Investigating the phenomenon of NSFW posts in Reddit

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Investigating the phenomenon of NSFW posts in Reddit
Enrico Corradini1, Antonino Nocera2∗, Domenico Ursino1, Luca Virgili1
1Department of Information Engineering, Polytechnic University of Marche,
2Department of Electrical, Computer and Biomedical Engineering, University of Pavia,
∗Corresponding author
{e.corradini, l.virgili}@pm.univpm.it; antonino.nocera@unipv.it; d.ursino@univpm.it
Abstract
In this paper, we study the characteristics of NSFW (Not Safe For Work) posts in Reddit,
highlighting their differences from SFW (Safe For Work) posts, which have been much more studied
in the past literature. In our investigation, we studied all Reddit posts from 2019. Through both
descriptive analytics techniques and social network analysis techniques, we extract three findings
on the main differences between NSFW and SFW posts in Reddit. Thanks to these findings, we
are able to better understand the dynamics (authors, subreddits, readers) behind NSFW posts. In
particular, it becomes clear that this is a niche world where authors are strongly cohesive. However,
at the same time, the most popular ones show a clear opening to new authors, whom they are willing
to collaborate with, from the beginning.
Keywords: Reddit; NSFW Posts; NSFW Authors; Co-posting Authors; Assortativity of NSFW
Authors; Knowledge Extraction
1 Introduction
Reddit1is currently one of the most active social media. It has been extensively studied by researchers
in the past [19]. In [25], the authors present an interesting longitudinal analysis of the evolution of
this social medium. Furthermore, many papers have focused on specific aspects of this social network,
concerning, for example, community structures and interactions [27, 8, 10], user behavior [3, 14, 16],
structure and content of subreddits, posts and comments [24], structural properties [10, 13, 33], text
classification [15], user migration [21], political and ideological aspects [12, 31].
One aspect of Reddit worth to be analyzed involves NSFW (Not Safe For Work) posts. This term
refers to user-submitted content not suitable to be viewed in public or in professional contexts. The
phenomenon of NSFW posts in Reddit has been very little investigated, although it is very common
in this social medium. In fact, only a very small number of authors have analyzed it [17, 20]. The
term “NSFW” has been proposed since 1998, and is one of the oldest acronyms of the Internet. Since
its first appearance, many social media, such as Twitter, WhatsApp and Reddit, have adopted it to
1https://www.reddit.com
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indicate certain sections or contents. In addition, several authors have focused on the analysis of this
phenomenon in other social networks. The study about the role of images and selfies in NSFW content
of tumblr.com, presented in [28], and the analysis of the anonymity level of NSFW content in both
Twitter and Whisper, described in [7] are two examples.
In this paper, we give a contribution in this setting investigating the phenomenon of NSFW posts
in Reddit and describing the whole context (authors, subreddits and readers) behind it. For this
purpose, we consider a dataset that includes all the posts published in Reddit from January 1st, 2019
to December 31st, 2019.
During our investigation, we carried out three types of analysis, namely:
•Descriptive Analysis, to study the distributions of the entities involved in the phenomenon (e.g.,
the distribution of NSFW posts against subreddits, authors, score and comments).
•Social Network Analysis, to study the co-posting phenomenon, and therefore the interactions
between authors of NSFW posts.
•Assortativity Analysis, to extend and deepen the previous analyses to discover and study whether
possible forms of assortativity [22] exist among the authors of NSFW posts. Recall that assor-
tativity is a particular case of homophily in social networks [18], which indicates the tendency
of a node to cooperate with nodes having similar characteristics.
These analyses allowed us to extract three findings regarding NSFW posts, NSFW authors and
NSFW subreddits, respectively. Throughout our analysis, in most of the cases, we compare each
finding on NSFW posts with the corresponding one on SFW (Safe For Work) posts. Some of the
questions these findings provide an answer to are the following:
•What can be said about the spread of NSFW posts in the subreddits?
•What can be said about the quantity of posts an NSFW author usually submits?
•What can be said about the score of NSFW posts?
•What can be said about the number and the score of comments to NSFW posts?
•What can be said about the level of interconnection between authors of NSFW posts?
•Is there a backbone among experienced authors of NSFW posts? In other words, do they tend
to interact only with their peers (i.e., authors with the same level of experience), or are they
open to collaborations with new authors who have just started publishing NSFW posts?
Finally, we suitably combine the knowledge represented by the three findings in order to describe
the dynamics behind the phenomenon of NSFW posts in Reddit.
The rest of this paper is organized as follows: In Section 2, we present related literature. In
Section 3, we describe the dataset used in our analysis. In Section 4, we provide an overview of our
investigation activity. In Section 5, we study various distributions involving NSFW posts. In Section
6, we study several distributions regarding comments of NSFW posts. In Section 7, we investigate the
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co-posting activity of the authors of NSFW posts. In Section 8, we evaluate the assortativity of the
authors of NSFW posts. In Section 9, we combine the three findings derived during our investigations
in order to define an overall picture of this phenomenon. Finally, in Section 10, we draw our conclusions
and think of some possible developments of our research efforts.
2 Related literature
The term “NSFW” was first proposed in 1998 and it is one of the oldest acronyms of the Internet.
It refers to content that is not suitable to be viewed in a working environment. Since then, different
online systems, like Twitter, WhatsApp, many forums, and Reddit, have adopted this term to label
sections with posted content not adequate for everybody and, in general, not suitable for public and
professional contexts. Specifically, Reddit has introduced a dedicated group of contents called NSFW
to separate posts suitable to be enjoyed in any context from those that should be watched in private
environments.
Even if the contents of NSFW posts are considered side-contents to be kept separated from front-
end contents, several researchers have started to study the characteristics of these contents, as well as
the communities underneath them [17, 4, 28, 32, 7, 11].
From a high level analysis of the research efforts in the context of NSFW content, we may distin-
guish two main directions. The former focuses on understanding the main characteristics of people
publishing or viewing such materials, as well as the features of the NSFW content itself. The latter,
instead, uses features of NSFW content to build content detection and filtering solutions, often with
the objective of enabling/disabling the visualization of this material for users.
In particular, the work described in [28] is an example of the first research direction. Here, the
author investigates the role of images and selfies in NSFW contents of tumblr.com. NSFW contents,
having the explicit NSFW label assigned by their authors, are extracted from tumblr blogs. Then,
the described analysis focuses on images and reactions (interactions) surrounding them. The aim of
this study is understanding the different roles that people assign to images and selfies, leading to
the creation (or breaking) of trust relationships between users. Furthermore, the author provides
evidence that different opinions about the membership of an image to the NSFW category may lead
to violations of assumed trust between two individuals, thus causing the dissolution of a community.
Another contribution in the first research direction is the one reported in [7]. In this paper, the
authors try to understand both the nature of the content posted in anonymous social media and
the difference between NSFW content posted in these media and in non-anonymous ones (like, e.g.,
Twitter). To do this, they define an anonymity sensitivity metrics measuring how much users think
that a post should be anonymous. Then, they use this metrics, in conjunction with a human annotator,
to identify NSFW posts with the same level of anonymity sensitivity in Whisper (an anonymous media
system) and Twitter. Hence, they carry out a deep comparative analysis of the two sets of posts and
find that, actually, there is a strong difference between them, especially when it comes to the shades
or levels of anonymity and their linguistic features.
Even if its main focus is slightly different from the one defining this first research direction, the
work described in [17] gives a mentionable contribution in this setting. Indeed, the author considers
a particular protest carried out by moderators of Reddit in 2015, when participants disabled their
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subreddits to block posting activities. In this context, the author studies the different behavior of
NSFW and SFW subreddit moderators. The results show that, even if several confounding factors
could be considered to understand the underlying dynamics, NSFW subreddit moderators were more
inclined to join the protest and block posting activities.
In the second research direction mentioned above, several works have been published in recent
scientific literature [20, 9, 2, 32, 6]. For instance, the work described in [20] focuses on the protection
of minors accessing the Internet from the exposure to unwanted and harmful contents. The proposed
system can be seen as both an active content filtering solution, which protects the access of minor
users to NSFW content, and a watchdog constantly monitoring and moderating websites to avoid the
diffusion of unwanted content.
The problem of classifying video content as NSFW is faced in [9]. In this paper, the authors exploit
Convolutional Neural Networks (ConvNets) for extracting audio-video patterns from NSFW videos.
Specifically, they first extract separated audio and video features and then merge the two feature
sets to obtain a single feature vector. After that, they provide this vector in input to some baseline
classifiers. Even if the approach is naive, the achieved results outperform those of other methods, thus
proving the adequacy of this proposal.
Similarly, the approach of [2] makes use of a deep neural network based solution to identify content
belonging to the NSFW category. This approach is based on a residual network, which returns a
value specifying the probability that a given content belongs to NSFW category. Moreover, it allows
the computation of the degree of explicitness of the analyzed content, which can be used to feed a
filtering system. Finally, it is capable of labeling media content with tampered extension to warn users
about the potential risk of suspiciously unwanted material. The experiments show very interesting
performance for this approach, which reaches an accuracy of about 96% also on image and video
contents.
Still in this context, also the approach described in [32] makes use of a fast Convolutional Neural
