Reddit: A Novel Online Financial Consultancy
Lydia Manikonda, Shailesh Divey
Rensselaer Polytechnic Institute{manikl, sdivey }@rpi.edu

Abstract
Social media forums are becoming rich and valuable
data sources due to their features and the level of user
engagement on these venues. This paper investigatesdata from online social media forums – which are popular finance-related subreddits to provide answers tofive different research questions proposed in this paper. Our analysis revealed five main interesting insights.
1) We found that users primarily seek information andshare information on these subreddits. 2) We found that
finance-related subreddits are highly negative in sentiment contrary to the popular belief that Reddit posts
are in general positive in sentiment. 3) Users on thesesubreddits do not focus much on their past nor future
but their present situation. 4) Even though overall sentiment is negative across the subreddits we considered,word-wise, positive emotion dominates negative emotion. 5) All these four subreddits have large instances ofdifferent set of pronouns which provide insights about
whom the users are addressing in their posts. Overall,
we found the patterns of seeking personal advice as well
as sharing experiences of financial decision-making are
dominating compared to any other interests.

1

Introduction

Social media platforms have become the de facto medium
to seek information, share opinions, and connect with otherindividuals (Benevenuto et al. 2009; Wilson et al. 2009).
The user communication over these platforms for different purposes is generating enormous amounts of rich valuable data that has been used for various applications. Someof these applications include – predicting stock market
prices (Bollen, Mao, and Zeng 2011), predicting flu outbreak (Achrekar et al. 2011), real-time analysis about politics (Wang et al. 2012), etc. In order to make these kinds ofpredictions, analyzing the data to first identify important predictors and variables is crucial. Especially, given the enormity as well as the rich diverse content present in the onlinesocial media data helps in better understanding of the offline
behavior of individuals and thereby, society at large.
Despite the growing interest in intersection of social networks and capital markets, much of this limited work has
been focused on platforms that are not forums (Chen etal. 2014; Hazari and Richards 2011; Way, Wong, and Gibbons 2011; Xu and Zhang 2009). Investigating these doCopyright © 2020, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.

mains on social media forums designed for discussions
have a great potential to reveal important patterns. Reddit(http://www.reddit.com) is an online social news website
and forum wherein the content is organized in subreddits
by the areas of interest. In each subreddit, users can make apost in the form of natural language, web urls, visual entities
like images and videos. According to a recent Pew Research
report 1 , 11% of adults in United States are active users of
Reddit. The widespread usage along with the rich discussions help best answer the research questions we propose.
Using quantitative analysis, we investigate five researchquestions as follows – RQ1. Can we infer the main motivations for users to participate in these subreddits about
finance?; RQ2. What topics do users discuss on these forums?; RQ3. How are their posts aligned with regard toemotions and other psycholinguistic metrics?; RQ4. Are
these discussions about their past, present or future plans?.
Finally, RQ5. what are their sentiments expressed throughthe online posts shared on these subreddits?
We summarize our contributions as follows:• Main aim of users on these forums is to seek informationor share information.
• We found that longer posts (#words/characters) do notnecessarily receive higher social engagement. We measured this phenomenon both in terms of active (posts andtheir replies) and passive (likes) engagement.
• Contrary to the existing literature, posts made on financerelated subreddits are highly negative in sentiment.
• Users on these subreddits do not focus much on their pastor future but only their present situation.
• Even though we found the overall sentiment negativeacross the posts on the subreddits, word-wise, positive
emotion dominates negative emotion.
• Topic modeling and word embeddings revealed that users
talk about various issues surrounding their personal lives
including credit card debt, insurance related to medical, auto, home, HVAC-related issues, student loans, etc.
Users emphasize on setting realistic goals to achieve theirfinancial independence and to invest smarter.
• Each subreddit contains content focusing on different
types of pronouns which suggest that some forums are1
https://www.pewresearch.org/internet/
chart/who-uses-youtube-whatsapp-and-reddit/

Subreddit
FIND
INV
PF
SMKT

Max Responses
497
490
475
482

Mean Responses
125
25
10
15

Table 1: Active engagement: Maximum number of responses received for a post and average number of responses
per post in a given subreddit
primarily to seek information and other forums are exclusively to share information.

1.1

DatasetWe crawl data from four subreddits that focus onefficient financial decision-making which aim at
different goals – 1) approaches to retiring early(/r/financialindependence/), 2) actively participating in
financial markets (/r/investing), 3) asset management andfinancial planning (/r/personalfinance), and 4) discussmarket news and strategy (/r/stockmarket). We utilize
the Python wrapper for Reddit API (PRAW2 ) to crawl
the Reddit posts and their metadata. The final data has
698 discussion threads from /r/financialindependence (wedenote this as FIND in the rest of this paper), 852 discussion
threads from /r/investing (INV), 908 discussion threads
from /r/personalfinance domain (PF), and 712 discussion
threads from /r/stockmarkets (SMKT).

