into the problems of initial posts.
3.3 Word Frequencies
We analyzed the most frequent words to gain a ﬁrst impression of the topics and sentiment of the forums.
Therefore, all stop words and tokens, which were not tagged as a noun, were removed from the corpora.
We have chosen only nouns instead of all words, as these provide a better ﬁrst overview of the corpora.
The tokens were lemmatized and then counted to calculate the word frequency.
The most used word of the Beyond Blue corpus (ﬁgure 1) is ”time” with 20,101 mentions. This is due
to the users writing regarding to a speciﬁc time in their or other people’s life. It is striking that the majority
of the most frequent nouns are indeed other time related terms: ”day” (0.245%), ”year” (0.207%) and
”week” (0.098%). ”Depression”, which is the main topic of the forum, is unsurprisingly the third most
used word with 14,307 mentions (0.287%). Other frequent words like ”friend” and ”family”, ”work” and
”job”, ”feeling” and ”thought” or ”help” and ”support” possibly indicate some topics, which are explored
in detail in the topic modeling section. Looking at the r/depression corpus (Figure 2), ”time” is also the
most used word (0.376%). The time-related terms are very common as well with 0.262% (day), 0.228%
(year), 0.084% (month) and 0.080% (week), which could indicate that much of the conversation relies
on narrating own experiences. In direct comparison, the top twenty most frequent words of both forums
are quite similar. Four of the twenty most frequent words of Beyond Blue are not included in the ones of
r/depression (”lot”, ”post”, ”anxiety”, ”support”) and vice versa (”person”, ”school”, ”shit”, ”month”).
The occurrence of ”school” (0.107%) among the most frequent nouns might indicate a younger user base
3.4 Word Categories
To analyze the language used in both forums we split their content into language categories with the
help of the LIWC dictionary. Results are received by assigning words of a given text to the linguistic
and semantic word categories of the LIWC dictionary and yield the occurrence of each category in
percentages as a result. Table 2 illustrates the results.
Figure 2: Twenty most frequent words of r/depression
LIWC Category Examples for words in this cate-
Reddit Value BB Value Difference
Personal Pronoun e.g. he, she, you 15.77 16.21 0.44
1st Person Singular e.g. I, me, myself 15.01 12.78 2.23
1st Person Plural e.g. our, we, us 0.99 1.45 0.46
2nd Person e.g. y’all, you, yourself 4.34 5.37 1.03
3rd Person Singular e.g. he, she, oneself 1.52 1.67 0.15
3rd Person Plural e.g. them, they, they’ll 0.78 0.66 0.12
Affective Processes e.g. emotion, hopes, ugh and all
terms of positive/negative emotion
14.30 14.72 0.42
Positive Emotion e.g. better, fabulous, joy 4.43 4.81 0.38
Negative Emotion e.g. bad, rotten, upset 7.12 6.54 0.58
Past Focus e.g. ago, previous, remembered
and other verbs in past tense
3.90 3.66 0.24
Present Focus e.g. current, nowadays, under-
stands and other verbs in present
17.70 17.95 0.25
Future Focus e.g. expect, hopeful, wishing 1.60 1.65 0.05
Death e.g. bury, die, kill 0.38 0.15 0.23
Swear Words e.g. damn, hell, moron 1.33 0.74 0.59
Table 2: Percentages for LIWC word categories occurrence in r/depression and Beyond Blue
Sentiment Class Beyond Blue r/depression
Positive 40.20% 34.49%
Neutral 34.42% 34.20%
Negative 25.38% 31.32%
Table 3: Overall ratio of sentences classiﬁed with a polarity class
Sentiment Class Beyond Blue r/depression
initial answer initial answer
Positive 29.12% 42.54% 28.44% 38.23%
Neutral 33.45% 34.62% 34.33% 34.12%
Negative 37.43% 22.84% 37.23% 27.65%
Table 4: Ratio of sentences classiﬁed with a polarity class, split by initial posts and answers
For clearer visualization, we only included a few categories, which either have large differences or are
important for our context of depression forums. First, users of r/depression make more frequent use of
negative language than those of Beyond Blue as shown by the positive emotions and negative emotions
categories. This validates similar ﬁndings for sentiment analysis. Differences can also be seen in the
rather negative categories swear words and death, where r/depression has nearly twice or three times the
amount of Beyond Blue. Especially the difference considering swear words might be another indicator
for a younger user base using more informal language. r/depression has a stronger past focus while
Beyond Blue has a slightly stronger present focus. While both forums use about the same amount of
pronouns, there is a notable difference in which pronouns they use. Within r/depression, the ﬁrst person
singular is more frequently used. Contrary, inside Beyond Blue ﬁrst person plural and second person are
more common. This might point to an important difference in communication since users in r/depression
tend to talk more about themselves while the user base in Beyond Blue engages more in discussion
directed towards each other. This is to some extent also in line with the ﬁnding that answers to initial
posts are longer in Beyond Blue than in r/depression. Nevertheless, the results overall underline related
ﬁndings that language of depressive individuals is negatively charged and includes frequent use of ﬁrst
person singular (Lee et al., 2007). Beyond Blue’s smaller percentages in those two categories therefore
imply that their user base utilizes less depressive language.