Network (CNN) for the detection of both NSFW and SFW images. Specifically, this proposal deals
with the design of a neural network based solution to detect pictures with nudity in NSFW contents.
After that, it defines picture filtering strategies for online media services.
Finally, the approach described in [6] strives to build a classifier for detecting NSFW content by
looking at images and visual material in the post. The proposed solution uses a weighted sum of the
results of multiple deep neural network models. The weighted combination is obtained by learning
a linear regression model through Ordinary Least Squares. The authors prove that their solution
outperforms the state of the art solutions based on single CNN models. For this purpose, they present
a deep comparison on a manual labeled dataset.
Our approach is somehow near to the studies belonging to the first research direction introduced
above. However, these approaches only study the content of NSFW posts and none of them focus
on the structural network-based properties of NSFW and SFW posts and authors. Instead, we want
to study such differences between the two categories with a comparative approach and typical Social
Network Analysis methodologies.
The identified findings can be fundamental to improve existing techniques for content detection,
parental control or content filtering solutions, such as the ones mentioned above. To the best of our
knowledge, no similar studies have been conducted in social media platforms. Our paper aims at
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providing a first contribution in this setting using Reddit as reference social network. However, as we
will see below, our investigation strategy is general and can be specialized to other social media [5].
3 Dataset description
The dataset used for our analysis has been downloaded from the website pushshift.io [1], one of the
main Reddit data sources. In particular, we extracted all the posts published on Reddit from January
1st, 2019 to September 1st , 20192. The number of posts available for our analysis was 150,795,895.
In Reddit, an NSFW post must be marked as such by its author. Therefore, there is no need for
automatic labeling by Reddit or manual labeling by third-parties. If the user specifies that a post
she/he is publishing is NSFW, Reddit puts a red label when displaying it and sets the value of the
over 18 field in its database to true. We used the value of this field to separate NSFW posts from
SFW ones in our analyses.
We performed a preliminary ETL (Extraction, Transformation and Loading) activity on our
dataset. In Data Analytics, this activity is typically carried out prior to any data analysis campaign.
It aims at cleaning the data in the dataset, removing any errors and inconsistencies, integrating any
data from different sources, and transforming the cleaned and integrated data into a single format
chosen for the next data analysis tasks [23].
During the ETL phase, we observed that some of the available posts were made by authors who
had left Reddit. We decided to remove these posts from our dataset. At the end of this activity,
the number of available posts was 122,568,630. NSFW posts were 11,908,377, equivalent to 9.72% of
them.
As pointed out in the Introduction, the goal of our paper is to understand the characteristics of
NSFW posts and their authors, comparing them with the SFW posts and their authors. For this
reason, we decided to extract from the dataset described above two sub-datasets, with the same
number of posts each. Both of them are limited to January and February 2019. The first dataset D
contains only SFW posts, while the second, called D, stores only NSFW posts. We randomly selected
1,250,000 posts for each of them to reduce the datasets’ size and the computation time. It should be
noted that this number is absolutely in line with the number of posts generally used in the analyses
of Reddit [29, 21, 26, 12]. However, we repeated all the analyses on two other datasets D0and D0to
verify the stability of our results. The set D0(resp., D0) consists of 1,250,000 SFW (resp., NSFW)
posts published in March and April 2019, randomly selected from the original dataset. In addition,
we carried out a deeper stability check evaluating all posts of 2019 month by month (see Section 6.4).
As a preliminary analysis, we focused on the “context” of SFW and NSFW posts. Here, we use
the term “context” of a post to denote its author, its comments and the subreddits in which it was
published. In this analysis, we wanted to verify if the context of SFW posts and the one of NSFW
posts are the same or not. To answer this question, we calculated the values of some parameters on
Dand Dand, then, on D0and D0. The results obtained are shown in Table 1.
This table shows that the reference contexts for SFW and NSFW posts are basically independent.
2Actually, only for stability analysis, we considered all the posts from January 1st, 2019 to December 31st, 2019 (see
Section 6.4).
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Parameter Dand D D0and D0
Number of authors who published at least one SFW post 59,465 58,561
Number of authors who published only SFW posts 58,801 57,891
Percentage of authors publishing SFW posts who published only posts of this type 98.88% 98.52%
Number of authors who published at least one NSFW post 36,758 36,461
Number of authors who published only NSFW posts 36,094 36,131
Percentage of authors publishing NSFW posts who published only posts of this type 98,19% 99.09%
Number of subreddits containing at least one SFW post 89,360 92,445
Number of subreddits containing only SFW posts 82,050 85,157
Percentage of subreddits containing SFW posts that contain only posts of this type 91.82% 92.12%
Number of subreddits containing at least one NSFW post 41,365 45,910
Number of subreddits containing only NSFW posts 34,055 38,622
Percentage of subreddits containing NSFW posts that contain only posts of this type 82.33% 84.13%
Table 1: Parameters about the authors and the subreddits of SFW and NSFW posts - D(resp., D)
stores SFW (resp., NSFW) posts of January and February 2019, while D0(resp., D0) stores the same
kind of post but for March and April 2019
In fact, more than 98% of authors writing SFW posts do not write NSFW posts, and vice versa. In
addition, more than 91% of subreddits containing SFW posts do not contain NSFW posts, and more
than 82% of subreddits containing NSFW posts do not contain SFW posts. Another important result
is that all the computations are stable over time because the values obtained for January and February
2019 (Jan-Feb, for short) are very similar to the ones returned for March and April 2019 (Mar-Apr,
for short).
4 Overview of our investigation activity
Our investigation of the phenomenon of NSFW posts in Reddit follows the workflow shown in Figure 1.
Figure 1: The workflow representing the tasks of our investigation
Due to layout reasons, this figure shows the dataset in input only to the first module. Actually,
the dataset is provided in input to each module of the workflow. Similarly, the descriptive (resp.,
co-posting) knowledge, which are shown as an input for the co-posting (resp., assortativity) analysis
module, are also an output of the investigation activity.
As we can see in Figure 1, the first phase of our investigation consists of a descriptive analysis of the
phenomenon of NSFW posts in Reddit. In particular, this analysis extracts knowledge through several
distributions involved in the phenomenon (e.g., the distribution of NSFW posts against subreddits,
authors and scores, the distribution of the authors of NSFW posts against subreddits, etc.).
This knowledge, together with the original dataset, represents the input of the second phase of our
investigation. This analysis employs a social network derived from co-posting activities of authors.
In particular, the nodes of this social network represent the authors, whereas the edges indicate co-
posting activities between them. Starting from this social network, we study the co-posting activities
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of the authors of SFW and NSFW posts and extract the corresponding knowledge.
The result of this analysis, together with the original dataset, represents the input of the third
phase. It leverages the social network built in the previous phase to carry out analyses on the assor-
tativity of the authors of SFW and NSFW posts with respect to some forms of centrality. The results
obtained during this phase represent the last kind of knowledge returned as output by our approach.
5 Investigating distributions involving NSFW posts
In this section, we present some analyses directly involving NSFW and SFW posts. In particular, we
study the distribution of subreddits and authors against posts and the distribution of posts against
the scores assigned to them by Reddit users.
5.1 Distribution of subreddits against posts
We computed the distributions of the subreddits against NSFW and SFW posts for the datasets D
and D. The results obtained are reported in Figure 2.
This figure shows that the two distributions follow a power law. We also computed some parameters
for the two power law distributions; they are shown in the second and third columns of Table 2. To
verify the stability of results found, we made the same computations on D0and D0datasets. They are
shown in the fourth and fifth columns of Table 2.
Parameter SFW posts NSFW posts SFW posts NSFW posts
Jan-Feb Jan-Feb Mar-Apr Mar-Apr
Maximum number of subreddits 47,480 (53.13%) 18,332 (44.31%) 49,502 (53.24%) 21,034 (45.02%)
Number of subreddits of the 99 percentile 1,095 571 1,101 569
Maximum number of posts 25,006 (4.62%) 34,424 (4.57%) 26,650 (4.98%) 31,329 (4.76%)
Number of posts of the 99 percentile 7,719 9,862 7,721 9,859
Average number of subreddits 126 54 137 57
Average number of posts 767 981 768 905
α(power law parameter) 1.6539 1.6974 1.6767 1.6859
δ(power law parameter) 0.0266 0.0364 0.0306 0.0432
Table 2: Parameters of the distributions of subreddits against posts
From this table, we can observe that the maximum and the average numbers of subreddits for
SFW posts is more than twice the value obtained for NSFW posts. The maximum and the average
numbers of NSFW posts in a subreddit are slightly higher than SFW posts. There are no significant
differences in the αand δparameters of the two power law distributions. Indeed, both of them are
very steep. The comparison of the second and the third columns of Tables 2, on the one hand, and the
fourth and fifth columns of the same table, on the other hand, also tells us that the trends obtained
are stable over time, because their variations between Jan-Feb and Mar-Apr are not significant.
Although the two curves show almost identical trends, as confirmed by the similar values of αand
δ, we found interesting the differences in the maximum and average values. In other words, the curve
shapes are similar but the ranges of values are different. To confirm these results we compared the
two distributions through the Wilcoxon rank sum test [30].
This test indicated that the number of subreddits in which Jan-Feb SFW posts were published was
statistically significantly higher than the corresponding one of NSFW posts (τ= 2.8·10−4, p < 0.01).
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Figure 2: Log-log plots of the distributions of subreddits against SFW posts (on top) and NSFW posts
(on bottom) - Datasets regarding January and February 2019