2

Content Analysis

Social media discussion threads are commonly studied fromthe perspective of social dynamics (Backstrom et al. 2013).
There exists literature (Rubin and Spaht 2010; Spaht and Rubin 2012) in the area of finance about various strategies andoptions to achieve financial independence as well as ideas
to retire early. Alongside, there are numerous financial advising and consulting firms that help individuals personallyto achieve their financial goals. However, the subreddits weconsidered here are popular among online users, and ourmain goal in this research is to understand what users talkon these subreddits related to finance and how they help eachother to achieve their financial goals. We investigate various
factors of the users’ language on these communities and extract different patterns to draw insights.
Social Engagement: Longer the post doesn’t mean
higher engagement To understand the engagement onthese subreddits, we consider both passive (the upvotes
and downvotes) and active (responses) engagement metrics(shown in Table 1) on these forums. We also consider thepost length statistics in Table 2 in the same context whichsuggests that indeed FIND is the most active subreddit withnot only passive engagement but also in terms of the active
engagement even though other subreddits do attract large
number of responses only for a specific set of posts.
Motivations of these communities: Acting as online financial consultancies To understand the main motivations of these communities, at a user-level, we first extracted2

https://praw.readthedocs.io/en/latest/index.html

the other subreddits that these users from our dataset are actively participating in. We found that overall ”/r/AskReddit”
is the most popular subreddit where these users (23.75%of them) are actively making posts. Among the top-10(AskReddit, personalfinance, Showerthoughts, funny, investing, pics, financialindependence, aww, wallstreetbets,
todayilearned) of the active subreddits, 6 of them are information seeking subreddits where most of them are related tofinance.
At a post-level, to draw insights about the interests ofthese communities we first conduct n-gram analysis. Ngram is a sequential list of n words. We use them to encode the likelihood that a phrase reveals the purpose of
these subreddits. As part of this analysis, a given sentence
has to be split into chunks of consecutive words that areof length ‘n’. Due to space restrictions, we show only thelist of top-10 trigrams in Table 3 that are extracted from theposts made on these four different subreddits separately. The
n-grams suggest that users on these forums are primarily
seeking information and sharing information. For example,
phrases such as non-investing discussion, helpful retirement
accounts, high yield savings, etc. suggest that these subreddits might be acting as online financial consultancies.
Topics of discussion: Wide spectrum of inquiries regarding advice to earning, spending, saving, budgeting, asset
management, trading know-how, etc We utilize Latent
Dirichlet Allocation (LDA) (Blei, Ng, and Jordan 2002) toperform topic modeling. LDA assumes that the document isa mixture of topics where each topic is a mixture of words
assigned to a topic, where the topic distribution is assumed
to have a dirichlet prior. We use the Python package gensim(Řehůřek and Sojka 2010) to perform topic modeling over
the online reviews in our dataset. Table 4 shows that users
sharing their personal financial details such as loans, insurances, and at the same time posing questions to other users
by asking questions such as – ”rent or buy a house?”. These
kinds of questions as well as sharing their personal financial
details along with the concerns are quite evident in these topics. These observations are similar to the other insights weobtained from the above analyses that users are coming tothese platforms to seek advice extensively.
Posts are highly negative in sentiment Towards conducting sentiment analysis, we utilize the Stanford CoreNLP library (Manning et al. 2014) to automatically classify thesentiment of subreddit posts. For a given sentence, thispackage produces the percentage distribution of the sentiment present in the text to generate percentage distributions
across the 3 categories of sentiments. Table 5 shows interesting results that majority of the posts shared in thesesubreddits are overall negative in sentiment. Existing literature (Manikonda et al. 2018; Schrading et al. 2015;
Steinbaur 2012) in social media analysis shows that Reddit posts are generally positive or neutral but the sentimentsof the posts in our dataset contradict the past insights about
sentiments from other subreddits.
Subreddits are all uniquely focusing on a specific pronoun where words in a given post express positive emotion irrespective of overall negative sentiment To investigate different linguistic emotions, we employ the psycholinguistic lexicon LIWC (http://liwc.wpengine.com/) on
the reviews that are posted online towards different cate-Subreddit
FIND
INV
PF
SMKT

Total num of posts
87,68222,08010,287
11,021TC
299 (8)297 (4)295 (15)300 (4)TW
68 (1)
58 (1)
71 (2)
69 (1)

