Conference PaperPDF Available

Exploring Online Depression Forums via Text Mining: A Comparison of Reddit and a Curated Online Forum


Abstract and Figures

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.
Content may be subject to copyright.
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
Luis Moßburger
Media Informatics Group
University of Regensburg
Regensburg, Germany
Felix Wende
Media Informatics Group
University of Regensburg
Regensburg, Germany
Kay Brinkmann
Media Informatics Group
University of Regensburg
Regensburg, Germany
Thomas Schmidt
Media Informatics Group
University of Regensburg
Regensburg, Germany
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.
1 Introduction
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 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
for r/depression.
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”]).
4 Limitations
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
5 Discussion
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
Science, 113:65–72.
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
analysis perspective.
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
Psychiatry, 39(4):248–254.
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.
A Appendix
Figure 3: Cleaned LDA topics for r/depression
Figure 4: Cleaned LDA topics for Beyond Blue
... Background Mental health is fundamental to our individual and collective capabilities of thinking and interacting with each other and enjoying life [1]. Mental health challenges have been increasing worldwide, with consequent negative impacts on social and economic prosperity [2][3][4]. A 2017 survey [5] found that approximately 1 in 7 people (equivalent to 1 billion people) worldwide have experienced mental illness. ...
... For example, a triaging system was used to assist web-based peer support by classifying forum messages into different risk levels based on how urgently moderators' attention was needed [23]. A study [3] compared 2 web-based depression forums (Beyond Blue [11] and r/depression on Reddit [24]) using NLP techniques based on user sentiments and discussion topics. NLP techniques have also been used to identify which posts from web-based health forums (HealthBoards [25], Inspire [26], and HealthUnlocked [27]) are related to the COVID-19 pandemic based on conversations among mental health consumers. ...
Full-text available
Background: Resilience is an accepted strengths-based concept that responds to change, adversity, and crises. This concept underpins both personal and community-based preventive approaches to mental health issues and shapes digital interventions. Online mental health peer-support forums have played a prominent role in enhancing resilience by providing accessible places for sharing lived experiences of mental issues and finding support. However, little research has been conducted on whether and how resilience is realized, hindering service providers' ability to optimize resilience outcomes. Objective: This study aimed to create a resilience dictionary that reflects the characteristics and realization of resilience within online mental health peer-support forums. The findings can be used to guide further analysis and improve resilience outcomes in mental health forums through targeted moderation and management. Methods: A semiautomatic approach to creating a resilience dictionary was proposed using topic modeling and qualitative content analysis. We present a systematic 4-phase analysis pipeline that preprocesses raw forum posts, discovers core themes, conceptualizes resilience indicators, and generates a resilience dictionary. Our approach was applied to a mental health forum run by SANE (Schizophrenia: A National Emergency) Australia, with 70,179 forum posts between 2018 and 2020 by 2357 users being analyzed. Results: The resilience dictionary and taxonomy developed in this study, reveal how resilience indicators (ie, "social capital," "belonging," "learning," "adaptive capacity," and "self-efficacy") are characterized by themes commonly discussed in the forums; each theme's top 10 most relevant descriptive terms and their synonyms; and the relatedness of resilience, reflecting a taxonomy of indicators that are more comprehensive (or compound) and more likely to facilitate the realization of others. The study showed that the resilience indicators "learning," "belonging," and "social capital" were more commonly realized, and "belonging" and "learning" served as foundations for "social capital" and "adaptive capacity" across the 2-year study period. Conclusions: This study presents a resilience dictionary that improves our understanding of how aspects of resilience are realized in web-based mental health forums. The dictionary provides novel guidance on how to improve training to support and enhance automated systems for moderating mental health forum discussions.
... Twitter_German_Federal_Election_2021 2 Related Work Ever since the rise of social media, sentiment analysis on social media platforms is a very active research area (Wang et al., 2012;Elbagir and Yang, 2019). Sentiment analysis is used, for example, to explore sentiment in Reddit forums (Schmidt et al., 2020c;Moßburger et al., 2020), on Twitter (Elbagir and Yang, 2019) or social media artefacts like memes (Schmidt et al., 2020b). In the following chapters, we summarize important research in the context of political analysis on Twitter and offer an overview of current sentiment analysis methodology. ...
Conference Paper
Full-text available
We present the results of a project performing sentiment analysis on tweets from German politicians and party accounts for the 2021 German federal election. We collected over 58,000 tweets from the Twitter accounts of the seven parties represented in the German Bundestag, of which a selection of 2,000 tweets were annotated by three annotators. Based on the annotated data, we implemented multiple sentiment analysis approaches and evaluated the sentiment classification performance. We found that transformer-based models like bidirectional encoder from transformers (BERT) performed better than traditional machine learning models such as Naive Bayes and lexicon-based models like GerVADER. The best performing BERT model achieved an accuracy of 93.3% and macro f1 score of 93.4%. Applying sentiment analysis on the overall corpus via this method showed that overall, negative sentiment was most frequent and that there were multiple major shifts in sentiment a few months before and after the election. Furthermore, we found that tweets from opposition parties had on average more negative sentiment than those from governing parties.