3.5 Sentiment Analysis
Sentiment analysis was conducted by using VADER (Hutto and Gilbert, 2014). VADER is a lexicon-
based sentiment analysis tool that is speciﬁcally attuned to sentiments expressed in social media and is
therefore also used for Reddit (Schmidt et al., 2020b). VADER shows good evaluation results for thie
context of social media (Hutto and Gilbert, 2014). Therefore, it is a ﬁtting selection for our sentiment
approach. VADER provides sentiment analysis on a sentence-level. VADER classiﬁes the given text
with one of three polarities: positive, neutral and negative.
There is nearly the same ratio of neutral sentences in both forums, but positive and negative sentence
ratios differ quite notably (see table 3). Beyond Blue consists of 40.20% positive sentences and 25.38%
negative ones, whereas r/depression shows 34.49% positive and 31.32% negative sentences. Accord-
ingly, r/depression generally has a more negative sentiment than Beyond Blue. The results of sentiment
analysis when splitting initial posts and answers into sentences, are shown in Table 4.
The sentiment of initial posts is nearly identical for both forums. The answers are more positive
compared to the initial posts overall, but answers on Beyond Blue are more positive than the ones on
r/depression. According to the sentiment, the content that users post in one of these forums is approxi-
mately the same. The difference are the replies to those posts, which are more positive on Beyond Blue.
This implies that the community in Beyond Blue reacts more supportive to people talking about their
3.6 Topic Modeling
For our analysis, we created a Latent Dirichlet Allocation (LDA) model with the Python library gensim
(Rehurek and Sojka, 2010) for each forum. We decided on creating one text document per post as it led
to better results than using an entire thread. Tokenization and lemmatization were done for each post and
stop words were removed. Posts which then had less than ﬁve tokens left were excluded from the topic
modeling process, as they most likely will not contain any useful information. Originally, we created
models with thirty topics, each represented by thirty keywords for both forums. Manual decrease for the
number of topics to ﬁfteen and the number of words to twenty lead to clearer visualization. The topics
removed were mostly duplicates or topics with unclear results, the ﬁltered keywords were duplicates and
those which did not provide additional information. After testing with different variables, we decided on
a chunk size of 2500 posts and 15 passes for each model, which generated meaningful topics.
Please refer to the appendix for the reduced and ﬁltered 15 topics per forum and the corresponding 20
key terms for each topic (Appendix: Figure 3-4). Please note that the names for the topics are added by
us based on our interpretation of the term list as LDA topic modeling does not produce names for topics.
We will use these names in the following sub chapter.
While r/depression has entire topics revolving around school and college life (topics 2 [”School”] and
10 [”College”]) pointing towards user groups consisting of teenagers and young adults, Beyond Blue
has several topics dealing with subjects like ﬁnances or insurance (e.g. topics 12 [”Social Care”] and 14
[”Work”]). This becomes even clearer when looking at the topic ”Family” for each forum (topic 4 for
r/depression and topic 6 for Beyond Blue). While the r/depression topic seems to be mainly from the
viewpoint of someone growing up, with keywords like mom or dad, the Beyond Blue topic seems to be
from the viewpoint of a parent with keywords like boy or girl.
Other differences can be found in the user’s interests and hobbies of each forum. r/depression contains
topics revolving around ”Entertainment Media” (topic 12) or ”Social Life” with friends (topic 9), whereas
Beyond Blue has topics about general ”Lifestyle” (topic 13, keywords e.g. exercise, book, walk) and
”Local Life” (topic 5, keywords e.g. dog, shop, town, car). Similar topics, that occur for both forums
include ”Relationships” (r/depression topic 8; Beyond Blue topic 4), medical aspects (r/depression topics
14 [”Therapy”] and 15 [”Treatments”]; Beyond Blue topic 2 [”Mental Condition”], 10 [”Treatments”]
and 11 [”Therapy”]), ”Work” (r/depression topic 13, Beyond Blue topic 14) or ”Family”. Topics about
relationships and medication are important in both forums and seem to be a consistent discussion point.