This result can be explained taking into account the intrinsic nature of NSFW posts, whose content
is certainly less suitable for the general public than the one of SFW posts.
5.2 Distribution of authors against posts
Figure 3 shows the distributions of authors against SFW and NSFW posts for the datasets Dand D.
From the analysis of this figure we can see that both distributions follow a power law.
In Table 3, we report the main parameters of these two power law distributions for the datasets
Dand D, on one hand, and D0and D0, on the other hand.
A Wilcoxon rank sum test showed that the number of authors of Jan-Feb SFW posts was statis-
tically significantly higher than the corresponding one of NSFW posts (τ= 1.2·10−4, p < 0.01).
This result can also be explained taking into account the topics of NSFW posts. Indeed, these are
more specific than those involving SFW posts. Differently from SFW posts that can be written by
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Figure 3: Log-log plots of the distributions of authors against SFW posts (on top) and NSFW posts
(on bottom) - Datasets regarding January and February 2019
Parameter SFW posts NSFW posts SFW posts NSFW posts
Jan-Feb Jan-Feb Mar-Apr Mar-Apr
Maximum number of authors 555,854 (79.06%) 131,070 (56.43%) 551,863 (78.97%) 133,594 (57.01%)
Number of authors of the 99 percentile 11,471 5,055 11,469 5,052
Maximum number of posts 18,724 (11.85%) 16,383 (5.70%) 16,513 (10.98%) 15,674 (5.48%)
Number of posts of the 99 percentile 5,426 5,393 5,424 5,393
Average number of authors 2,190 439 2,083 416
Average number of posts 491 543 491 521
α(power law parameter) 1.4631 1.5566 1.4505 1.5435
δ(power law parameter) 0.0473 0.0353 0.0304 0.0287
Table 3: Parameters of the distributions of authors against posts
anyone, the authors who generally publish NSFW posts are a small circle of people almost exclusively
dedicated to this type of post. Consequently, while it is true that NSFW posts are much fewer than
SFW posts, it is also true that they are published by an extremely limited number of authors. This
explains the result.
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5.3 Distribution of posts against scores
A newly submitted post on Reddit has a score of 1. A user can upvote (resp., downvote) the post,
increasing (resp., decreasing) its score by 1. We have computed the distributions of SFW and NSFW
posts against scores for the datasets Dand D, and, then, for D0and D0, on the other hand. For the
sake of simplicity, in Table 4, we report the main parameters of these distributions, which again follow
a power law.
Parameter SFW posts NSFW posts SFW posts NSFW posts
Jan-Feb Jan-Feb Mar-Apr Mar-Apr
Maximum score 183,453 (57.98%) 106,947 (47.26%) 191,864 (61.87%) 112,830 (49.62%)
Number of score of the 99 percentile 4,746 3,645 4,825 3,275
Average score 9,881 4,191 8,809 3,819
α(power law parameter) 1.5998 1.5140 1.6061 1.5165
δ(power law parameter) 0.0197 0.0366 0.0154 0.0355
Table 4: Parameters of the distributions of posts against scores
A Wilcoxon rank sum test showed that the score of Jan-Feb SFW posts was statistically signifi-
cantly higher than the corresponding one of NSFW posts (τ= 0.00109, p < 0.01).
Once again, this result can be explained by the type of contents that generally characterizes NSFW
posts.
5.4 Analysis of positive and negative posts for SFW and NSFW cases
In the previous section, we have observed that each post has a score, initially equal to 1, which can
increase or decrease based on the upvotes or downvotes of users. Actually, Reddit does not report the
posts with a negative score in its database. For this reason, the values of the scores both in Reddit
and in pushshift.io range in the interval [0,+∞). In this setting, posts with a score equal to 0 are
particularly relevant, because they are the only ones that have been rated negatively by at least one
user, or have received more downvotes than upvotes.
We computed the distributions of authors against negative posts for both SFW and NSFW posts.
In both cases, we have found that they follow a power law. We report the main parameters of these
distributions in Table 5.
Parameter SFW posts NSFW posts SFW posts NSFW posts
Jan-Feb Jan-Feb Mar-Apr Mar-Apr
Maximum number of authors 66,162 (92.31%) 24,607 (74.86%) 61,254 (91.98%) 24,172 (73.87%)
Number of authors of the 99 percentile 40,028 11,606 40,024 11,598
Maximum number of posts 133 (9.64%) 460 (14.38%) 103 (8.98%) 399 (13.76%)
Number of posts of the 99 percentile 126 369 122 370
Average number of authors 1,666 505 1,691 544
Average number of posts 32 49 28 47
α(power law parameter) 1.4360 1.4349 1.5512 1.4360
δ(power law parameter) 0.0615 0.0,0616 0.0543 0.0616
Table 5: Parameters of the distributions of authors against negative posts
A Wilcoxon rank sum test showed that the number of authors of Jan-Feb SFW negative posts was
statistically significantly higher than the corresponding one of NSFW posts (τ= 5.1·10−4, p < 0.01).
These conclusions, although interesting, must be intertwined with those regarding positive posts,
to better characterize the features of negative ones. For this reason, we computed the distributions of
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authors against positive posts. Also in this case, the distributions follow a power law similar to the
previous ones. We report the values of the main parameters of these distributions in Table 6.
Parameter SFW posts NSFW posts SFW posts NSFW posts
Jan-Feb Jan-Feb Mar-Apr Mar-Apr
Maximum number of authors 522,540 (79.66%) 124,054 (56.56%) 519,774 (79.54%) 126,602 (56.89%)
Number of authors of the 99 percentile 9,083 4,346 9,080 4,352
Maximum number of posts 18,684 (11.88%) 16,383 (5.77%) 16,481 (10.67%) 15,564 (5.73%)
Number of posts of the 99 percentile 5,165 4,638 5,160 4,641
Average number of authors 2,018 418 1,944 394
Average number of posts 483 541 493 514
α(power law parameter) 1.4318 1.5145 1.4855 1.5498
δ(power law parameter) 0.0311 0.0263 0.0275 0.0291
Table 6: Parameters of the distributions of authors against positive posts
A Wilcoxon rank sum test indicated that the number of authors of Jan-Feb SFW positive posts was
statistically significantly higher than the corresponding one of NSFW posts (τ= 1.1·10−4, p < 0.01).
We now compare Tables 5 and 6 to extract the features characterizing negative posts versus positive
ones. There are no significant differences between positive and negative posts in the maximum and
average number of authors of NSFW and SFW posts. The same is true for the average number of
posts and the trends of the power law distributions. However, there is a very interesting aspect that
differentiates negative posts from positive ones. Indeed, the maximum number of negative posts is
much higher for NSFW posts than for SFW ones. This trend is not found in positive posts.
The explanation behind this result is the same as the one seen in Section 5.3.
5.5 Distribution of subreddits against authors
We computed the distributions of subreddits against the authors of SFW and NSFW posts. In both
cases, we saw that they follow a power law similar to those shown in the previous figures. We report
the values of the most important parameters in Table 7.
Parameter SFW posts NSFW posts SFW posts NSFW posts
Jan-Feb Jan-Feb Mar-Apr Mar-Apr
Maximum number of subreddits 62,839 (70.32%) 29,798 (72.03%) 65,861 (71.12%) 33,963 (72.01%)
Number of subreddits of the 99 percentile 932 538 930 533
Average number of subreddits 151 87 161 101
Maximum number of authors 20,285 (5.70%) 11,161 (4.70%) 21,801 (5.64%) 11,326 (4,59%)
Number of authors of the 99 percentile 6,435 4,627 6,431 4,635
Average number of authors 604 499 601 481
α(power law parameter) 1.7143 1.7992 1.6944 1.7343
δ(power law parameter) 0.0302 0.0.0382 0.0288 0.0362
Table 7: Parameters of the distributions of subreddits against authors
A Wilcoxon rank sum test showed that: (i) the number of subreddits of Jan-Feb SFW posts was
statistically significantly higher than the corresponding one of NSFW posts; (ii) the number of authors
of Jan-Feb SFW posts was statistically significantly higher than the corresponding one of NSFW posts
(τ= 6.3·10−4, p < 0.01).
The explanation behind this result is essentially related to the fact that NSFW posts have particular
contents that are of interest to a minority of people. Therefore, they are published in a limited number
of subreddits.
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In the next analyses, to save space, we will avoid highlighting those cases where the values αand
δof power law distributions are similar, as well as those cases where the parameter values are stable
when switching from Jan-Feb to Mar-Apr. Only if one or both of these conditions are not valid in
some analysis, we will explicitly highlight this situation.
6 Investigating distributions on comments to NSFW posts
In this section, we analyze the comments to NSFW posts investigating their authors, the scores they
get and the subreddits they are submitted to.
6.1 Distribution of comments against posts
The distributions of comments against SFW posts and NSFW posts follow a power law. Table 8 shows
the values of the main parameters of these distributions.
Parameter SFW posts NSFW posts SFW posts NSFW posts
Jan-Feb Jan-Feb Mar-Apr Mar-Apr
Maximum number of posts 499,068 (2.29%) 667,942 (5.79%) 522,477 (2.94%) 676,606 (5.81%)
Number of posts of the 99 percentile 8,257 10,707 8,362 10,719
Maximum number of comments 41,478 (39.93%) 28,227 (53.43%) 36,283 (40.01%) 23,485 (51.32%)
Number of comments of the 99 percentile 10,582 21,983 9,985 22,735
Average number of comments 1,237 771 1,402 656
α(power law parameter) 1.4836 1.3990 1.4779 1.4353
δ(power law parameter) 0.0178 0.0304 0.0160 0.0291
Table 8: Parameters of the distributions of comments against posts
A Wilcoxon rank sum test showed that the number of comments of Jan-Feb SFW posts was
statistically significantly higher than the corresponding one of NSFW posts (τ= 8.68 ·10−5, p < 0.01).
As a further investigation on this topic, we considered both the top 150 most commented SFW and
NSFW posts. As a first analysis, we observed that SFW (resp., NSFW) posts have been submitted by
141 (resp., 130) authors in 55 (resp., 77) different subreddits. This result highlights that there is no
author or subreddit able to monopolize post comments. Indeed, the phenomenon is highly distributed.
Then, we computed the distributions of the number of these comments against subreddits. They
are reported in Figure 4. Plots (a) and (b) of this figure show that the two distributions follow
a power law. We computed the parameter values of these power laws and we obtained α= 3.41
and δ= 0.075 for SFW post comments, and α= 3.53 and δ= 0.07 for NSFW post comments.
A Wilcoxon rank sum test indicated that the number of comments associated with the subreddits
containing Jan-Feb SFW posts was statistically significantly higher than the corresponding one of
NSFW posts (τ= 0.16493, p < 0.01).
Finally, we computed the distribution of the number of these comments against authors. Also
in this case, we found that it follows a power law. The values of the corresponding parameters are
α= 3.06 and δ= 0.03 for SFW post comments and α= 2.20 and δ= 0.03 for NSFW post comments.
The conclusions about the trend and the values are analogous to the previous ones.
A Wilcoxon rank sum test indicated that the number of comments for Jan-Feb SFW posts was
statistically significantly higher than the corresponding one of NSFW posts (τ= 0.34951, p < 0.01).
The motivations behind this result are the same as those in Section 5.5.
12