TW.µ(σ 2 )11.8 (56)12.2 (60.4)
11.9 (57.3)
12.1 (59)CC
18,650 (0)8435 (0)6016 (0)38,908 (0)CW4062 (1)1761 (1)1357 (1)9745 (1)CW.µ(σ 2 )67.9 (9498.8)
52.9 (6100.9)
76.3 (8412.9)
47.0 (16228.0)

Table 2: Aggregated statistics about the length of the post’s title and its content. TC #characters in the Title; TW #words in the
Title; CC #characters in the content; CW #words in the content. Each cell represents the max (min) of that particular column
name unless it is the mean value (variance).
FIND
work life balance, part time job, fulltime job, 401k roth ira, credit carddebt, student loan debt, households
averse spendingINV
non investing discussion, subprime
auto loans, blow day delinquencies,
financial crisis peakPF
credit card debt, helpful retirement
accounts, high yield savings, 401fund selection

SMKT
high income millenials, long term
investment, company last earnings,
finance yahoo news, trump tariffs
set

Table 3: tri-grams extracted from the four subreddits
Subreddit

Topics

FIND

INV

PF

SMKT

Table 4: Topics compiled, and associated overall posts sentiments for the subreddits
Subreddit
r/financialindependence
r/investingr/personalfinancer/stockmarket

Positive
9.6%
8.1%
10.3%
8%

Neutral
14.2%
17.2%
12.9%
20.4%

Negative
76.2%
74.7%
76.8%
71.6%

Table 5: Overall average sentiments scores measured for theposts shared on the 4 Subreddits in our dataset
gories and various brands that we consider as part of ourdataset. We focus on mainly 4 different emotional attributes,
motivated from the type of interests users exhibit in these
domains – pronouns (i (1st person singular), we (1st person
plural), you (2nd person), shehe (3rd person singular), they
(3rd person plural), ipron (impersonal pronouns like it, its,
those)), time orientations (past, present, future tenses), affective mechanisms (Positive emotions, negative emotions, anxiety, anger) and personal concerns (Achievement, Leisure,
Money). Affect displays the affective processes in terms of
how the product in our scenario affects the users. Cognitive
mechanisms or processes provides insights about aspects
related to cognition such as insight,causation, discrepancy,
tentative, certainty, inhibition, etc.

These results show that users on these subreddits do not
focus much on either their past or future, but their present
situation which we found was very interesting as one of themain goals of these forums is to help users achieve financial independence which happens in the future. Since weare utilizing a word-level psycholinguistic metric, we suspect that this might be due to the fact that users are sharing
their present living conditions or income to seek information about achieving financial freedom to retire early. This
also shows that users are aggressively focused on seeking
feedback on financial conditions.
Co-occurring words: Sharing personal financial details
in the community to set realistic goals To detect similar words based on their semantics and syntax, we trained
Word2Vec model (Mikolov et al. 2013) using the subreddit posts. The word embeddings suggest interesting insights
about how users are openly sharing their financial information to the online community. For example, words such as
fire are highly associated across all the subreddits with words
such as ’goal’, ’achieve’, ’realistic’, ’reached’, ’confident’
revealing that users want to have realistic goals in order toachieve financial independence and retire early. Similarly,Lingusitic Dimensions
Pronouns
i
we
you
shehe
they
ipron

FIND
9.09
2.35
0.35
1.829
0.3
0.42
3.83

INV
8.21
1.62
0.32
1.38
0.28
0.68
3.91

PF
8.91
2.13
0.24
2.01
0.33
0.54
3.66

SMKT
8.62
1.78
0.35
1.52
0.31
0.67
3.98

Past Tense
Present Tense
Future Tense

1.73
6.73
0.75

1.64
6.71
0.83

1.61
6.99
1.07

1.66
7.06
0.90

Positive Emotions
Negative Emotions
Anxiety
Anger

3.65
1.02
0.15
0.34

3.23
1.08
0.12
0.38

2.94
0.81
0.14
0.18

3.54
1.11
0.12
0.43

Achievement
Leisure
Money

1.42
0.46
1.85

1.34
0.34
2.58

1.29
0.30
3.14

1.28
0.37
2.61

Table 6: Metric values for different psycholinguistic attributes extracted from the posts shared on the four subreddits.
users share that 401K should be maximized and they are also
contributing to 403b while discussing various issues pertaining to these retirement plans. We also found that users onthese platforms are skeptical in terms of investing in certain
places due to their concerns about market crashes and recessions. They also provide details into personal issues suchas credit-card debt, and insurance related to medical, auto,
home, HVAC-related issues, student loans, etc.