While topics concerning self-harm and suicide can also be found in both forums (topic 1 [”Emo-
tional Pain”] and 3 [”Self-Harm”] for r/depression and topic 1 [”Self-Harm”] for Beyond Blue), they
are represented very differently. In r/depression with keywords like hate, cry, tired, emptiness, suffering,
meaningless and torture, the topics seem to be about venting or describing one’s feelings and emotions
that are connected to thoughts about self-harm. Even though this can be found in some words of the
Beyond Blue topic as well (e.g. anger, invisible), it seems the underlying motive is about supporting
other members of the community who have suicidal thoughts, shown by words like encourage, concern,
support or relate. Beyond Blue contains more words like reach, seek, offer and help (topic 8 [”BB
Community”]) or suggestion, advice and sharing (topic 3 [”BB Forum”]) paired with terms describing
the forum itself like BB, community or reply and post which indicates that people offer help or reach
out for help from other members of the forum. While the r/depression topics also include some words
like encourage (topic 14 [”Therapy”]) or recommend (topic 15 [”Treatments”]), their occurrences are
rare. All in all, similar to the ones about self-harm, most of the r/depression topics rather seem to be
about describing one’s own feelings and experiences (e.g. topics 9 [”Social Life”], 10 [”College”] and
11 [”Future Expectations”]).
We want to highlight some of the major limitations of our project. The striking problem is that both cor-
pora differ largely from each other, predominantly considering the size. We tried to control this limitation
to some extent by (1) focusing on normalized metrics for our methods and (2) including at least a year
for r/depression to avoid seasonal changes and to reduce the size of r/depression. The demographic of
the user base might differ as well. Beyond Blue is a predominantly Australian forum while r/depression
is constituted by a more international user base. While statistics show that Reddit’s user base is primarly
male and young7, certain results point to a more adult user base for Beyond Blue (althoug we have no
concrete statistics). r/depression has also no moderation by trained professionals. We address further
differences between the user base in the upcoming chapter. Another important limitation lies in the ap-
plication of some of the methods. The results of the sentiment analysis have to be taken with caution.
While VADER is optimized for social media content, we did not precisely evaluate the performance on
these speciﬁc corpora. While the LIWC dictionary is an established resource for lexical analysis, it is
also not speciﬁcally designed for social media content and might therefore lack some important terms or
Please not that due to the fact that we gathered publicly available data and we only deal with anonymized
data, we did not need permission by the ethics commission of our institution at the time of the writing.
First, we want to highlight similarities between both forums, which might point to general attributes of
depression forums and their users: In both forums time-related terms are among the most frequent ones,
showing that the narration of past and present events is a consistent discussion in depression forums.
Both forums show a tendency to positive sentiment, thus illustrating that support and the recollection of
positive aspects are an important part of depression forums in general. Topics about relationships, family,
work and medication are highly important in both forums and structured rather similar. Especially the
consistency considering the topics about relationships and family show the importance of these aspects
for depressive illnesses.
Considering differences of the two forums, our ﬁndings correlate well and support assumptions over
all applied methods. General corpus analysis and analysis about word categories conducted with LIWC
show that the posts in general, as well as the language in Beyond Blue revolve around support and posi-
tivity, whereas negative speech and posts with little information value are more common in r/depression.
This is in line with the ﬁnding that the answers to posts are much longer on average in Beyond Blue
than in r/depression pointing to a more seriously engaged user and moderator base. The sentiment scores
support this impression with Beyond Blue being noticeably more positive, mainly because answers to
initial submissions in r/depression have a more negative sentiment than the rather positive responses on
Beyond Blue. The ﬁndings produced by the topic modeling approach include the impression that the
user base of Beyond Blue is older, as some topics have a focus on ﬁnancial and organizational themes,
and indicate a ”parent view” while r/depression’s users speak about school, college and their parents.
Most interestingly, and complementing other ﬁndings, topics dealing with self-harm and suicide can be
found in both forums but are considerably more support oriented in Beyond Blue and more emotional
and negatively biased in r/depression.
Concluding, Beyond Blue and r/depression seem to have different focal points. Where Beyond Blue’s
support oriented language and content seems to indicate conversations where concrete problems are
solved, r/depression’s emotional posts, past tense and rather negative sentiment imply the focus lies on
sharing experiences and converse about emotions. While our ﬁndings are exploratory at the moment,
we hypothesize that a professionally curated and smaller forum might be more beneﬁcial to be used by
persons affected by depression since it is more supportive and positive in its content. This conclusion
has to be taken with caution because (1) many differences might rely on the different user demographic,
which seems to be younger in r/depression and (2) the open and more peer-oriented culture of Reddit
might still be helpful and beneﬁcial in some situations.
There are certain approaches we want to pursue to further investigate our results. We want to create a
more balanced corpus by acquiring a collection of similar forums like Beyond Blue instead of just one.
It is also necessary to optimize several of our text mining techniques for our context. Lastly, we also
plan to work closely with professional psychologists and therapists to integrate their expertise into our
Marouane Birjali, Abderrahim Beni-Hssane, and Mohammed Erritali. 2017. Machine learning and semantic
sentiment analysis based algorithms for suicide sentiment prediction in social networks. Procedia Computer
David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. Journal of machine
Learning research, 3(Jan):993–1022.