Figure 4: Distributions of comments to the top 150 most commented SFW posts (on top) and NSFW
posts (on bottom) against subreddits - Datasets regarding January and February 2019
6.2 Distribution of subreddits against comments
We computed the distributions of subreddits against the comments to SFW and NSFW posts. In
both cases we obtained that they follow a power law and show trends similar to those shown in the
previous figures. The main parameters of these distributions are reported in Table 9.
A Wilcoxon rank sum test showed that the number of comments associated with the subreddits
containing Jan-Feb SFW posts was statistically significantly higher than the corresponding one of
NSFW posts (τ= 6.34 ·10−6, p < 0.01).
Once again, the motivations behind this result are the same as those in Section 5.5.
13

Parameter SFW posts NSFW posts SFW posts NSFW posts
Jan-Feb Jan-Feb Mar-Apr Mar-Apr
Maximum number of comments 484,792 (5.45%) 301,040 (9.17%) 462,415 (5.41%) 244,912 (9.73%)
Number of comments of the 99 percentile 47,590 25,056 47,698 28,635
Average number of comments 3,942 2,607 3,800 2,391
α(power law parameter) 1.8025 1.7659 1.7981 1.7507
δ(power law parameter) 0.0236 0.0235 0.0217 0.0310
Table 9: Parameters of the distributions of subreddits against comments
6.3 Distribution of comments against scores
We computed the distributions of comments to SFW and NSFW posts against scores. They are
reported in Figures 5 and 6 for the datasets Dand D. These figures show that the corresponding
distributions do not follow a power law, and this is the first case. As we can see from figures, the
distributions are irregular, even if both of them seem having a Gaussian trend.
Figure 5: Distribution of comments to SFW posts against scores - Datasets regarding January and
February 2019
Also in this case, we computed some parameters for the two distributions. They are shown in
Table 10.
Parameter SFW posts NSFW posts SFW posts NSFW posts
Jan-Feb Jan-Feb Mar-Apr Mar-Apr
Average score 9,881 4,191 8,809 3,819
Score of the last comment of the first quartile 2,035 1,157 1,993 1,215
Score of the last comment of the second quartile 4,686 2,357 4,551 2,484
Score of the last comment of the third quartile 11,106 4,486 9,953 4,667
Score of the last comment of the fourth quartile 202,696 69,591 209,154 71,566
Table 10: Parameters of the distributions of comments to posts against scores
A Wilcoxon rank sum test indicated that the score of comments for Jan-Feb SFW posts was
statistically significantly higher than the corresponding one of NSFW posts (τ= 5.88 ·10−5, p < 0.01).
The motivations behind this result are the same as those behind the knowledge extraction in
Section 5.3.
14

Figure 6: Distribution of comments to NSFW posts against scores - Datasets regarding January and
February 2019
6.4 A deeper analysis of the stability of results
All the distributions we have seen so far are based on a data sample recovered from January 1st, 2019
to September 1st, 2019. Due to computational complexity reasons, we could not process the whole
sample at the same time and, therefore, we divided it into bi-months, i.e. Jan-Feb and Mar-Apr. In
all the distributions we have presented so far, we could verify that the Jan-Feb and Mar-Apr data led
to very similar results. This is a strong remark of the stability of the results of our investigations.
However, before continuing with the next analyses, which will have an even higher computational
complexity, we decided to carry out a further stability check. To this end, we considered all the posts
published in Reddit from January 1st, 2019 to December 31st, 2019, and split them months by months.
Then, for each month, we computed several parameters previously seen for the two bi-months. The
results obtained are shown in Table 11 for SFW posts, and in Table 12 for NSFW posts. The analysis
of these tables fully confirms that the results of our investigations are stable.
7 Investigating co-posting activity of the authors of NSFW posts
The goal of this analysis is to verify whether there is any correlation between the authors of NSFW
posts. As usual, we will extract the information of interest and we will compare the behavior of
authors of NSFW posts with the ones of SFW posts. In this activity, we will use a support data
structure that we call co-posting network. Having observed in all the previous experiments that the
results obtained for the Jan-Feb datasets (i.e., Dand D) are stable, from now on we will refer to these
two datasets only, avoiding to report the analysis of Mar-Apr datasets, too. In addition, since most
of the operations that we will perform on the co-posting network are computationally expensive, we
randomly extracted a subset D∗(resp., D∗) of D(resp., D) consisting of 75,000 SFW (resp., NSFW)
posts to work on.
As a first task of this analysis, we give a formal definition of the co-posting network P(resp., P)
built from the authors of SFW (resp., NSFW) posts stored in D∗(resp., D∗).
15