... Table 2 displays the 31 topics rendered from the final topic model with K = 31, which are ranked based on the topic prevalence across the corpus (RQ1). Combining the terms with four different scoring methods (highest probabilities, FREX, Lift and Score) and the representative post(s), we have generated a label for with each topic separately following the common practices in previous studies 36,37 . 3 And later we compared and validated all 31 topics together and categoried them into seven groups. ...
Full-text available
This paper aims at identifying user’s information needs on Coronavirus and the differences of user’s information needs between the online health community MedHelp and the question-and-answer forum Quora during the COVID-19 global pandemic. We obtained the posts in the sub-community Coronavirus on MedHelp (195 posts with 1627 answers) and under the topic of COVID-19(2019-2020) on Quora (263 posts with 8401 answers) via web scraping built on Selenium WebDriver. After preprocessing, we conducted topic modeling on both corpora and identified the best topic model for each corpus based on the diagnostic metrics. Leveraging the improved sqrt-cosine similarity measurement, we further compared the topic similarity between these two corpora. This study finds that there are common information needs on both platforms about vaccination and the essential elements of the disease including the onset symptoms, transmission routes, preventive measures, treatment and control of COVID-19. Some unique discussions on MedHelp are about psychological health, and therapeutic management of patients. Users on Quora have special interests of information about the association between vaccine and Luciferase, and attacks on Fauci after email trove released. The work is beneficial for researchers who aim to provide accurate information assistance and build effective online emergence response programs during the pandemic.
... Due to simple word-based calculations one can infer the sentiment of a text: By summing up the number of positive words and subtracting the number of negative words, one receives an overall value for the sentiment of the text unit which can be regarded as negative if the value is below 0, neutral for 0 and positive if the value is above 0. Oftentimes, sentiment lexicons offer continuous values instead of nominal assignments which can be used similarly. In research, lexicon-based sentiment analysis is often chosen when machine learning is not possible due to the lack of well annotated corpora and is a common method in sentiment analysis on literary and historical texts [1,24,14,29,34,26,35,50,2,40] or for special social media corpora [44,46,25]. ...
Conference Paper
Full-text available
We present first results of an exploratory study about sentiment analysis via different media channels on a German historical play. We propose the exploration of other media channels than text for sentiment analysis on plays since the auditory and visual channel might offer important cues for sentiment analysis. We perform a case study and investigate how textual, auditory (voice-based), and visual (face-based) sentiment analysis perform compared to human annotations and how these approaches differ from each other. As use case we chose Emilia Galotti by the famous German playwright Gotthold Ephraim Lessing. We acquired a video recording of a 2002 theater performance of the play at the "Wiener Burgtheater". We evaluate textual lexicon-based sentiment analysis and two state-of-the-art audio and video sentiment analysis tools. As gold standard we use speech-based annotations of three expert annotators. We found that the audio and video sentiment analysis do not perform better than the textual sentiment analysis and that the presentation of the video channel did not improve annotation statistics. We discuss the reasons for this negative result and limitations of the approaches. We also outline how we plan to further investigate the possibilities of multimodal sentiment analysis.
... Emotion recognition is a sub-field of affective computing (cf. Halbhuber et al. 2019;Hartl et al. 2019;Ortloff et al. 2019;Schmidt et al. 2020c) and is often applied in DH to predict sentiment and emotions from written text (Moßburger et al. 2020;Schmidt / Burghardt 2018;Schmidt, 2019;Schmidt et al. 2019a;Schmidt et al. 2020b). We focus on the image channel of movies and for the emotion prediction we use the Python module FER 2 (Goodfellow et al. 2013). ...
Conference Paper
Full-text available
We present an exploratory study in the context of digital film analysis inspecting and comparing five canonical movies by applying methods of computer vision. We extract one frame per second of each movie which we regard as our sample. As computer vision methods we explore image-based object detection, emotion recognition, gender and age detection with state-of-the-art models. We were able to identify significant differences between the movies for all methods. We present our results and discuss the limitations and benefits of each method. We close by formulating future research questions we plan to answer by applying and optimizing the methods.
... Online media and content have gained a lot of interest in Digital Humanities (DH) in recent years (e.g. Moßburger et al. 2020;Schmidt et al. 2020a;Schmidt et al. 2020c). In the context of literary studies, the analysis of online creative writing platforms has gained more and more Cite as: Schmidt, T., Grünler, J., Schönwerth, N. & Wolff, C. (2021). ...