Mike Conway and Daniel O’Connor. 2016. Social media, big data, and mental health: current advances and
ethical implications. Current opinion in psychology, 9:77–82.
Elena Davcheva, Martin Adam, and Alexander Benlian. 2019. User dynamics in mental health forums–a sentiment
Munmun De Choudhury and Sushovan De. 2014. Mental health discourse on reddit: Self-disclosure, social
support, and anonymity. In Eighth international AAAI conference on weblogs and social media.
Munmun De Choudhury and Emre Kiciman. 2017. The language of social support in social media and its effect
on suicidal ideation risk. In Eleventh International AAAI Conference on Web and Social Media.
Barbara Silveira Fraga, Ana Paula Couto da Silva, and Fabricio Murai. 2018. Online social networks in health care:
A study of mental disorders on reddit. In 2018 IEEE/WIC/ACM International Conference on Web Intelligence
(WI), pages 568–573. IEEE.
Clayton J Hutto and Eric Gilbert. 2014. Vader: A parsimonious rule-based model for sentiment analysis of social
media text. In Eighth international AAAI conference on weblogs and social media.
Matthew L Jockers. 2013. Macroanalysis: Digital methods and literary history. University of Illinois Press.
Anthony F Jorm, Anthony F Jorm, Helen Christensen, Kathleen M Grifﬁths, Anthony F Jorm, Helen Christensen,
and Kathleen M Grifﬁths. 2005. The impact of beyondblue: the national depression initiative on the aus-
tralian public’s recognition of depression and beliefs about treatments. Australian & New Zealand Journal of
Alistair Kennedy and Diana Inkpen. 2006. Sentiment classiﬁcation of movie reviews using contextual valence
shifters. Computational intelligence, 22(2):110–125.
Chang H Lee, Myungju Lee, Sungwoo Ahn, and Kyungil Kim. 2007. Preliminary analysis of language styles in a
sample of schizophrenics. Psychological reports, 101(2):392–394.
Changhwan Lee, Kyungil Kim, Jeongsub Lim, and Yoonhyoung Lee. 2015. Psychological research using lin-
guistic inquiry and word count (liwc) and korean linguistic inquiry and word count (kliwc) language analysis
methodologies. Journal of Cognitive Science, 16(2):132–49.
Bing Liu. 2016. Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge University Press.
Albert Park and Mike Conway. 2017. Longitudinal changes in psychological states in online health community
members: understanding the long-term effects of participating in an online depression community. Journal of
medical Internet research, 19(3):e71.
James W Pennebaker, Ryan L Boyd, Kayla Jordan, and Kate Blackburn. 2015. The development and psychometric
properties of liwc2015. Technical report.
Radim Rehurek and Petr Sojka. 2010. Software framework for topic modelling with large corpora. In In Proceed-
ings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. Citeseer.
Philip Resnik, William Armstrong, Leonardo Claudino, Thang Nguyen, Viet-An Nguyen, and Jordan Boyd-Graber.
2015. Beyond lda: exploring supervised topic modeling for depression-related language in twitter. In Proceed-
ings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to
Clinical Reality, pages 99–107.
Thomas Schmidt and Manuel Burghardt. 2018. An evaluation of lexicon-based sentiment analysis techniques for
the plays of gotthold ephraim lessing. In Proceedings of the Second Joint SIGHUM Workshop on Computational
Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 139–149.
Thomas Schmidt, Isabella Engl, Juliane Herzog, and Lisa Judisch. 2020a. Towards an analysis of gender in video
game culture: Exploring gender-speciﬁc vocabulary in video game magazines. In Proceedings of the Digital
Humanities in the Nordic Countries 5th Conference (DHN 2020, pages 333–341.
Thomas Schmidt, Florian Kaindl, and Christian Wolff. 2020b. Distant reading of religious online communities:
A case study for three religious forums on reddit. In Proceedings of the Digital Humanities in the Nordic
Countries 5th Conference (DHN 2020, pages 157–172.
Maite Taboada, Julian Brooke, Milan Toﬁloski, Kimberly Voll, and Manfred Stede. 2011. Lexicon-based methods
for sentiment analysis. Computational linguistics, 37(2):267–307.
Xinyu Wang, Chunhong Zhang, Yang Ji, Li Sun, Leijia Wu, and Zhana Bao. 2013. A depression detection model
based on sentiment analysis in micro-blog social network. In Paciﬁc-Asia Conference on Knowledge Discovery
and Data Mining, pages 201–213. Springer.
Figure 3: Cleaned LDA topics for r/depression
Figure 4: Cleaned LDA topics for Beyond Blue