Parameter Jan Feb Mar Apr May Jun
GENERAL CHARACTERISTICS
Number of authors who published at least one SFW post 391,898 387,458 365,785 389,154 387,562 374,531
Number of authors who published only SFW posts 380,261 374,564 359,851 378,582 377,423 365,751
Percentage of authors publishing SFW posts who published only posts of this type 97.03% 96.67% 98.37% 97.28% 97.38% 97.65%
Number of subreddits containing at least one SFW post 58,843 57,965 58,786 57,653 58,426 57,953
Number of subreddits containing only SFW posts 54,189 53,482 53,952 54,236 54,873 52,432
Percentage of subreddits containing SFW posts that contain only posts of this type 92.09% 92.22% 91.77% 94.07% 93.91% 90.47%
DISTRIBUTION OF SUBREDDITS AGAINST POSTS
Maximum number of subreddits 47,480 47,116 47,996 49,502 48,294 47,733
Maximum number of posts 25,006 23,746 26,055 26,650 28,743 24,211
Average number of subreddits 125 120 154 141 133 118
Average number of posts 762 599 768 698 747 703
α(power law parameter) 1.6321 1.5806 1.7512 1.8358 1.6293 1.7024
δ(power law parameter) 0.0256 0.0238 0.0362 0.0357 0.0263 0.029
DISTRIBUTION OF AUTHORS AGAINST POSTS
Maximum number of authors 555,854 559,602 566,139 540,511 551,863 541,585
Maximum number of posts 18,724 17,401 18,268 16,513 17,226 19,949
Average number of authors 2,106 1,862 2,280 2,164 2,021 2,209
Average number of posts 487 533 434 548 620 462
α(power law parameter) 1.4531 1.6718 1.3565 1.399 1.5478 1.3742
δ(power law parameter) 0.0465 0.0359 0.0545 0.0233 0.0428 0.0757
DISTRIBUTION OF POSTS AGAINST SCORES
Maximum score 183,453 185,056 180,553 191,864 180,578 179,099
Average score 9,826 8,651 9,594 9,576 8,901 9,415
α(power law parameter) 1.5986 1.631 1.4672 1.6026 1.6507 1.5681
δ(power law parameter) 0.0189 0.0186 0.0198 0.0086 0.0179 0.0359
DISTRIBUTION OF SUBREDDITS AGAINST AUTHORS
Maximum number of subreddits 62,839 65,934 70,585 65,861 63,087 62,325
Average number of subreddits 149 145 154 150 133 148
Maximum number of authors 20,285 19,571 18,808 21,801 20,029 19,801
Average number of authors 603 623 584 678 587 650
α(power law parameter) 1.7185 1.7064 1.6209 1.608 1.7013 1.7853
δ(power law parameter) 0.0298 0.0485 0.0315 0.02 0.0379 0.0327
Parameter Jul Ago Sep Oct Nov Dec
GENERAL CHARACTERISTICS
Number of authors who published at least one SFW post 59,465 60,563 59,489 59,873 58,985 60,236
Number of authors who published only SFW posts 58,801 59,423 58,965 58,742 58,632 59,542
Percentage of authors publishing SFW posts who published only posts of this type 98.88% 98.11% 99.11% 98.11% 99.40% 98.84%
Number of subreddits containing at least one SFW post 89,360 87,953 89,236 88,462 87,932 88,167
Number of subreddits containing only SFW posts 82,050 82,587 85,496 83,647 83,146 84,963
Percentage of subreddits containing SFW posts that contain only posts of this type 91.82% 90.74% 93.68% 91.76% 91.7% 94.4%
DISTRIBUTION OF SUBREDDITS AGAINST POSTS
Maximum number of subreddits 46,283 46,882 48,777 47,676 48,886 47,070
Maximum number of posts 22,261 19,071 23,642 29,330 26,346 28,419
Average number of subreddits 158 99 116 109 110 120
Average number of posts 794 889 814 704 748 713
α(power law parameter) 1.582 1.8481 1.7838 1.7313 1.5937 1.5125
δ(power law parameter) 0.0186 0.0305 0.0535 0.0329 0.0468 0.0154
DISTRIBUTION OF AUTHORS AGAINST POSTS
Maximum number of authors 541,585 574,678 542,568 569,611 576,835 556,736
Maximum number of posts 16,823 19,320 18,692 18,460 16,499 17,766
Average number of authors 2,377 2,298 1,919 1984 2,008 2,123
Average number of posts 441 579 429 614 264 551
α(power law parameter) 1.3323 1.406 1.4688 1.4054 1.3093 1.525
δ(power law parameter) 0.0713 0.0491 0.0561 0.0424 0.064 0.038
DISTRIBUTION OF POSTS AGAINST SCORES
Maximum score 194,305 176,975 164,394 186,004 172,001 177,739
Average score 10,449 9,926 9,103 9,813 8,434 9,345
α(power law parameter) 1.5089 1.5785 1.4772 1.6389 1.4331 1.6354
δ(power law parameter) 0.0114 0.054 0.0245 0.0389 0.0226 0.0012
DISTRIBUTION OF SUBREDDITS AGAINST AUTHORS
Maximum number of subreddits 59,963 57,573 59,898 52,885 62,111 63,232
Average number of subreddits 145 144 163 155 153 154
Maximum number of authors 18,901 20,056 20,285 19,962 21,078 20,909
Average number of authors 686 673 811 543 611 651
α(power law parameter) 1.7622 1.6287 1.4544 1.8174 1.5256 1.7388
δ(power law parameter) 0.0159 0.0263 0.043 0.0254 0.0184 0.0378
Table 11: Monthly trend of some parameters related to SFW posts
Formally speaking,
16

Parameter Jan Feb Mar Apr May Jun
GENERAL CHARACTERISTICS
Number of authors who published at least one NSFW post 36,758 35,452 36,542 36,874 36,863 36,453
Number of authors who published only NSFW posts 36,094 35,259 36,501 36,165 36,135 36,023
Percentage of authors publishing NSFW posts who published only posts of this type 98.19% 99.45% 99.88% 98.07% 98.02% 98.82%
Number of subreddits containing at least one NSFW post 41,365 40,985 41,298 41,547 41,235 40,958
Number of subreddits containing only NSFW posts 34,055 33,254 34,587 32,982 33,563 34,159
Percentage of subreddits containing NSFW posts that contain only posts of this type 82.33% 81.13% 83.74% 79.38% 81.39% 83.40%
DISTRIBUTION OF SUBREDDITS AGAINST POSTS
Maximum number of subreddits 18,332 17,985 19,547 21,034 20,135 20,235
Maximum number of posts 34,424 32,547 31,854 31,329 30,896 32,541
Average number of subreddits 53 52 50 54 55 52
Average number of posts 892 890 896 901 895 890
α(power law parameter) 1.6896 1.6721 1.6874 1.6852 1.6796 1.6852
δ(power law parameter) 0.0258 0.0254 0.0251 0.0254 0.0214 0.0261
DISTRIBUTION OF AUTHORS AGAINST POSTS
Maximum number of authors 131,070 130,152 131,250 133,594 131,452 132,654
Maximum number of posts 16,383 16,125 14,214 15,674 16,540 14,210
Average number of authors 437 432 435 441 432 436
Average number of posts 541 542 540 542 544 539
α(power law parameter) 1.5463 1.7985 1.6222 1.8407 1.9456 1.4833
δ(power law parameter) 0.03345 0.0233 0.0239 0.0639 0.0388 0.0458
DISTRIBUTION OF POSTS AGAINST SCORES
Maximum score 106,947 146,561 75,657 112,830 105,566 66,095
Average score 8,805 9,170 7,123 7,885 9,287 10,197
α(power law parameter) 1.6062 1.5162 1.6933 1.8989 1.6951 1.4956
δ(power law parameter) 0.0145 0.0265 0.042 0.0611 0.0346 0.0139
DISTRIBUTION OF SUBREDDITS AGAINST AUTHORS
Maximum number of subreddits 62,839 63,382 61,204 33,963 50,609 53,781
Average number of subreddits 150 151 140 148 162 163
Maximum number of authors 20,285 17,549 19,347 11,326 18,495 19,324
Average number of authors 603 600 636 533 538 647
α(power law parameter) 1.7156 1.7682 1.6166 1.9204 1.753 1.6321
δ(power law parameter) 0.0312 0.0241 0.0384 0.0236 0.0187 0.0418
Parameter Jul Ago Sep Oct Nov Dec
GENERAL CHARACTERISTICS
Number of authors who published at least one NSFW post 37,165 35,986 36,432 36,540 36,354 36,589
Number of authors who published only NSFW posts 36,984 35,421 35,962 35,986 35,756 35,852
Percentage of authors publishing NSFW posts who published only posts of this type 99.51% 98.42% 98.77% 98.48% 98.35% 97.98%
Number of subreddits containing at least one NSFW post 41,542 40,986 41,246 41,258 40,983 41,496
Number of subreddits containing only NSFW posts 34,478 33,352 34,254 34,165 33,241 33,986
Percentage of subreddits containing NSFW posts that contain only posts of this type 82.99% 81.37% 83.04% 82.80% 81.10% 81.90%
DISTRIBUTION OF SUBREDDITS AGAINST POSTS
Maximum number of subreddits 20,135 18,564 17,423 19,631 18,328 20,124
Maximum number of posts 30,451 32,598 30,125 29,874 34,210 32,021
Average number of subreddits 50 59 52 53 51 50
Average number of posts 891 885 891 889 893 891
α(power law parameter) 1.6236 1.6454 1.59874 1.6598 1.6432 1.6953
δ(power law parameter) 0.0265 0.0259 0.0298 0.0265 0.0264 0.0254
DISTRIBUTION OF AUTHORS AGAINST POSTS
Maximum number of authors 130,254 134,250 133,247 132,478 136,587 131,489
Maximum number of posts 16,125 14,256 15,879 16,325 14,369 16,362
Average number of authors 436 435 431 442 429 432
Average number of posts 543 540 539 551 543 544
α(power law parameter) 1.6992 1.4551 1.5295 1.5527 1.5524 1.6091
δ(power law parameter) 0.0446 0.048 0.0201 0.0268 0.0031 0.0428
DISTRIBUTION OF POSTS AGAINST SCORES
Maximum score 97,462 143,430 102,590 100,844 104,027 81,167
Average score 7,866 8,613 8,801 11,050 7,148 8,012
α(power law parameter) 1.6422 1.5874 1.4948 1.7059 1.7936 1.3969
δ(power law parameter) 0.040 0.028 0.0386 0.0324 0.0184 0.0354
DISTRIBUTION OF SUBREDDITS AGAINST AUTHORS
Maximum number of subreddits 49,210 76,791 64,241 54,351 50,864 34,037
Average number of subreddits 127 146 136 170 120 139
Maximum number of authors 17,425 20,605 23,952 20,608 18,613 16,594
Average number of authors 592 591 657 708 600 545
α(power law parameter) 1.7653 1.7342 1.5258 1.9738 1.6143 1.5882
δ(power law parameter) 0.0317 0.037 0.0204 0.0371 0.0207 0.0401
Table 12: Monthly trend of some parameters related to NSFW posts
P=hN, E i P =hN , Ei
17