Conference Paper
Full-text available
We report upon a digital humanities project on the acquisition and analysis of a corpus of German online writings. We have implemented a scraper to gather the German language material as well as corresponding metadata of the popular online writing platform Archive of Our Own (AO3), which is a platform primarily focused on the text sort of fan fictions. The corpus consists of 9,640 writings resulting in over 39 million tokens and 3.6 million sentences. The texts have varying lengths with a median of around 2,500 tokens per story. We present results on the analysis of metadata and general text statistics like the most frequent words. While we can support previous findings of literary and media studies like the dominance of male-male romantic and erotic narratives, we can also identify attributes that are very specific and unique to German culture as well as differences to results of research for English online writings. We will outline in our future work how we plan to further increase and analyze the corpus to support research in digital humanities as well as German literary and fan studies.
... Sentiment analysis (or opinion mining) is a term used to describe computational methods for predicting and analyzing sentiment, predominantly in written text (Liu, 2016, p. 1). Sentiment analysis is especially popular for social media content (Moßburger et al., 2020;Schmidt, Hartl, Ramsauer, Fischer, Hilzenthaler, & Wolff, 2020;Schmidt, Kaindl, & Wolff, 2020) and any other form of user generated content (cf. Mäntylä et al., 2018). ...
Conference Paper
Full-text available
We present SentText, a web-based tool to perform and explore lexicon-based sentiment analysis on texts, specifically developed for the Digital Humanities (DH) community. The tool was developed integrating ideas of the user-entered design process and we gathered requirements via semi-structured interviews. The tool offers the functionality to perform sentiment analysis with predefined sentiment lexicons or self-adjusted lexicons. Users can explore results of sentiment analysis via various visualizations like bar or pie charts and word clouds. It is also possible to analyze and compare collections of documents. Furthermore, we have added a close reading function enabling researchers to examine the applicability of sentiment lexicons for specific text sorts. We report upon the first usability tests with positive results. We argue that the tool is beneficial to explore lexicon-based sentiment analysis in the DH but can also be integrated in DH-teaching.
Full-text available
Individuals choose varying music listening strategies to fulfill particular mood-regulation goals. However, ineffective musical choices and a lack of cognizance of the effects thereof can be detrimental to their well-being and may lead to adverse outcomes like anxiety or depression. In our study, we use the social media platform Reddit to perform a large-scale analysis to unearth the several music-mediated mood-regulation goals that individuals opt for in the context of depression. A mixed-methods approach involving natural language processing techniques followed by qualitative analysis was performed on all music-related posts to identify the various music-listening strategies and group them into healthy and unhealthy associations. Analysis of the music content (acoustic features and lyrical themes) accompanying healthy and unhealthy associations showed significant differences. Individuals resorting to unhealthy strategies gravitate towards low-valence tracks. Moreover, lyrical themes associated with unhealthy strategies incorporated tracks with low optimism, high blame, and high self-reference. Our findings demonstrate that being mindful of the objectives of using music, the subsequent effects thereof, and aligning both for well-being outcomes is imperative for comprehensive understanding of the effectiveness of music.
Conference Paper
Full-text available
We present preliminary results of a project examining the role and usage of gender specific vocabulary in a corpus of video game magazines. The corpus consists of three popular video game magazines with 634 issues from the 1980s until 2011 and was gathered via OCR-scans of the platform archive .org. We report on the distribution and progression of gender-specific words by using word lists of the LIWC for the categories "male" and "female". We can indeed show that words of the type male are considerably more frequent than words of the type female, with a slight increase of female words during 2006-2010. This is in line with the overall development of gaming culture throughout these years and previous research in the humanities. Furthermore, we analyzed how the usage of negatively connoted words for female depictions (e.g. chick, slut) has evolved and identified a constant increase throughout the years reaching the climax around 2001-2005, a timespan that coincides with the release and popularity of games encompassing rather sexist concepts. We discuss the limitations of our explorations and report on plans to further investigate the role of gender in gaming culture.
Conference Paper
Full-text available
We present results of a project examining the application of computational text analysis and distant reading in the context of comparative religious studies, sociology, and online communication. As a source for our corpus, we use the popular platform Reddit and three of the largest religious subreddits: the subreddit Christianity, Islam and Occult. We have acquired all posts along with metadata for an entire year resulting in over 700,000 comments and around 50 million tokens. We explore the corpus and compare the different online communities via measures like word frequencies, bigrams, collocations and sentiment and emotion analysis to analyze if there are differences in the language used, the topics that are talked about and the sentiments and emotions expressed. Furthermore, we explore approaches to diachronic analysis and visualization. We conclude with a discussion about the limitations but also the benefits of distant reading methods in religious studies.