3Conclusions

We conduct a quantitative analysis on four subreddits related to finance and investments. The results highlight invarious ways (user interest level and content-wise) that individuals are coming to these subreddits to primarily seek
advice. With over 16 million subscribers, we observe, thetopics being discussed cover a wide spectrum of inquiry regarding advice and information to earning, spending, saving,budgeting, asset management, investing in markets, tradingknow-how, and even the more technical and financial analytics. In addition, financial conversations highlight the feelings of insecurity, which could help explain why we obtainan overall negative sentiment from the financial subreddits.
Anonymous forums such as these will allow people interested in pursuing long-term financial independence and stability to learn from shared experiences of other subscribers,
thereby leading a wide spectrum of topics of discussion.

Bollen, J.; Mao, H.; and Zeng, X. 2011. Twitter mood predicts the stock market.
Journal of computational science 2(1):1–8.
Chen, H.; De, P.; Hu, Y. J.; and Hwang, B.-H. 2014. Wisdom of crowds: The valueof stock opinions transmitted through social media. The Review of Financial Studies
27(5):1367–1403.
Hazari, S., and Richards, A. R.-A. 2011. A qualitative study of adoption of social
media for personal finance investing. In Proceedings of the Southern Association
for Information Systems Conference, Atlanta, GA. Retrieved from http://sais. aisnet.
org/2011/HazariRichards. pdf.
Manikonda, L.; Beigi, G.; Liu, H.; and Kambhampati, S. 2018. Twitter for sparking
a movement, reddit for sharing the moment: #metoo through the lens of social media.
CoRR abs/1803.08022.
Manning, C. D.; Surdeanu, M.; Bauer, J.; Finkel, J.; Bethard, S. J.; and McClosky, D.
2014. The Stanford CoreNLP natural language processing toolkit. In Association for
Computational Linguistics (ACL) System Demonstrations, 55–60.
Mikolov, T.; Sutskever, I.; Chen, K.; Corrado, G. S.; and Dean, J. 2013. Distributed
representations of words and phrases and their compositionality. In Advances in neural
information processing systems, 3111–3119.
Řehůřek, R., and Sojka, P. 2010. Software Framework for Topic Modelling with
Large Corpora. In Proceedings of the LREC 2010 Workshop on New Challenges
for NLP Frameworks, 45–50. Valletta, Malta: ELRA. http://is.muni.cz/
publication/884893/en.
Rubin, H., and Spaht, C. 2010. Financial independence: Attainable, maintainable.
Journal of Applied Business and Economics 11(3):11–18.
Schrading, N.; Alm, C. O.; Ptucha, R.; and Homan, C. 2015. An analysis of domestic abuse discourse on reddit. In Proceedings of the 2015 Conference on Empirical
Methods in Natural Language Processing, 2577–2583.

References

Spaht, C., and Rubin, H. 2012. An investment strategy for financial independence.
Journal of Accounting and Finance 12(3):38–53.

Achrekar, H.; Gandhe, A.; Lazarus, R.; Yu, S.-H.; and Liu, B. 2011. Predicting flu
trends using twitter data. In 2011 IEEE conference on computer communications
workshops (INFOCOM WKSHPS), 702–707. IEEE.

Steinbaur, T. 2012. Information and social analysis of reddit. Retrieved from TROYSTEINBAUER@ CS. UCSB. EDU.

Backstrom, L.; Kleinberg, J.; Lee, L.; and Danescu-Niculescu-Mizil, C. 2013. Characterizing and curating conversation threads: expansion, focus, volume, re-entry. In
Proceedings of the sixth ACM international conference on Web search and data mining, 13–22. ACM.
Benevenuto, F.; Rodrigues, T.; Cha, M.; and Almeida, V. 2009. Characterizing user
behavior in online social networks. In Proceedings of the 9th ACM SIGCOMM conference on Internet measurement, 49–62. ACM.
Blei, D. M.; Ng, A. Y.; and Jordan, M. I. 2002. Latent dirichlet allocation. In Advancesin neural information processing systems, 601–608.

Wang, H.; Can, D.; Kazemzadeh, A.; Bar, F.; and Narayanan, S. 2012. A system forreal-time twitter sentiment analysis of 2012 us presidential election cycle. In Proceedings of the ACL 2012 system demonstrations, 115–120. Association for Computational
Linguistics.
Way, W. L.; Wong, N.; and Gibbons, D. 2011. Online talk about money: An investigation of interactions around personal finance in social media. Madison, WI: Center
for Financial Security.
Wilson, C.; Boe, B.; Sala, A.; Puttaswamy, K. P.; and Zhao, B. Y. 2009. User interactions in social networks and their implications. In Proceedings of the 4th ACM
European conference on Computer systems, 205–218. Acm.

Xu, S. X., and Zhang, X. M. 2009. How do social media shape the information
environment in the financial market? ICIS 2009 Proceedings 56.