Here, N(resp., N) is the set of the nodes of P(resp., P). There is a node ni∈N(resp., N)
for each author aiof SFW (resp., NSFW) posts of D∗(resp., D∗). There is an edge (ni, nj, wij )∈E
(resp., E) if the authors aiand aj(associated with niand nj, respectively) submitted at least one
post in the same subreddit. wij is the number of subreddits having at least one SFW (resp., NSFW)
post of aiand, simultaneously, at least one SFW (resp., NSFW) post of aj.
Then, we calculated some of the basic parameters of Pand P; they are shown in Table 13. From
the analysis of this table, we can deduce that:
•The number of co-posting authors of NSFW posts is smaller than the number of co-posting
authors of SFW posts.
•The authors of NSFW posts are more interconnected with each other. This is shown by both
the density of P(which is about three times the one of P) and the average degree of P(which is
much greater than twice the degree of P). As we will see in the following, this can be explained
considering that they are authors belonging to a niche context.
•The average clustering coefficient of Pis greater than the one of P, but not as much as the
density. This suggests that in Pfewer triads are closed than in P. This implies that, probably,
in Pthere are more “bridge” authors than in P. These authors tend to act as intermediaries
between other authors who do not know each other. They could be expert authors who cooperate
with many new authors initially unknown to each other.
Parameter P P
Number of nodes 59,465 36,758
Number of edges 3,164,169 5,398,082
Density 0.001789 0.007990
Maximum Degree 2,593 3,670
Average Degree 106.42 293.70
Average Clustering Coefficient 0.7388 0.7755
Table 13: Basic parameters of the co-posting networks Pand P
After this, we computed the distribution of the nodes of Pand Pagainst their degree centrality.
The results obtained are reported in Figures 7 and 8.
From the analysis of these figures we can see that both distributions follow a power law. We
computed the corresponding values of αand δand obtained that α= 2.2929 and δ= 0.0470 for P
and α= 2.6811 and δ= 0.0678 for P. These values tell us that the two distributions are similar.
Furthermore, looking carefully at the distributions in Figures 7 and 8, it emerges another unex-
pected, extremely peculiar, feature. In fact, we can observe some spikes. Excluding that these spikes
are noise, they could be caused by the fact that the networks Pand Pare actually disconnected and
each network consists of a set of connected components. We found extremely interesting to check if
this hypothesis was true. Therefore, we carried out this analysis and verified that, actually, we were
right. In fact, we found that Pconsists of 15,952 connected components. Of these, 11,514 are made
up of a single node. The maximum connected component includes 21,364 nodes (equal to 35,92% of
the network nodes) and 2,909,206 arcs (equal to 91.94% of the network arcs). The distribution of
the connected components against their size (i.e., the number of nodes they include) follows a power
18

Figure 7: Distribution of the nodes of Pagainst their degree centrality - linear scale (on top) and
log-log scale (on bottom)
law with α= 1.562 and δ= 0.060. The network Pconsists of 6,032 connected components, where
5,214 are made of a single node. The maximum connected component comprises 28,165 nodes (equal
to 76.62% of the network’s nodes) and 5,382,255 arcs (equal to 99.71% of the network’s arcs). The
distribution of the connected components against their size follows a power law with α= 1.548 and
δ= 0.065.
The analysis of connected components strengthens some results obtained previously, in particular:
(i) the number of co-posting authors of SFW posts is greater than the corresponding number of co-
posting authors of NSFW posts; (ii) the authors of NSFW posts are more connected to each other
(probably due to the presence of the “bridge” users mentioned above) than the ones of SFW posts.
At this point, we wanted to investigate more on the behavior of the authors of SFW and NSFW
posts. Specifically, we treated three activities, namely the writing of posts, the tendency to publish
on many subreddits and the ability to attract interest. For each of these activities, we selected the
19

Figure 8: Distribution of the nodes of Pagainst degree centrality - linear scale (on top) and log-log
scale (on bottom)
top-ten authors from the maximum connected component of Pand Pand we studied their behavior.
In particular, Figure 9 (resp., 10 and 11) shows the top-ten authors who wrote the highest number of
posts (resp., published in the largest number of subreddits, received the highest number of comments).
The left part of this figure refers to the authors of SFW posts (belonging to the network P), while the
right part refers to the authors of NSFW posts (belonging to the network P).
These figures altogether outline a very precise author behavior. In fact, it can be noted that,
regardless of the activity considered, the authors of SFW posts show a power law distribution, while
the authors of NSFW posts show a very slowly decreasing distribution. This allows us to conclude
that there are few very active authors of SFW posts and many inactive ones in Reddit. By contrast,
there are many quite active authors of NSFW posts. Once again, it seems that these last tend to
“team up” much more than the ones of SFW posts.
These results can be explained considering that the phenomenon of NSFW posts is a niche one
20

Figure 9: Top-ten authors who submitted more posts - authors of SFW posts at left and of NSFW
posts at right
Figure 10: Top-ten authors who published on more subreddits - authors of SFW posts at left and of
NSFW posts at right
Figure 11: Top-ten authors who received more comments - authors of SFW posts at left and of NSFW
posts at right
involving mostly particular kinds of user. These are very cohesive and form a fairly closed group. On
the other hand, as we will see better in Section 9, all the knowledge extracted confirms this reasoning
about the context behind NSFW posts.
8 Evaluating assortativity of the authors of NSFW posts
The concept of “assortativity”, or “assortative mixing”, in a social network points out the predilection
of its nodes to be connected with other nodes that are somehow similar to them. This concept,
21

introduced by Newman [22], can be seen as an evolution of the concept of homophily [18], typical of
Social Network Analysis. Assortativity is orthogonal to node similarity metrics considered, even if
most of the authors in the literature have studied it with respect to node degree. According to this
definition of assortativity, the nodes of a social network tend to be linked with other nodes having a
degree similar to their own.
Assortativity is considered an extremely important property to be investigated by social network
researchers. So we decided to analyze it for the authors of SFW and NSFW posts in Reddit. We
would also pinpoint that: (i) like in the previous analyses performed in this paper, the goal is to
characterize the assortativity of the authors of NSFW posts versus the one of the authors of SFW
posts; (ii) the similarity property we decided to test for assortativity is node degree, because it is the
most investigated one in the past literature on assortativity3.
To carry out our assortativity analyses, we used the co-posting networks Pand Pdefined in
Section 7. We showed the distributions of the nodes of these networks against degree centrality in
Figures 7 and 8. As a first task, we sorted the authors of the two networks in descending order of
degree centrality. After that, we splitted this ordered list into intervals. In particular, we considered
40 equi-width intervals {I1,I2,· · · ,I40}for Pand {I1,I2,· · · ,I40}for P. Since the number of nodes
of P(resp., P) was 59,465 (resp., 36,578), each interval Ik(resp., Ik) contained 1,487 (resp., 915)
authors4.
At this point, we considered the interval I1(resp., I1) and, for each interval Ik(resp., Ik), we
determined how many authors of I1(resp., I1) were connected to at least one author of Ik(resp., Ik).
The results obtained are shown in Figure 12(a) (resp., 12(c)). Next, we computed the percentage of
the authors of Ik(resp., Ik), who were connected to at least one author of I1(resp., I1). The results
obtained are shown in Figure 12(e) (resp., 12(g)).
The analysis of Figures 12(a) and 12(e) shows a close correlation (i.e., a sort of backbone) between
the authors of SFW posts with the highest degree centrality. On the contrary, the analysis of Figures
12(c) and 12(g) shows that this phenomenon does not occur for the authors of NSFW posts.
In order to evaluate the statistical significance of this result, we generated a null model to compare
our outcomes with those of an unbiasedly random scenario. In particular, we built our null model
shuffling the arcs of P(resp. P) among the nodes of this network. In this way, we left the original
characteristics of P(resp. P) unchanged, except for the distribution of co-posting activities, which
became unbiasedly random in the null model. The results obtained are shown in Figures 12(b), 12(d),
12(f) and 12(h).
Comparing Figures 12(b) and 12(f) with Figures 12(a) and 12(e) we can see that the represented
distributions are similar. Indeed, many of the ranges with the highest values of Figures 12(a) and
12(e) continue to reach the highest values in Figures 12(b) and 12(f), too. However, these values are
much smaller in the latter case. Therefore, we can conclude that the behavior observed in Figures
12(a) and 12(e) is not random, but intrinsic to P(and, therefore, to the authors of SFW posts in
Reddit). On the contrary, if we consider Figures 12(c) and 12(g) (regarding the authors of NSFW
posts in Reddit) and compare them with Figures 12(d) and 12(h), we can see that this phenomenon
3Actually, at the end of this section, for a further evidence of the results obtained, we also considered eigenvector
centrality, beside degree centrality.
4Actually, the last interval had a slightly smaller size equal to 1,472 (resp., 893) authors.
22

(a) (b)
(c) (d)
(e) (f)
(g) (h)
Figure 12: Degree Assortativity of the authors of NSFW and SFW posts (high degree authors)
does not occur for the authors of P.
The above analysis suggests that there is a degree assortativity among the authors of SFW posts
23

but not among the authors of NSFW posts. However, in order to confirm the assortativity of the
authors of SFW posts, we need to verify whether this trend is still valid for the authors with an
intermediate degree centrality and for those with a low degree centrality. If we want to make an
exhaustive analysis, we should repeat the tasks previously performed for I1(resp., I1) for all the
40 intervals. For lack of space, we will limit our analysis to the intervals I20 (resp., I20), as the
representative of those with intermediate degree centrality, and I30 (resp., I30), as the representative
of those with low degree centrality5.
Figure 13(a) (resp., 13(c)) shows the number of authors of I20 (resp., I20 ) connected with at least
one author of Ik(resp., Ik), while Figure 13(e) (resp., 13(g)) shows the percentage of authors of Ik
(resp., Ik) connected with at least one author of I20 (resp., I20). The analysis of these figures suggests
the existence of a close correlation among the authors of SFW posts with an intermediate degree of
centrality; this correlation does not exist for the authors of NSFW posts.
Even in this case, we compared these findings with those obtained in the null model. The latter
are shown in Figures 13(b), 13(d), 13(f) and 13(h). Looking at all the diagrams reported in Figure 13,
once again we can conclude that the observed behavior is not random, but it is a property of Reddit.
In the light of the last observation and of the previous conclusions on authors with an intermediate
and a high degree centrality, we can certainly assert that there is no degree assortativity for the authors
of NSFW posts. Instead, the possibility that such assortativity exists for the authors of SFW posts
remains open.
In order to verify this last possibility, we carried out a study on the authors of I30. Figure 14(a)
shows the number of authors of I30 connected to at least one author of Ik, while Figure 14(c) shows
the percentage of authors of Ikconnected to at least one author of I30. These figures reveal the
presence of a close correlation between the authors of SFW posts with a low degree centrality.
Even in this case, we compared the results obtained with those returned using the null model. We
report the latter in Figures 14(b) and 14(d). The comparison of these figures with Figures 14(a) and
14(c) confirms that the behavior observed for these authors is an intrinsic property of Reddit.
Having verified that there is a sort of backbone among the authors of SFW posts with high (resp.,
medium, low) degree centrality, we can conclude that there is a degree assortativity for the authors of
SFW posts in Reddit. Instead, this property is absent for the authors of NSFW posts in Reddit.
A further interesting analysis is to check if the tendency of the authors of SFW posts to be
assortative and the tendency of the authors of NSFW posts to be not assortative is general or strongly
depends on the type of assortativity that is being considered (in this case, degree assortativity).
As a premise to this discussion, it should be pointed out that every form of assortativity is inde-
pendent, so it is impossible to come to a general rule. However, the analysis previously mentioned
could surely lead us to discover some trends.
Therefore, we chose a second form of centrality (in particular, the eigenvector centrality) and we
repeated all the steps previously taken for degree centrality with this second one.
The results obtained are very similar to those we have seen for degree centrality, i.e., we found the
5We did not choose the intervals Ik(resp., Ik), k > 30, because, during the analysis of the connected components,
we saw that there is a high number of isolated nodes in P(resp., P) - see Section 7. Clearly, these nodes belong to the
highest intervals and, if considered, could represent a bias in our analysis. To avoid this bias, we chose to not consider the
intervals where they reside, and to select I30 (resp., I30) as the representative of the intervals with low degree centrality.
24

(a) (b)
(c) (d)
(e) (f)
(g) (h)
Figure 13: Degree Assortativity of the authors of NSFW and SFW posts (medium degree authors)
existence of a strong eigenvector assortativity for the authors of SFW posts and a lack of eigenvector
assortativity for the authors of NSFW posts. For space reasons, we cannot show all the results.
25

(a) (b)
(c) (d)
Figure 14: Degree Assortativity of the authors of SFW posts (low degree authors)
However, in order to give an idea of them, in Figure 15, we report what happens for authors with
high eigenvector centrality. Comparing this figure with Figure 12, we can observe a strong similarity
in the authors behavior in the two cases. As a consequence, we can say that SFW authors tend to be
assortative, while NSFW authors tend to be not assortative.
This result can be explained by the strong community sense of the authors of NSFW posts. They
are so cohesive that they do not feel the need to split into groups of peers. The most active people
are still willing to interact with everyone else and not only with other equally active people.
9 Discussion
Combining together all the previous results, we can define three main findings related to posts, authors
and subreddits, respectively. Some of these findings are made up of several sub-findings.
The three findings are the following:
PF (Finding on NSFW posts)
1. NSFW posts are generally published in much fewer subreddits, have much lower scores
and are much less commented than SFW posts.
2. The scores of comments to NSFW posts are much lower than the ones to SFW posts.
AF (Finding on NSFW authors)
26

(a) (b)
(c) (d)
(e) (f)
(g) (h)
Figure 15: Eigenvector Assortativity of the authors of NSFW and SFW posts (high degree authors)
1. NSFW authors tend: (i) to publish more posts, (ii) to publish in a fewer subreddits, (iii)
to have a lower number of co-posting authors, (iv) to be more interconnected, active and
27

“teamed” than SFW authors.
2. The maximum number of negative posts published by a single NSFW author is much
higher than the corresponding one of a single SFW author.
3. Differently from what happens to SFW authors, there is no degree assortativity and no
eigenvector assortativity among NSFW authors.
SF (Finding on NSFW subreddits)
1. NSFW subreddits receive much fewer comments than SFW subreddits.
Now, we examine the previous findings in order to identify their correlations. This allows us to
have a general view of the phenomenon of NSFW posts in Reddit.
The finding PF.1 tells us that an NSFW post is published in a limited number of subreddits.
The finding AF.1 states that NSFW authors publish more than SFW ones. Now, since NSFW posts
are fewer than SFW ones, we can conclude that NSFW posts have a much more limited number of
authors. In addition, the combination of PF.1 and AF.1 is also a justification to the claim that NSFW
authors publish in fewer subreddits than SFW authors.
Combining the findings PF.1 and AF.1 we can conclude that the phenomenon of NSFW posts is a
niche one.
The finding PF.1 also tells us that the NSFW posts are little appreciated; actually, this information
was quite expected. The results expressed by the finding PF.1 are reinforced by the finding AF.2, which
tells us that the maximum number of negative posts published by a single NSFW author is greater than
the corresponding number of an SFW author. The finding AF.2 is also, in part, a direct consequence
of the finding AF.1.
The finding SF.1, stating that the NSFW subreddits receive fewer comments than SFW ones,
represents a further confirmation of what the findings AF.1 and PF.1 say about the fact that NSFW
posts are a niche phenomenon.
The poor consideration for NSFW posts, expressed by the finding PF.1, is further confirmed by
the finding PF.2, which tells us that not only NSFW posts, but even comments to these posts, receive
a much lower score than the comments to SFW posts.
The finding AF.1 (which tells us that the number of co-posting NSFW authors is fewer than SFW
authors and that NSFW authors are more interconnected, active and “teamed” than SFW ones)
represents a further confirmation that the NSFW post phenomenon is a niche one, carried out by few
authors. However, it also tells us that these authors are very active and very well interconnected,
ready to play “teamwork”.
The last finding extracted, i.e., the finding AF.3, specifies that there is no degree or eigenvector
assortativity for NSFW authors. In other words, the strong connection existing among NSFW authors
is so widespread and compact that it does not let authors group into “narrow circles”. In fact, the
sense of cooperation between these authors is so high that the most active ones still collaborate with
everyone else and do not limit their interactions to only those with their direct peers, as often happens
in many other contexts.
28

10 Conclusion
In this paper, we have presented an approach to investigate NSFW posts in Reddit. We have seen that
this type of content is frequent in this social medium and, despite this, there are very few studies on
this subject in the past literature. We have tried to fill this gap and we have proposed an approach that
investigates the phenomenon of NSFW posts in Reddit with descriptive, co-posting and assortativity
analyses.
In this way, we have obtained three findings, which, together with the principles underlying our
approach, are certainly the two main contributions of this paper. In fact, the findings reported in
this paper provide valuable knowledge to better understand this phenomenon still little investigated.
In addition, our way of proceeding defines a methodology that can be used to uncover the dynamics
underlying NSFW contents in other social media.
In the future, there are several possible developments of our research efforts. First, it is possible to
apply the proposed approach to other social media managing NSFW contents. In addition, we could
extend our study of NSFW posts including an in-depth analysis of their content from a semantic point
of view. Similarly, we could deepen our knowledge on the authors of NSFW posts applying sentiment
analysis techniques to the posts they wrote or commented. Finally, we could consider to define a
Machine Learning based approach to automatically identify and label NSFW posts, authors and
communities, particularly when NSFW posts are not manually labeled by users. This last application
can become extremely important to prevent NSFW contents from being sneakily and deceptively
offered to unsuitable users (e.g., children).
Acknowledgments
This work was partially supported by: (i) the Italian Ministry for Economic Development (MISE)
under the project “Smarter Solutions in the Big Data World”, funded within the call “HORIZON2020”
PON I&C 2014-2020 (CUP B28I17000250008), and (ii) the Department of Information Engineering
at the Polytechnic University of Marche under the project “A network-based approach to uniformly
extract knowledge and support decision making in heterogeneous application contexts” (RSAB 2018).
References
[1] J. Baumgartner, S. Zannettou, B. Keegan, M. Squire, and J. Blackburn. The pushshift Reddit dataset. In Proc.
of the International AAAI Conference on Web and Social Media (ICWSM’20), volume 14, pages 830–839, Atlanta,
GA, USA, 2020. AAAI Press.
[2] A.Q. Bhatti, M. Umer, S. H. Adil, M. Ebrahim, D. Nawaz, and F. Ahmed. Explicit Content Detection System: An
Approach towards a Safe and Ethical Environment. Applied Computational Intelligence and Soft Computing, page
1463546, 2018. Hindawi.
[3] C. Buntain and J. Golbeck. Identifying Social Roles in Reddit Using Network Structure. In Proc. of the International
Conference on World Wide Web (WWW 2014), page 615–620, Seoul, Korea, 2014. ACM.
[4] M. Carpenter and M. Garner. NSFW: An Empirical Study of Scandalous Trademarks. Cardozo Arts & Ent. LJ,
33:321, 2015. HeinOnline.
[5] N. Cassavia, E. Masciari, C. Pulice, and D. Sacc`a. Discovering User Behavioral Features to Enhance Information
Search on Big Data. ACM Transactions on Interactive Intelligent Systems, 7(2), 2017. ACM.
29

[6] T. Connie, M. Al-Shabi, and M. Goh. Smart content recognition from images using a mixture of convolutional
neural networks. In IT Convergence and Security 2017, pages 11–18. 2018. Springer.
[7] D. Correa, L. A. Silva, M. Mondal, F. Benevenuto, and K. P. Gummadi. The many shades of anonymity: Charac-
terizing anonymous social media content. In Proc. of the International AAAI Conference on Web and Social Media
(ICWSM 2015), pages 71–80, Oxford, UK, 2015. AAAI.
[8] S. Datta and E. Adar. Extracting Inter-Community Conflicts in Reddit. In Proc. of the International Conference
on Web and Social Media (ICWSM 2019), pages 146–157, Munich, Germany, 2019. AAAI.
[9] P.V.A. de Freitas, G.N.P. Santos, A.J.G. Busson, A.L.V. Guedes, and S. Colcher. A baseline for NSFW video
detection in e-learning environments. In Proc. of the Brazillian Symposium on Multimedia and the Web (WebMedia
2019), pages 357–360, Rio de Janeiro, Brazil, 2019. ACM.
[10] M. Fire and C. Guestrin. The rise and fall of network stars: Analyzing 2.5 million graphs to reveal how high-degree
vertices emerge over time. Information Processing & Management, 57(2):102041, 2020. Elsevier.
[11] A. Grewal and J. Lin. The evolution of content analysis for personalized recommendations at Twitter. In Proc. of
the International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR ’18), pages
1355–1356, Ann Arbor, MI, USA, 2018. ACM.
[12] A. Guimaraes, O. Balalau, E. Terolli, and G. Weikum. Analyzing the Traits and Anomalies of Political Discussions
on Reddit. In Proc. of the International Conference on Web and Social Media (ICWSM 2019), pages 205–213,
Munich, Germany, 2019. AAAI.
[13] Q. He, X. Wang, F. Mao, J. Lv, Y. Cai, M. Huang, and Q. Xu. CAOM: A community-based approach to tackle
opinion maximization for social networks. Information Sciences, 513:252–269, 2020. Elsevier.
[14] Y. Kou, C.M. Gray, A.L. Toombs, and R.S. Adams. Understanding Social Roles in an Online Community of
Volatile Practice: A Study of User Experience Practitioners on Reddit. ACM Transactions on Social Computing,
1(4):17:1–17:22, 2018. ACM.
[15] J. LaViolette and B. Hogan. Using Platform Signals for Distinguishing Discourses: The Case of Men’s Rights and
Men’s Liberation on Reddit. In Proc. of the International Conference on Web and Social Media (ICWSM 2019),
pages 323–334, Munich, Germany, 2019. AAAI.
[16] Y. Li, Z. Su, J. Yang, and C. Gao. Exploiting similarities of user friendship networks across social networks for user
identification. Information Sciences, 506:78–98, 2020. Elsevier.
[17] J.N. Matias. Going dark: Social factors in collective action against platform operators in the Reddit blackout. In
Proc. of the International Conference on Human Factors in Computing Systems (ACM CHI 2016), pages 1138–1151,
San Jose, CA, USA, 2016. ACM.
[18] M. McPherson, L. Smith-Lovin, and J.M. Cook. Birds of a feather: Homophily in social networks. Annual Review
of Sociology, 27:415–444, 2001. JSTOR.
[19] A.N. Medvedev, R. Lambiotte, and J.C. Delvenne. The Anatomy of Reddit: An Overview of Academic Research.
In Dynamics On and Of Complex Networks III, pages 183–204, Cham, 2019. Springer International Publishing.
[20] B. K. Narayanan and M. Nirmala. Adult content filtering: Restricting minor audience from accessing inappropriate
Internet content. Education and Information Technologies, 23(6):2719–2735, 2018. Springer.
[21] E. Newell, D. Jurgens, H.M. Saleem, H. Vala, J. Sassine, C. Armstrong, and D. Ruths. User Migration in Online
Social Networks: A Case Study on Reddit During a Period of Community Unrest. In Proc. of the International
Conference on Web and Social Media (ICWSM 2016), pages 279–288, Cologne, Germany, 2016. AAAI.
[22] M.E.J. Newman. Clustering and preferential attachment in growing networks. Physical Review E, 64(2):025102,
2001. APS.
[23] A. Nocera and D. Ursino. PHIS: a system for scouting potential hubs and for favoring their “growth” in a Social
Internetworking Scenario. Knowledge-Based Systems, 36:288–299, 2012. Elsevier.
[24] Q. Shen and R. Carolyn. The Discourse of Online Content Moderation: Investigating Polarized User Responses to
Changes in Reddit’s Quarantine Policy. In Proc. of the International Workshop on Abusive Language Online (ALW
2019), pages 58–69, Florence, Italy, 2019. Association for Computational Linguistics.
30

[25] P. Singer, F. Fl¨ock, C. Meinhart, E. Zeitfogel, and M. Strohmaier. Evolution of Reddit: From the Front Page of the
Internet to a Self-Referential Community? In Proc. of the International Conference on World Wide Web (WWW
2014), page 517–522, Seoul, Korea, 2014. ACM.
[26] A. Soliman, J. Hafer, and F. Lemmerich. A Characterization of Political Communities on Reddit. In Proc. of the
ACM Conference on Hypertext and Social Media (HT’19), page 259–263, Hof, Germany, 2019. ACM.
[27] C. Tan and L. Lee. All Who Wander: On the Prevalence and Characteristics of Multi-Community Engagement. In
Proc. of the International Conference on World Wide Web (WWW 2015), page 1056–1066, Florence, Italy, 2015.
ACM.
[28] K. Tiidenberg. Boundaries and conflict in a NSFW community on tumblr: The meanings and uses of selfies. New
Media & Society, 18(8):1563–1578, 2016. Sage Publications.
[29] T. Weninger. An exploration of submissions and discussions in social news: mining collective intelligence of Reddit.
Social Network Analysis and Mining, 4:173–192, 2014. Springer.
[30] F. Wilcoxon. Individual Comparisons by Ranking Methods. In Breakthroughs in statistics, pages 196–202. 1992.
Springer.
[31] Y. Wu, H. Huang, N. Wu, Y. Wang, M.Z.A. Bhuiyan, and T. Wang. An incentive-based protection and recovery
strategy for secure big data in social networks. Information Sciences, 508:79–91, 2020. Elsevier.
[32] D. Zhelonkin and N. Karpov. Training Effective Model for Real-Time Detection of NSFW Photos and Drawings.
In Proc. of the International Conference on Analysis of Images, Social Networks and Texts (AIST 2019), pages
301–312, Kazan, Russia, 2019. Springer.
[33] B. Zheng, O. Liu, J. Li, Y. Lin, C. Chang, B. Li, T. Chen, and H. Peng. Towards a distributed local-search approach
for partitioning large-scale social networks. Information Sciences, 508:200–213, 2020. Elsevier.
31