Conference Paper
Full-text available
We present results from a project on sentiment analysis of drama texts, more concretely the plays of Gotthold Ephraim Lessing. We conducted an annotation study to create a gold standard for a systematic evaluation. The gold standard consists of 200 speeches of Lessing's plays and was manually annotated with sentiment information by five annotators. We use the gold standard data to evaluate the performance of different German sentiment lexicons and processing configurations like lemmatization, the extension of lexicons with historical linguistic variants, and stop words elimination, to explore the influence of these parameters and to find best practices for our domain of application. The best performing configuration accomplishes an accuracy of 70%. We discuss the problems and challenges for sentiment analysis in this area and describe our next steps toward further research.
Conference Paper
Full-text available
Individuals around the world in need of mental healthcare do not find adequate treatment because of lacking resources. Since the necessary support can often not be provided directly, many turn to the Internet for assistance, whereby mental health forums have evolved into an important medium for millions of users to share experiences. Information Systems research lacks empirical evidence to analyze how health forums influence users' moods. This paper addresses the research gap by conducting sentiment analysis on a large dataset of user posts from three leading English-language forums. The goal of this study is to shed light on the mood effects of mental health forum participation, as well as to better understand user roles. The results of our exploratory study show that sentiment scores develop either positively or negatively depending on the condition. We additionally investigate and report on user forum roles.
Full-text available
Full text can be found here: Background: Major depression is a serious challenge at both the individual and population levels. Although online health communities have shown the potential to reduce the symptoms of depression, emotional contagion theory suggests that negative emotion can spread within a community, and prolonged interactions with other depressed individuals has potential to worsen the symptoms of depression. Objective: The goals of our study were to investigate longitudinal changes in psychological states that are manifested through linguistic changes in depression community members who are interacting with other depressed individuals. Methods: We examined emotion-related language usages using the Linguistic Inquiry and Word Count (LIWC) program for each member of a depression community from Reddit. To measure the changes, we applied linear least-squares regression to the LIWC scores against the interaction sequence for each member. We measured the differences in linguistic changes against three online health communities focusing on positive emotion, diabetes, and irritable bowel syndrome. Results: On average, members of an online depression community showed improvement in 9 of 10 prespecified linguistic dimensions: "positive emotion," "negative emotion," "anxiety," "anger," "sadness," "first person singular," "negation," "swear words," and "death." Moreover, these members improved either significantly or at least as much as members of other online health communities. Conclusions: We provide new insights into the impact of prolonged participation in an online depression community and highlight the positive emotion change in members. The findings of this study should be interpreted with caution, because participating in an online depression community is not the sole factor for improvement or worsening of depressive symptoms. Still, the consistent statistical results including comparative analyses with different communities could indicate that the emotion-related language usage of depression community members are improving either significantly or at least as much as members of other online communities. On the basis of these findings, we contribute practical suggestions for designing online depression communities to enhance psychosocial benefit gains for members. We consider these results to be an important step toward a better understanding of the impact of prolonged participation in an online depression community, in addition to providing insights into the long-term psychosocial well-being of members.
Mental health (including substance abuse) is the fifth greatest contributor to the global burden of disease, with an economic cost estimated to be US $2.5 trillion in 2010, and expected to double by 2030. Developing information systems to support and strengthen population-level mental health monitoring forms a core part of the World Health Organization's Comprehensive Action Plan 2013-2020. In this paper, we review recent work that utilizes social media “big data” in conjunction with associated technologies like natural language processing and machine learning to address pressing problems in population-level mental health surveillance and research, focusing both on technological advances and core ethical challenges.
Conference Paper
Datasets originating from social networks are valuable to many fields such as sociology and psychology. But the supports from technical perspective are far from enough, and specific approaches are urgently in need. This paper applies data mining to psychology area for detecting depressed users in social network services. Firstly, a sentiment analysis method is proposed utilizing vocabulary and man-made rules to calculate the depression inclination of each micro-blog. Secondly, a depression detection model is constructed based on the proposed method and 10 features of depressed users derived from psychological research. Then 180 users and 3 kinds of classifiers are used to verify the model, whose precisions are all around 80%. Also, the significance of each feature is analyzed. Lastly, an application is developed within the proposed model for mental health monitoring online. This study is supported by some psychologists, and facilitates them in data-centric aspect in turn.
Sentiment analysis is the computational study of people's opinions, sentiments, emotions, and attitudes. This fascinating problem is increasingly important in business and society. It offers numerous research challenges but promises insight useful to anyone interested in opinion analysis and social media analysis. This book gives a comprehensive introduction to the topic from a primarily natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs that are commonly used to express opinions and sentiments. It covers all core areas of sentiment analysis, includes many emerging themes, such as debate analysis, intention mining, and fake-opinion detection, and presents computational methods to analyze and summarize opinions. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences.