# An End-to-End Dialogue Summarization System for Sales Calls

Abedelkadir Asi1, Song Wang2, Roy Eisenstadt1,Dean Geckt1,
Yarin Kuper1, Yi Mao2, Royi Ronen1
1Microsoft Dynamics,
2Microsoft Azure
{abeasi,sonwang,reisenstadt,t-deangeckt,
<EMAIL_ADDRESS>
###### Abstract

Summarizing sales calls is a routine task performed manually by salespeople.
We present a production system which combines generative models fine-tuned for
customer-agent setting, with a human-in-the-loop user experience for an
interactive summary curation process. We address challenging aspects of
dialogue summarization task in a real-world setting including long input
dialogues, content validation, lack of labeled data and quality evaluation. We
show how GPT-3 can be leveraged as an offline data labeler to handle training
data scarcity and accommodate privacy constraints in an industrial setting.
Experiments show significant improvements by our models in tackling the
summarization and content validation tasks on public datasets.

An End-to-End Dialogue Summarization System for Sales Calls

Abedelkadir Asi1, Song Wang2, Roy Eisenstadt1,Dean Geckt1, Yarin Kuper1, Yi
Mao2, Royi Ronen1 1Microsoft Dynamics, 2Microsoft Azure
{abeasi,sonwang,reisenstadt,t-deangeckt<EMAIL_ADDRESS>

##  1 Introduction

An integral part of salespeople daily routine is summarizing sale calls. The
summarization process aims to distill salient information from sales dialogues
into succinct highlights, which are then leveraged by salespeople for
productivity and coaching purposes. Manually curating a call summary is
considered as one of the biggest time wasters for B2B sellers Zhang et al.
(2020). It distracts salespeople from nurturing the relationship with their
next customer. Recently, this practice has become more demanding due to the
emerging landscape of remote selling where virtual meetings become the new
norm Gavin et al. (2020).

Dialogue summarization induces a variety of unique challenges compared to
summarization of documents such as news or scientific papers (Zhu and Penn,
2006). Information density is a key challenge in dialogue text; information is
scattered over multiple utterances and participants, leading to frequent
coreferences and topic alternations. Spoken dialogues, usually transcribed by
speech recognition engines, impose additional challenges such as redundancies
and misrecognized words. The length of these dialogues, e.g. $50K$ tokens in a
$45$ minutes call, imposes another challenge to state-of-the-art summarization
models as it exceeds their input limits Zhang et al. (2021). Figure 1
illustrates parts of the challenges imposed by automatically transcribed sales
dialogues.

Figure 1: A customer-agent call transcript with corresponding summary
highlights. Challenges imposed by automatic speech recognition engine can be
observed.

Developing a production system which is both fully automatic and agnostic to
the input text genre is an extremely difficult task given the current state-
of-the-art technology. To this end, we present a pragmatic solution that
enables users to interactively edit machine-generated summary for customer-
agent sales calls as appears in Figure 2. Our solution summarizes the call
into a collection of abstractive highlights to accurately capture the various
details of the call. The machine-human interaction is enabled through a
designated human-in-the-loop user experience (Ostheimer et al., 2021). It
enables users to modify the generated summaries, yet, the intervention is
designed to be minimal so that the overall time consumption of users is
significantly reduced.

Figure 2: Illustrating human-in-the-loop experience which enables users to
interactively handle summarization challenges by adding relevant summary
highlights to the editing canvas and modify them, if necessary.

Overall, our contributions are listed as follows:

  1. 1.

Dialogue summarization system. We introduce an innovative production
summarization system for summarizing call transcripts with a human-in-the-loop
setting. Our system uses an advanced summarization model to generate
abstractive summaries for dialogues. Additionally, it employs a novel model
for quantifying the coherence of the summaries to compensate for the
summarization model limitations.

  2. 2.

GPT-3 as an offline label generator. We present a technique for leveraging
GPT-3 model to generate pseudo labels without the need to deploy and maintain
it in production. This enables us to (i) efficiently generate labels in low-
resource setting, (ii) distill GPT-3 knowledge into lighter models, and (iii)
accommodate data privacy constraints.

  3. 3.

Custom evaluation metric. We examine the importance of leveraging a
comprehensive evaluation metric which takes into account various quality
aspects of the generated summary. The metric is utilized to focus our efforts
on potential candidate models during the development phase. The suggested
metric goes beyond lexical overlap and help us validate that our production
model is optimized for generating summaries which are fluent, relevant and
factually reliable.

##  2 Related Work

#### Document Summarization

Summarization methods can be categorized into two classes: extractive and
abstractive. Early works focused on extractive methods (Hovy and Lin, 1997;
Marcu, 1997), followed by rule-based approaches for abstractive summarization
(Barzilay and Elhadad, 1997; Barzilay et al., 1999). Advancements in
capabilities of deep neural models led to works such as Rush et al. (2015)
where a seq-2-seq attention-based model is used for abstractive summarization.
See et al. (2017) overcomes some of the former work’s limitations by
introducing a pointer generator network that has the ability to copy words
from the source document. A major advancement in the field of deep neural
models was the introduction of Transformer architecture (Vaswani et al.,
2017), which is the basis for current state-of-the-art summarization
approaches. Recently, several powerful Transformer-based models have been
developed and showed remarkable results on various benchmark summarization
tasks (Lewis et al., 2020; Zhang et al., 2019a; Raffel et al., 2020) .

#### Dialogue Summarization

The task of dialogue summarization has been witnessing many commonalities as
document summarization as well as new techniques for handling unique
structures of various dialogue types. Early works in the domain suggested
tackling the problem using extractive methods (Murray et al., 2005; Riedhammer
et al., 2008). Shang et al. (2018) used a pure unsupervised graph-based method
for keyword extraction and sentence compression. Goo and Chen (2018) proposed
to explicitly model relationships between dialogue acts using attention-based
sentence-gated mechanism. Chen and Yang (2020) extracted Transformer-based
representations for different views of dialogues, conditioned on view
representations, to generate summaries using a second Transformer. Zhu et al.
(2020) presented a hierarchical Transformer architecture to encompass the
structure of dialogues.

##  3 Method

While most existing methods summarize a call transcript as a single paragraph,
our system provides a collection of sentences that summarize the entire
dialogue in a chronological order. Given a call transcript, the system
utilizes word embeddings to break the transcript into semantically coherent
segments Alemi and Ginsparg (2015). Each segment is summarized independently
capturing key information such as: customer’s issue, agent’s solution or the
underlying topic of the discussion. Finally, the grammatical coherence of
highlights is analyzed using a dedicated model before suggesting them to the
user. Figure 3 provides a high-level overview of the system’s flow.

Figure 3: A high-level overview of the system’s flow

Next, we introduce the key components of our dialogue summarization system in
details.

###  3.1 DialogBART: Dialogue Summarization Model

Unlike general documents, conversation transcripts have unique structures
associated with speakers and turns. In sales calls, participants can either be
a customer or an agent and these roles impose a unique language style that can
be leveraged by the model. Motivated by this observation, we propose an
encoder-decoder model called DialogBART, which adapts the well-known BART
Lewis et al. (2020) model with additional embedding parameters to model both
turns and speakers positions (Zhang et al., 2019c; Bao et al., 2020). For
speaker embeddings, we introduce designated vectors to represent each speaker
which can be easily generalized to multi-participant dialogues. Additionally,
we leverage another set of vectors to model turn position embeddings. During
inference, the model determines the speaker and turn indices by leveraging a
special token that separates the dialogue’s turns.

As shown in Figure 4, DialogBART’s input is calculated as the sum of the
corresponding token, position, speaker and turn position embeddings. These
parameters are randomly initialized, however, the remaining parameters are
initialized with weights from a
pretrained111https://huggingface.co/sshleifer/distilbart-xsum-12-3 BART-like
encoder-decoder models (Lewis et al., 2020; Shleifer and Rush, 2020). All
these weights are further fine-tuned on dialogue summarization tasks.

Figure 4: Input representation of DialogBART’s encoder.

###  3.2 Acceptability Validation

Despite the human-in-the-loop user experience, customers still expect high
quality summaries which require minimal modifications by them. We propose a
novel model that determines the quality of each summary highlight in terms of
coherence, fluency and its acceptability in general.

Grammatical acceptability, a property of natural language text, implies
whether a text is accepted or not as part of the language by a native speaker.

The notion was widely investigated through vast work done in automatic
detection of grammatical errors (Atwell, 1987; Chodorow and Leacock, 2000;
Bigert and Knutsson, 2002; Wagner et al., 2007) and on acceptability judgment
of neural networks (Lau et al., 2017; Warstadt et al., 2019). And yet, we are
not aware of works that observe the acceptability of neural generated
summaries for validation purposes. To determine a highlight’s acceptability,
we compute the perplexity of each highlight given by a Pretrained Language
Model (PLM). This PLM is fine-tuned on summaries from DialogSum dataset (Chen
et al., 2021a) and in-domain proprietary data in a traditional self-supervised
manner. Recall that the perplexity of a sequence $W=w_{0}w_{1}...w_{n}$ is
defined as:

$\displaystyle
PP(W;\theta)=\sqrt[n]{\prod_{k=1}^{n}\frac{1}{p_{\theta}(w_{k}|w_{0}w_{1}...w_{k-1})}}$
(1)

where $\theta$ are the language model specific parameters and $p_{\theta}$ is
the probability function corresponding to distribution over vocabulary tokens
induced by the same model.

Based on the perplexity score, the system determines whether a given highlight
should be filtered out, presented to the user, or presented with an indication
that its revision may be required. Figure 2 illustrates how the system helps
users focus their efforts on modifying borderline acceptable highlights based
on the perplexity score.

##  4 GPT-3 as an Offline Labeler

Training a dialogue summarization model requires a large amount of labeled
examples. Manually annotating data for abstractive summarization is a time-
consuming and labor-intensive process, let alone the data privacy constraints.
In this work, we provide a method to automatically pseudo label examples to
overcome these challenges. We leverage GPT-3 model Brown et al. (2020) to
generate pseudo labels in a zero-shot setting for each call segment. GPT-3 is
a large auto-regressive language model with 175 billion parameters that
achieves promising results on various NLP tasks, including question answering.
We treat the problem of label generation as a question answering task. For
each segment, we concatenate a question-based prompt with the segment’s
content while expecting the GPT-3 model to provide the answer as appears in
Figure 5. These answers are used as pseudo labels for the corresponding
segments. This formulation provides the flexibility of defining multiple
questions per segment to summarize the segment from different perspectives.
Finally, these pseudo labels, combined with proprietary human labeled data,
are used to fine-tune the DialogBART model on conversational text.

Figure 5: Utilizing GPT-3 model to generate task-oriented summaries in an
offline manner.

##  5 Custom Evaluation Metric

Common evaluation metrics for text summarization task, i.e. ROUGE and METEOR,
have salient limitations as both metrics track lexical overlap between the
summary and the original text. This kind of assessment falls short when the
summary content perfectly aligns with a reference text but does not
necessarily contain any lexical overlap, e.g. abstractive summaries.

In an industrial setting, one needs to consider various quality perspectives
to guarantee that the summary’s quality does not introduce productivity
blockers for users or negatively affect business decisions. We introduce a
custom evaluation metric, SumSim, that relies on lexical overlap as well as
other quality aspects to ensure that summaries are fluent, coherent and
factually reliable. SumSim aims to cover the following quality perspectives:

  * •

Coverage \- how many units from the reference text are covered by the summary
($S_{r}$)

  * •

Relevance \- measures semantic consistency between the summary and the
reference text ($S_{b}$)

  * •

Informativeness \- how well it captures pre-defined keywords which are
critical to the business ($S_{i}$)

  * •

Factuality \- how factual the summary is with respect to the original text
($S_{f}$)

Our metric uses ROUGE-L Lin (2004), BertScore Zhang et al. (2019b), exact
match of keywords and FactCC Kryscinski et al. (2019) to capture the above
quality aspects, respectively. The quality of a given summary is calculated as
follows:

$S_{0}=\alpha\cdot S_{i}+\frac{1-\alpha}{2}\cdot(S_{r}+S_{b})$ (2)

$SumSim=\beta\cdot S_{f}+(1-\beta)\cdot S_{0}$ (3)

where $\alpha$ and $\beta$ are determined empirically based on the business
scenario sensitivity.

##  6 Experimental Results

In this section we evaluate the performance of our proposed models on various
datasets: DialogSum Chen et al. (2021b), SAMSum Gliwa et al. (2019), CoLA
Warstadt et al. (2019) and a proprietary data from the sales domain. We also
show the potential of $SumSim$ metric compared to traditional evaluation
metrics on the text summarization task. We use Huggingface Transformers (Wolf
et al., 2020) as a training framework in all of our experiments.

###  6.1 DialogBART

In the following experiments we show the performance of DialogBART model in
summarizing dialogues by examining two factors: (i) speaker/turn position
embedding parameters, and (ii) data augmentation by GPT3-labeled data. For
comparison purposes, we leverage two baseline models, BART-large and
distilBART, which achieved state-of-the-art results on the summarization task
Lewis et al. (2020); Shleifer and Rush (2020). All models, including the
baseline models, were initially fine-tuned on XSum dataset Narayan et al.
(2018).

First, we examine the contribution of DialogBART’s position embeddings on
DialogSum and SAMSum datasets. All models were fine-tuned using the relevant
training sets and evaluated on the test sets of the corresponding datasets.
Table 1 shows that the suggested speakers/turns positions embeddings provide
better results when compared to the baseline models.

Model | R1 | R2 | RL
---|---|---|---
| DialogSum
distilBART | 35.93 | 11.71 | 28.86
\+ embeddings | 46.97 | 21.34 | 39.45
BART-large | 46.48 | 20.89 | 38.12
\+ embeddings | 46.68 | 21.46 | 38.32
| SAMSum
distilBART | 41.93 | 19.17 | 34.05
\+ embeddings | 50.21 | 25.89 | 41.99
BART-large | 52.45 | 28.08 | 43.84
\+ embeddings | 52.91 | 28.39 | 43.90
Table 1: Effectiveness of DialogBART’s speaker and turn embedding parameters
using ROUGE metrics.

Second, we examine the implications of fine-tuning DialogBART model using
different data types: human-labeled ($20K$ samples) and GPT3-labeled ($21K$
samples) data 222The anonymized data was collected and used based on a data
sharing agreement with customers from different business domains. The human-
labeled data is composed of anonymized agent’s notes which were captured as
part of the daily routine of the agents and not in a crowdsourcing setting..

We evaluated the models on the test subset of: (i) DialogSum (500 samples),
(ii) SAMSum (819 samples), and (iii) proprietary data (100 samples). The
evaluation on the public datasets was conducted without fine-tuning the models
on the corresponding training sets. Table 2 shows that DialogBART model
outperforms the baseline models on public datasets even in out-of-domain
setting. Additionally, results show that DialogBART fine-tuned on a mixture of
human and pseudo labels outperforms its counterparts which were fine-tuned on
either fully human labels or fully pseudo labels. We note that fine-tuning
DialogBART on pseudo labels yielded higher ROUGE scores compared to human
labels. This could be explained by human tendency to generate variable
summaries which induces disagreements between human annotators (Clark et al.,
2021). While a model fine-tuned on pseudo labels is less variable in its
generations, a model fine-tuned using human data produces text that is, in
turn, more variable and leads to less lexical overlap with test references as
measured by ROUGE metrics333https://github.com/google-research/google-
research/tree/master/rouge .

Model | DialogSum | SAMSum | Proprietary
---|---|---|---
| R1 | R2 | RL | R1 | R2 | RL | R1 | R2 | RL
distilBART | 17.1 | 3.6 | 13.5 | 20.3 | 4.1 | 15.5 | 16.3 | 1.1 | 13.1
BART-large | 17.7 | 3.9 | 13.7 | 24.9 | 5.5 | 18.9 | 16.9 | 1.5 | 13.3
DialogBART |  |  |  |  |  |  |  |  |
\+ human | 21.7 | 4.5 | 19.1 | 18.9 | 3.0 | 16.8 | 20.2 | 5.3 | 19.2
\+ pseudo | 28.0 | 5.9 | 22.2 | 26.0 | 5.1 | 20.6 | 28.5 | 10.5 | 24.8
\+ human & pseudo | 33.5 | 9.0 | 24.5 | 30.4 | 7.5 | 22.4 | 33.1 | 13.0 | 26.3
Table 2: Results of fine-tuning DialogBART model on human labels, pseudo
labels and mixture of them.

###  6.2 Acceptability Validation

We examine mutiple candidate PLMs with language model objective for this task.
Initially we fine-tune the candidate PLM on summaries from DialogSum dataset
and later on positive examples from the train subset of our internal
acceptability benchmark consisting of in-domain summaries ($100$ samples). As
candidate PLMs, we experiment with GPT-2 (Radford et al., 2019), DistilGPT-2
Sanh et al. (2019) and a RoBERTa encoder (Liu et al., 2019) with a language
model head, RoBERTa-LM. Table 3 shows comparison results between the examined
models and leaderboard competitors on the development set of CoLA as well as
on the test subset of an internal benchmark.

Model | CoLAdev | Internal
---|---|---
TinyBERT (Jiao et al., 2020) | 54 | -
Synthesizer (Tay et al., 2021) | 53.3 | -
DeBERTa (He et al., 2021) | 69.5 | 54.5
DistilGPT-2 | 61.7 | 63.6
GPT-2 | 62.5 | 60.6
RoBERTa-LM | 64.2 | 66.7

Table 3: Accuracy of acceptability validation models.

We observe that all of the models trained using our method, in the bottom half
of the table, which were not trained on CoLA data yield competitive results
compared to models explicitly fine-tuned for the task, top half of Table 3. We
also found that the RoBERTa-LM model achieves highest results on the internal
set. Additionally, we fine-tuned DeBERTa, the strongest CoLA competitor, in a
classification setting on the internal benchmark. We observe that results
achieved by our models are significantly better. We hypothesize, this
phenomenon is due to the fact that valid in-domain highlights, as generated by
DialogBART, share a unique structure and can be viewed as forming a specific
language which properties are better captured by a language model rather than
a classifier.

###  6.3 Custom Evaluation Metric

We leverage the DialougSum test set to show the potential of the $SumSim$
metric. Table 2 shows that DialogBART fine-tuned on pseudo labels,
$M_{pseudo}$, outperforms its counterpart that was fine-tuned on human labels,
$M_{human}$. However, Figure 6 shows contradicting insights when comparing the
performance of these two models by different quality aspects, i.e., lexical
overlap (ROUGE) and factual reliability (factCC).

This observation emphasizes the need for non-standard quality measures for
evaluating the performance of abstractive summarization models. This need is
critical for customer-facing enterprise products where business decisions can
be affected by the generated summary. Figure 6 shows the strengths and
weaknesses of different quality metrics in evaluating three DialogBART
variants. The $M_{pseudo}$ model outperforms the $M_{human}$ in all quality
dimensions except of factuality. This observation is consistent with recent
studies which report that large size language models are less truthful than
their smaller peers Lin et al. (2021).

Figure 6: The capacity and limitation of various quality metrics. The bars’
colors represent three different models which were fine-tuned on human labels,
pseudo labels and a mixture of them, respectively.

##  7 Conclusions

In this paper, we present an end-to-end system for abstractive summarization
of agent-customer calls. We employ a two-stage strategy to summarize long call
transcripts by (i) splitting the dialogue into semantically coherent segments,
and (ii) generating summaries using our DialogBART summarization model. We
demonstrate how a pragmatic solution that combines a content selection model
with a human-in-the-loop user experience can help compensate on generative
models’ limitations. We show how GPT-3 model can be leveraged as an offline
data labeler to train lighter models and accommodate data privacy constraints.
Experiments show that the introduced embedding parameters combined with fine-
tuning on in-domain data significantly improve the quality of the generated
summaries with respect to publicly available BART-based summarization models.
We emphasize the need for non-standard evaluation metrics and show how common
metrics fall short when evaluation of abstractive summaries is considered.

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# Characterizing Online Vandalism: A Rational Choice Perspective

Kaylea Champion  University of WashingtonSeattleWA98195USA<EMAIL_ADDRESS>
(2020)

###### Abstract.

What factors influence the decision to vandalize? Although the harm is clear,
the benefit to the vandal is less clear. In many cases, the thing being
damaged may itself be something the vandal uses or enjoys. Vandalism holds
communicative value: perhaps to the vandal themselves, to some audience at
whom the vandalism is aimed, and to the general public. Viewing vandals as
rational community participants despite their antinormative behavior offers
the possibility of engaging with or countering their choices in novel ways.
Rational choice theory (RCT) as applied in value expectancy theory (VET)
offers a strategy for characterizing behaviors in a framework of rational
choices, and begins with the supposition that subject to some weighting of
personal preferences and constraints, individuals maximize their own utility
by committing acts of vandalism. This study applies the framework of RCT and
VET to gain insight into vandals’ preferences and constraints. Using a mixed-
methods analysis of Wikipedia, I combine social computing and criminological
perspectives on vandalism to propose an ontology of vandalism for online
content communities. I use this ontology to categorize 141 instances of
vandalism and find that the character of vandalistic acts varies by vandals’
relative identifiability, policy history with Wikipedia, and the effort
required to vandalize.

rational choice, RCT, vandalism, wikipedia, anonymity, graffiti, online
community

††journalyear: 2020††copyright: rightsretained††conference: International
Conference on Social Media and Society; July 22–24, 2020; Toronto, ON,
Canada††booktitle: International Conference on Social Media and
Society(SMSociety ’20), July 22–24, 2020, Toronto, ON, Canada††doi:
10.1145/3400806.3400813††isbn: 978-1-4503-7688-4/20/07††ccs: Security and
privacy Social aspects of security and privacy††ccs: Human-centered computing
Wikis††ccs: Human-centered computing Empirical studies in collaborative and
social computing††ccs: Human-centered computing Computer supported cooperative
work††ccs: Human-centered computing Collaborative content creation††ccs:
Human-centered computing Social media

##  1\. Introduction

Vandalism offers asymmetric power to the vandal. The object being damaged may
originate in the effort of a powerful entity such as a large institution or a
government or a large group of well-meaning people. A vandal’s momentary
action with a hammer or pen can be profoundly disruptive. In physical
environments, communities may expend substantial resources to restore
vandalized property: public works must be rebuilt, damaged artwork may be
irretrievably lost, and time and money spent removing defacement could be
deployed in other ways.

Online communities also struggle with vandalism. Automation in digitally-
mediated environments can aid vandals who use tools to vandalize with
incredible speed and volume (Geiger and Halfaker, 2013). Although automated
tools support rapid restoration of the digital work to its original
state—often within minutes (Alkharashi and Jose, 2018), automated vandal
fighting is not without costs. It requires development, invocation, and
maintenance of tools and it leads to impersonal messages and improper
rejection of contributions which has been implicated in a decline of well-
intentioned newcomers (Halfaker et al., 2013), a trend which requires
substantial effort to remediate (Halfaker et al., 2011; Morgan and Halfaker,
2018). The digital nature of online vandalism does not mean it is
victimless—besides offending and burdening hard-working creators, maintainers,
and moderators (Geiger and Ribes, 2010), the general public suffers as well.
On highly visited websites, some information-seekers are likely to read the
damaged page regardless of how quickly vandalism is removed.

Given the social and individual cost and the difficult to discern source of
benefit to the vandal, we may ask why it occurs at all. Competing theories in
criminology seek to explain the motivations for and causes of crime, ascribing
criminal behavior to such factors as lack of impulse control, lack of morals,
or to societal failure. Alternatively, rational choice theory proposes that
behaviors are the product of rational choices. In order to apply rational
choice theory to vandalism, this project seeks to understand vandal decision-
making in terms of preferences and constraints. Building on past examinations
of Wikipedia contributors, we examine vandalism from four groups: users of a
privacy tool, those contributing without an account, those contributing with
an account for the first time, and those contributing with an account but
having some prior edit history (Tran et al., 2020; Champion et al., 2019).

This paper makes three contributions: I describe the extension of rational
choice theory and value expectancy theory to Wikipedia, I offer an extension
to the existing ontologies for characterizing online vandalism, and I
demonstrate the application of both rational choice theory and this vandalism
ontology in a preliminary series of findings. The paper is structured as
follows. I offer background on the topic of vandalism and theories of rational
choice in §2. I describe the comparison groups in §3 and my approach to
analysis in §4. I share the results of classifying vandalism and examining the
relative prevalence of each type in §5 with limitations as described in §6. In
§7 I explore the implications of these findings, as well as how these results
can inform design and policy in online communities. I conclude in §8.

##  2\. Background

Wikipedia calls itself “the free encyclopedia that anyone can edit” and seeks
to present encyclopedic information from a neutral point of view, with freely
usable, editable, and distributable content, developed by a respectful
community with no firm
rules.111https://en.wikipedia.org/wiki/Wikipedia:Five_pillars Like a public
park, Wikipedia is a public good—defined as non-rivalrous in that one person’s
use does not consume it and non-excludable in that it is open to everyone.
Technological advances have allowed for the collaborative development of new
public goods through a process called commons-based peer production in which
self-directed volunteers contribute according to their interests, whether
small and casual or vast and serious (Benkler, 2006). However, in opening
editing power to almost anyone, Wikipedia opens itself up to the possibility
that not all participants engage in good faith to develop a high-quality,
neutral encyclopedia. Instead, some contributors seek to do harm.

As one of the top 10 most visited sites on the internet, damage to Wikipedia
holds the potential to harm information seekers in multiple ways. Vandalism
that deletes or disrupts a page will block access to information. Insertion of
deliberate misinformation can generate rumors or distort understanding of a
topic. Vandalism can expose the general public to threats or harassment.

Other vandalism in Wikipedia is targeted to damage the production community
itself, such as threatening and harassing contributors and administrators,
wasting volunteer time by spamming requests for help when no help is needed,
or by disrupting the behaviors of automated tools.

Some Wikipedia vandals write their names, add jokes, or make rude comments.
These kinds of contributions are also characterized as vandalism in
Wikipedia,222https://en.wikipedia.org/wiki/Wikipedia:Vandalism although the
content of the text is in some cases indistinguishable from what in the
physical world would be called graffiti. Although graffiti has long been
analyzed as a source of meaning in contexts ranging from ancient Pompeii to
modern university library bathrooms (Dombrowski, 2011), the addition of
unwelcome or disruptive contributions to Wikipedia is contrary to its use,
making vandalism an apt description regardless of text. The Wikipedia
community has a tradition of joking about persistent vandalism.333For example,
this humorous essay describes categories of vandals as ‘typing students’, ‘the
curious’, ‘critics’, ‘men with big penises’, ‘cheerleaders’, and ‘friends of
gays’, the last being a group of people who the essay calls “overly proud
friends” of gay and lesbian people, i.e. those whose vandalism consists of
texts like “[name] is gay.” The essay is available at:
https://meta.wikimedia.org/wiki/Friends_of_gays_should_not_be_allowed_to_edit_articles

Just as volunteers built the public good that is Wikipedia, they work to
defend its utility from damage. However, countering vandalism in Wikipedia is
a substantial undertaking. Vandal-fighters and their tools are recognized as
an important element of the community (Halfaker et al., 2013; Geiger and
Ribes, 2010). Vandalism-related research has tended to focus on the detection
and removal of vandalism (Tramullas et al., 2016), with relatively little
attention paid to understanding vandals themselves. Notable exceptions include
Shachaf and Hara (2010) who considered the cases of four people who Wikipedia
community administrators (“sysops”) had identified as “trolls” who had engaged
in acts of vandalism, including replacing main page photos with pornography
and writing threats. In their interviews with sysops, Shachaf and Hara found
that the sysops interpreted trolls as having engaged in harmful edits
intentionally, repetitively, and in violation of policy, and that they tended
to target the community itself for damage. The sysops further interpreted the
trolls as being motivated by “boredom, attention seeking, revenge….fun and
entertainment….[and desire to do] damage to the community” (Shachaf and Hara,
2010, p. 357).

Sierra and Castanedo (2018) asked focus groups of students and Wikipedia
editors to speculate as to why people vandalize. Students thought vandals
might be making a joke, acting out of boredom, or engaging in ideological
protest. Wikipedia editors added to these potential motives the possibility of
experiencing a sense of pride at defeating Wikipedia’s defenses against these
vandalism. These interpretations of vandal behavior suggest that increasing
the time required to vandalize may reduce more casual and sociable forms of
vandalism, but that some kinds of vandals may find obstacles part of the
appeal. Cruz et al. (2018) was able to interview individuals identified as
trolling in an online community, as well as witnesses to trolling, and found
that effective trolling requires knowledge of a community’s rules and norms.
This suggests that to the extent that some vandalism qualifies as trolling, it
may emanate from community members who know the norms well enough to
intentionally transgress them. Such individuals may have more to lose if their
behaviors lead to their accounts being blocked and may seek out ways to avoid
identification.

There are many types of vandalism. Some have only a mildly negative impact,
and others might even carry a positive impact alongside their harm. Acts of
vandalism may be amusing, insightful, or comprise a political protest. Some
scholars have found evidence that unwanted behavior on Wikipedia serves as a
signal of attention from the general public, which in turn may inspire further
efforts or improvements from the community. (Gorbatâi, 2014) That said, just
as janitorial staff scrub away even the most supportive and friendly bathroom
scribble, even the most innocuous forms of vandalism are removed from
Wikipedia to protect the integrity of the resource.

###  2.1. Theoretical Framing

One may consider vandalism on Wikipedia as an example of criminality and
antinormative behavior in a broad sense and seek insight from social theories
used to understand deviance. One important perspective is rational choice
theory (RCT), which has found applications in economics, biology, psychology,
and sociology. Although multiple articulations exist, the version of RCT I use
states that people select actions which they believe will maximize their
utility with consideration of their preferences and any constraints associated
with that action (Opp, 1999). I use a ‘wide’ version of RCT which Goldthorpe
(1998) recommends as most suitable for understanding social action and
relations. The wide version incorporates an understanding that people make
choices with incomplete information, subject to bounded rationality, with
concern for benefits beyond material wealth (such as social approval and a
positive emotional state), and nuanced models of personal preferences, traits,
and fears (Mehlkop and Graeff, 2010; Opp, 1999).

To assist in the application of RCT to analysis of specific acts, I use Value
Expectancy Theory (VET), which is derived from RCT. Riker and Ordeshook (1973)
articulate value expectancy theory as expected utility $E$, of some behavioral
alternative $a$ (identified specifically as alternative $i$ among some list of
alternatives) calculated as the sum of the product of two considerations: the
probability of some outcome $O$ (identified specifically as outcome $j$ among
some list of outcomes 1 through $N$) and the utility valuation $U$ of that
same outcome, $O_{j}$. Expected utility is thus
$E(a_{i})=\sum_{j=1}^{N}p_{ij}(O_{j})U(O_{j})$. With this definition in hand,
we can express RCT in terms of a simple relationship among expected utilities.
If the expected utility of an action $i$ exceeds the utility of some other
action $j$, then rational choice predicts the selection of action $i$:
$E(a_{i})>E(a_{j})\Rightarrow a_{i}$.

Utility maximization of this kind need not be assessed numerically for the
proposed relationship to hold. A would-be vandal might select among
alternatives iteratively, examining each possible pair in turn and choosing
the preferred option. After this round-robin process, if one’s thinking
process is consistent, a preference ranking of activities would emerge
(Gilboa, 2010). In order to discern whether online vandals make rational
choices consistent with VET, I examine vandalism and consider the factors
relevant to VET in building up hypotheses: what constraints, what preferences,
and what utility applies?

###  2.2. Constraints

Given the significance of Wikipedia, the freedom with which contributors
improve or damage its content, and the substantial challenge of protecting it,
we might ask what factors act as constraints against vandals. Although editing
is free of charge, anyone may click the edit button, make some changes, and
save them. Although this barrier sounds relatively low, relatively few people
actually contribute to Wikipedia compared to the number who read it. For
example, English Wikipedia, the largest of the 302 different language edition,
with over 6 million articles, received over 11 billion page views in April
2020 from almost 1 billion unique devices, but the total number of unique
editors in that month was only 424,155, of whom only 68,481 made five or more
contributions 444https://stats.wikimedia.org. Hence it may be that even a very
low level of effort is nonetheless meaningful. The Wikipedia sysops
interviewed about their impression of trolls’ motives by Shachaf and Hara
(2010) included “ease of execution” as an important factor in troll behavior,
suggesting that time constraints may be relevant. Creating an account costs
more in time and energy than making the same contribution without an account
because a person must fill out a brief form and choose a unique name and
acceptable password.

Because VET predicts that users who have expended more effort are less likely
to choose deviant behavior, and I hypothesize that (H1) users who have created
accounts will vandalize less frequently.

A second potential constraint against vandalism is fear of detection. Given
that internet activities are routinely monitored by employers, school
officials, parents, internet service providers, and governments, vandals may
be discouraged by a concern that they will be discovered. One component of
being detected is being identified. Friedman and Resnick (2001) describes the
decision between using an anonymous pseudonym and a persistent identifier as
“a strategic variable” in the interaction between participants in a
cooperative online platform because it allows control over the spread of a
user’s reputation (p. 174).

Wikipedia offers two types of identifiability for contributors. First, users
can contribute without making an account in which case their IP address is
publicly associated with their actions. Second, users can create and then log
in to an account to which all subsequent contributions will be
attributed.555https://en.wikipedia.org/wiki/Special:CreateAccount Despite its
arcane appearance as a sequence of numbers and/or letters, a user’s IP address
is identifiable information, and is regarded as such under privacy regulations
such as the GDPR. Although individuals may not be aware of the fact, IP
address can be used to identify a user’s geographic location. With assistance
from a service provider, the IP address can identify the individual home or
even computer. Wikipedia’s interface encourages contributors to create an
account, telling them that doing so will keep their IP address private from
other contributors and the general public.

In addition to choosing between these two levels of identifiability, users can
take other steps to hide their identity, including naming themselves according
to some pseudonym unlinked to their identity, avoiding sharing personal
information online, using multiple, shared, or public IP addresses, or using
an anonymity service to mask their IP address. Detecting whether users
mitigating against detection is difficult. We can address this limitation with
a unique dataset from Tran et al. (2020), who identified contributions made
using the Tor anonymity service.

The Tor anonymity network allows users to access the Internet while
maintaining IP privacy by relying on volunteers to transport network traffic
using a method called “onion routing.” Typical network routing creates a
traceable path of intermediate hops over which each network packet travels
such that each hop knows the identity of every other node in the network.
Onion routing only gives each layer—each step in the network path—the identity
of the next “hop.” Hence, none of the intermediate steps know the identity of
the point of origin, and none of them know the destination; they only know the
next step (Huang and Bashir, 2016).

Although Tor is sometimes characterized as a tool for criminals and is blocked
or limited by some online services (McDonald et al., 2019), privacy-protecting
tools are a crucial line of defense for people living under oppressive regimes
(e.g. Iran, (Nazeri and Anderson, 2013) or Turkey (Akgül and Kırlıdoğ, 2015)),
or whose identities or interests subject them to harassment and attacks, or
who simply seek relief from surveillance and the monetization of their online
behavior (Forte et al., 2017; Kang et al., 2013; Turner et al., 2003).
Although this prior work often tends to explore broadly normative behavior
that is nonetheless potentially costly to the speaker, privacy techniques can
certainly be used to protect high-risk anti-normative behaviors as well.
Because VET suggests that identifiability acts as a constraint on deviant
behavior, I hypothesize that (H2) populations which differ in their
identifiability produce different types of vandalism, such that the least
identifiable individuals are more likely to produce vandalism that has high-
risk repercussions.

Figure 1. Regardless of whether they are logged in or not, a Tor user will see
a message like the one above if they attempt to edit Wikipedia. In this case,
the author is seeking to edit her own personal user page while logged in. The
message states that editing one’s own User Talk page and e-mailing
administrators is still allowed.

###  2.3. Preferences

The second component of a VET model is the role of preferences. In the case of
vandalism, preferences can be observed in the types of vandalism a vandal
chooses. For example, if a vandal were simply desiring to do damage, any kind
of vandalism might do. However, if there is some communicative intent or a
specific target to be harmed, we might expect to see themes in the impact or
victim/s of vandalism.

This study compares vandals to understand whether, pursuant to H2, they differ
in their willingness to undertake higher risk vandalism based on their
expressed privacy preferences. In order to do so, I examine privacy-seekers
using Tor. Although Wikipedia forbids contributing via Tor, this policy was
not always consistently or correctly applied, with the bulk of edits evading
the block occurring between 2008 and 2013. During this time, about 11,000
edits were either lucky enough to fall through the cracks or able to evade the
block (Tran et al., 2020). Some of the individuals using Tor to edit Wikipedia
may be privacy-seekers who simply got lucky and have no idea that Tor is
blocked. Others from the sample are clearly aware of the contentious
relationship between Tor and Wikipedia, and taunt Wikipedia administrators
about their inability to block them (Champion et al., 2019).

As described, the groups under study differ by how they are treated by
community policies. Newcomers are targeted for social interventions to
welcome, train, and retain them. Wikipedia invites IP-based editors to create
accounts as well as welcoming them. However, Tor-based editors generally
experience rejection, despite the fact that their contributions (when they can
make them) are of similar quality to newcomers and IP-based editors (Tran et
al., 2020); Tor-based editors can only contribute through a combination of
luck and determination. Thus it seems reasonable to examine an additional
relationship between specific group traits and vandalism type. VET predicts
that preferences will drive target selections, and this suggests a final
hypothesis: (H3) Members of excluded groups are more likely to strike against
the community targeting them.

###  2.4. Conceptual model

Figure 2. Conceptual model of vandalism. In the ‘maximize utility’ stage,
Value Expectancy Theory argues that individuals weigh out their possible
actions against their possible outcomes, their value, and their likelihood,
then choose the action they find the most optimal.

Figure 2 uses VET to elaborate a conceptual model of vandalism. Read from left
to right, the figure suggests that the decision to vandalize Wikipedia (as
opposed to read it, contribute to it, or do nothing) begins with personal
preferences and motives as well as consideration and experience of
constraints. The model anticipates an individual weighing the combinations of
preferences and constraints with their relative likelihoods and choosing the
action that they find most suits their preferences.

##  3\. Data

The data for this study was assembled from a random sample of article
revisions made by members of four different groups of editors that differ in
their identifiability and in their relative time investment in editing
Wikipedia: (1) users of the Tor privacy service whose identity is fully masked
that I call “Tor-based editors” ($n=500$); (2) users who contribute without
making an account but whose IP addresses are disclosed in place of a username
making it highly likely that their location can be identified (Lieberman and
Lin, 2009) (although they may not know this) that I call “IP-based editors”
($n=200$); (3) users who have made an account and who are making their first
contribution that I call “New Contributors” ($n=200$); and (4) users with
accounts for whom a given edit is not their first that I call “Experienced
Contributors” ($n=200$). The available population of Tor-based edits is
relatively small (approximately 7,700), and the Tor-based sample is a random
sample of this population. For the other three groups, I drew a random sample
to time-match the Tor population on a monthly basis (e.g., if our Tor
population made 30 edits in January 2009, I would randomly draw 30 edits from
registered editors in January 2009, and so on.) I then drew a random sample
from these time-matched samples.

These user categories differ both in their privileges in Wikipedia (Tran et
al., 2020) and in their privacy concerns (Forte et al., 2017).

All IP editors have fewer privileges on the site and are automatically
scrutinized at higher levels. Contributors with accounts receive progressively
less scrutiny and increase in their site privileges as time passes. The
Wikipedia community also elevates some members for additional administrative
powers. These individuals have additional abilities to enforce norms and
sanction violators, including banning them from contributing. Established
contributors are also typically scrutinized less by algorithms(Halfaker et
al., 2016). The four groups are summarized in Table 1.

Table 1.  Constraints and preferences: vandalism has differing costs and risks for different kinds of uses.  Editor Population | Effort Required | Privacy Risk | Privacy Awareness
---|---|---|---
Tor-based editors | low to high: may require several | little to none | likely to be
| minutes to initiate or fail due to blocks |  | privacy-aware
IP-based editors | none: no sign-up required | location made public | may be unaware
New Account | low to moderate: registration process | location admin-visible | may be unaware
Experienced Account | moderate to high: account access | location admin-visible | may be unaware
| may be lost or reputation damaged |  |

##  4\. Methods

This study followed a mixed-methods approach, starting with a two-stage
qualitative content analysis followed by statistical analysis. I made use of
digital trace data made public by the Tor Project and by Wikipedia, benefiting
from a sample prepared in the course of Tran et al.’s (Tran et al., 2020)
comparison of Tor-based contributors to the same contributor groups I examine
in this paper. Because the research involved only public data and did not
involve interaction or intervention, the research was determined to not be
human subjects research by the IRB at the University of Washington. Despite
this, I have omitted names of most editors and articles and, in some cases,
paraphrased quotes in order to make reidentification more difficult.

Identifying vandalism from the sample described above made use of my
experience as both an editor of Wikipedia and a researcher who has observed
the community for several years.

I defined vandalism using a three-part definition, where all three criteria
must be met. First, an edit must be something which does not comply with
community norms or is not encyclopedic, and hence should be removed. In the
language of Wikipedia, these are “damaging” edits. Second, the edit must not
seem to be intended to contribute to the article in a direct and constructive
way. An edit can be damaging but made in good faith if it violates some rule
about formatting but otherwise appears well-intentioned. Third, an edit should
not be part of an “edit war.”

An edit war involves multiple parties changing the content of a particular
article back and forth. Edit wars may be due to competing visions of what is
correct (e.g., moving an article back and forth between English spelling and
British spelling), or due to competing beliefs about what is true (e.g., a
dispute about how best to summarize the plot of a novel or the reasons for a
historical event), or due to competing beliefs about the nature of knowledge
(e.g., describing herbal medicine as pseudoscientific versus a reasonable and
safe alternative). According to Wikipedia policy, disputes over content should
be resolved through discussion and, if needed, arbitration—not by continually
doing and undoing the work of others. Edit wars, while damaging to the article
and contrary to community norms about engaging in good faith, are ultimately
content-level disputes. By contrast, vandalism disrupts content and seeks to
amuse, offend, disrupt, make comments to, or deliberately misinform the
reader. In summary, vandalism in Wikipedia is damaging, bad-faith, and
communicative or meta-communicative. The proposed analysis required that I
identify and characterize acts of vandalism, a process which I will now
describe. The categorization schema is used is drawn from research on graffiti
and vandalism in physical locations as well as specifically within Wikipedia.
After categorizing each act of vandalism by its type, I used statistical tests
to assess if observed differences were statistically significant.

###  4.1. Constructing an Ontology of Vandalism

In order to classify vandalism into types, I extend two ontologies—one from
Chin et al. (2010) which describes vandalism in the context of Wikipedia, and
one from White (2001) which elaborates types of graffiti in the physical
world. My proposed synthesis is summarized with working definitions in Table
2.

One extension in this proposed ontology is the addition of “attack graffiti.”
While attacks on groups is combined with political graffiti by White, my
contention is that while racial slurs obviously have a politics, so might many
statements of opinion or fact. However, racial slurs are qualitatively
different from, say, calling on the ruling party to resign, and collapsing the
two together does not improve clarity. Similarly, White combines attacks on
individuals with social graffiti and toilet humor. I distinguish between
“social graffiti” including goofy nonsense and lighthearted toilet humor and
“attack graffiti” including intimidation, insults, and threats. These
distinctions are not always immediately clear and require some interpretation,
just as some nuance exists between teasing and bullying.

My proposed ontology does not distinguish between what White calls “political
graffiti” and “protest graffiti.” White defines protest graffiti as a subset
of political graffiti targeting the content of “mainstream commercial visual
objects.” In the context of an online encyclopedia, versus a physical
environment full of advertisements and signs, a distinction singling out
commercial targets is less salient. Additionally, White adopts the emic
vocabulary of street graffiti to describe tagger graffiti versus gang
graffiti. White describes tagging as a message of “I’m here” while gang
graffiti asserts power and control. My proposed ontology follows this
underlying distinction in the categorization scheme, but renames “gang
graffiti” to “pro-group graffiti” to avoid confusion. I also omit White’s
category of “Graffiti Art”.

Applying the ontology proposed in this study is necessarily interpretive,
derived from the coder’s personal perspective, background, and knowledge,
including social norms about gender and what qualifies as a joke. The
vandalistic acts which fell within the “attack graffiti” category in this
analysis are varied in their severity, including gross insults, repeated name
substitutions, racial slurs, and rape threats against a specific Wikipedia
administrator. Indeed, all categories contain variation in their severity.

Table 2.  Proposed Ontology of Vandalism Category | Description | Source
---|---|---
Blanking | Large content deletion | Chin et al. (2010)
Large-scale editing | “Massive” insert or change | Chin et al. (2010)
Misinformation | Replace content with false information | Chin et al. (2010)
Image attack | Insert or replace an image with an irrelevant one. | Chin et al. (2010)
Link spam | Adding irrelevant links | Chin et al. (2010)
Irregular Formatting | Insert or remove tags incorrectly | Chin et al. (2010)
Political Graffiti | public policy, law, power structures, social norms | combines two categories from
|  |  White (2001): political graffiti
|  | and protest graffiti
Attack graffiti | attack an individual or group | new
Pro-group graffiti | asserting group power | White (2001)
Tagging | assertion of personal presence, bragging | White (2001)
Community-related Graffiti | opposition to community, norms, or policies | new
Social Graffiti | Gossip, jokes, anat omical comments | called “Toilet and Other Public”
|  | in White (2001)
Figure 3. This screenshot is taken from Wikipedia’s “diff” view. It highlights
the differences between two versions of the article on Social Media. The left
side shows the “before” view; “after” is on the right. The highlighted blue
area with a + sign indicates what was added by the version. In this case, an
IP-based editor adds a type of vandalism this paper would characterize as
social (“whoop whoop big man tings”). Arrows above the text allow navigation
through the revision history of the article.

Wikipedia includes a range of features for reviewing the history of articles
as well as the contributions of individual users. Figure 3 shows the interface
that anyone can use to walk through the revision history of a given article
and replay the timestamped sequence of the actions that built the page. As
seen in Figure 4, anyone can search for and view the list of contributions
made by a given user. These features help readers, contributors, and
researchers understand users’ history in Wikipedia, trace interactions among
contributors, and record the evolution of each page through time.

Figure 4. This screenshot is taken from a Wikipedia user’s contribution page,
in this case the contributions from the co-founder of Wikipedia, Jimmy (Jimbo)
Wales. This information is available for all contributors.

##  5\. Findings

###  5.1. Examples of Vandalism From Wikipedia

Despite the small sample of 141 examples of vandalism found by the manual
content analysis on 1100 revisions described in §4, I identified examples of
all but one of the categories in my proposed ontology of vandalism.

Blanking vandalism removes sections of text and entire articles. Sometimes the
text is replaced, other times it is simply removed. For example, on June 19,
2010, an IP-based editor deleted a section of an article about a historical
figure. The section was written in an encyclopedic tone and had several
references, and the IP editor did not leave any explanation. In another
example, a newly-created account using the name of a rap group deleted all of
the text on the page of another rap group which appeared to be from the same
US city. The lengthy text of the victimized group’s page was replaced with a
brief description and a link to a myspace.com page with the same name as the
vandal.

Large-scale editing involves adding a substantial amount of the text to the
page. For example, a Tor-based editor inserted multiple paragraphs of repeated
text stating over and over that “[Celebrity stalker] died.” in the text of
several television and celebrity articles on December 24, 2008. In other
cases, the text added by a Tor-based editor is varied and seemed to be making
an argument, but not one relevant to the page. One example involved a Tor user
critiquing an administrative decision in Wikipedia, claiming to be an
individual who had been identified and banned for misusing dozens of accounts
(a practice called “sockpuppeting”), and calling other editors “evil” on
multiple occasions from February to April 2008.

Misinformation vandalism refers to attempts to deliberately introduce false
information or to assert information based on rumors. One example of
misinformation vandalism occurred on February 26, 2013 when a Tor-based editor
changed two letters in a word in a long quotation, changing the meaning of the
quote. I looked at multiple primary sources and all of them contradicted this
change. This misinformation persisted until November 5, 2013. Another incident
of misinformation vandalism occurred on February 1, 2013, when an IP-based
editor altered the biography of a broadcaster stating that he had been shot to
death in his home. News reports contemporary to the edit described his death
as occurring in a hospital after an illness. Wikipedia includes an option for
editors to summarize their change as a note for other editors to see. In this
case the vandal described their change as fixing a “minor spelling error.”
However, their edit did not change any spellings.

Image attack vandalism involves substituting an irrelevant or harassing image
for the legitimate image on a page. This type of vandalism is difficult to
assess retrospectively because generally a removed image is no longer
available online. In some cases, the nature of the attack is clearer: for
example, on September 23, 2009, a new editor replaced a female politician’s
photo with an image with the word “horse” in the name, which was reversed 33
minutes later.

Link spam is the insertion of links to external sites in violation of the
Wikipedia external links policy. These links may be commercial in nature or
connected to malware sites. For example, on February 11, 2012, a Tor-based
editor inserted a link to a spam website into an article about a card game.
The links were removed 7 days later as part of a group of edits removing
similar links.

Irregular formatting refers to vandalism which specifically targets the wiki-
specific syntax of a page. This includes special code-like symbols such as
square brackets [], curly braces {}, and so on which control how the page
appears. Altering a single symbol can cause the entire page to display
incorrectly or to be unreadable. I observed this type of vandalism in two
cases: for example, on December 17, 2012, an IP-based editor removed “}}” from
a biography of a prominent female scientist, causing the top portion of the
page to appear jumbled and difficult to read.

Political graffiti is vandalism that invokes or protests public policy, law,
power structures, and social norms. I observed only one example of this type
of vandalism in which an IP-based editor on December 13, 2011 added to an
article which described a particular substance as having been historically
believed to diminish homosexual desire stating that “these…insane theories
still persist to the current day” and that “People need to learn to mind their
own [expletive] business.” This mostly empty category suggests further
refinement to the ontology is needed.

Attack graffiti insults, seeks to intimidate, or seeks to humiliate
individuals or groups. One example of attack graffiti occurred on September
20, 2008 when a newly-created account edited a Wikipedia administrator’s
personal messages page (called their “User Talk” page within Wikipedia) to
include a description of the administrator being raped by an individual who
had recently been banned. I found that threats of this kind were made
repeatedly from both IP-based user identities and newly created accounts. The
administrator used a female-presenting name and the vandal in question used a
male-presenting name. Another example of attack graffiti occurred on December
31, 2007, and altered a page about medieval history with text which altered
the word Wikipedia to contain a reference to pedophilia and then to state that
they should “…should probably start blocking Tor.” This edit was also coded as
community-related graffiti.

Pro-group graffiti, the redefinition of “gang” graffiti for the context of
Wikipedia is graffiti which asserts some group identity or ownership. The
sample as coded did not contain any examples of this type of graffiti,
suggesting that the ontology may need to be refined in this dimension as well.

Tagging in the context of Wikipedia is defined as actions which assert
personal presence or brag in some way about an individual, presumably oneself.
Of course, it cannot be determined if tagging is perpetrated by the person
whose name is used or if it is done by a rival seeking to draw negative
attention to an apparent braggart. One example of tagging graffiti in
Wikipedia occurred on an article about a beer company, in which on February 5,
2008 a newly registered account added the phrase “[name] is a pimp” in capital
letters. Another example of tagging graffiti occurred on December 17, 2007,
when an IP-based editor altered a page about an online first-person-shooter
video game to include five sentences describing a particular player by name as
being a dominant player in the game, ending with the phrase that he “pwns
noobs out da club.”

Community graffiti is vandalism that references the specific community where
it appears. In my case, this type of vandalism referred directly to Wikipedia
or its policies, or made use of sophisticated understanding of Wikipedia
features to target Wikipedia administrators with vandalism. One example
occurred on April 30, 2008 when a Tor-based editor repeatedly updated the user
page associated with their own Tor IP in such a way as to trigger a
malfunction in an automated script (“bot”) used by the Wikipedia community to
monitor and block Tor-based users. The malfunction persisted until November
10, 2008 when a different bot cleaned up the page.

Social graffiti refers to joking comments, anatomical references, and
exchanges of greetings. One example of social graffiti occurred on December 9,
2007, when an IP-based user altered a page about a technology to include the
words “stupid people are so stupid” and some random characters. Another
example occurred on December 3, 2011, when a newly-created account updated an
article about a particular species of plant to claim that it grows in a
fictional videogame universe.

###  5.2. Vandalism Rate

The relative prevalence of vandalism in each group is summarized in Table 3. I
observed vandalism in 13% of the Tor-based edits, 24% of the IP-based edits,
1% of registered user edits, and 15% of the new editor edits. In order to
understand whether the variation in rates by group is statistically
significant, I used a $\chi^{2}$ test of a frequency table containing the
counts of vandalism and non-vandalism for each type. The $\chi^{2}$ reported
$p<0.001$, indicating that the difference among user types is statistically
significant.

As a robustness check, I verified that the results of the $\chi^{2}$ test of
the statistical significance of the differences between vandalism rates still
held without considering registered editors (pursuant to H1); this result was
confirmed with a p-value estimated at $p<.002$. This provides evidence in
support of H1 that populations which have expended more effort will vandalize
less frequently.

Table 3.  The rate of vandalism in the sample. Editor Population | Vandalism Prevalence
---|---
Registered Editors | 1%
First-time Editors | 15%
IP-based Editors | 24%
Tor-based Editors | 13%

The highest-effort group (registered editors) engaged in almost no vandalism.
Tor-based editors arguably must work harder than IP-based editors, and their
rate of vandalism was lower. This observed variation in behavior by user types
is somewhat surprising given prior work which found that levels of non-
damaging editing behavior are roughly equivalent among the first-time, IP-
based, and Tor-based editor populations (Tran et al., 2020).

The full sample of revisions examined through content analysis ($n=1100$) was
used for assessing H1. However, because the rate of observed vandalism was so
low in registered editors (2 instances in the sample of 200 edits), further
analysis was impractical. The registered editor group is omitted from
consideration in H2 and H3, and the only the identified acts of vandalism in
the other three groups ($n=141$) are used for these latter two hypothesis
tests.

###  5.3. Vandalism Types

To test H2, that types of vandalism will vary such that groups with lower
identifiability will be more likely to engage in vandalism, I determined the
relative prevalence of vandalism types.

Table 4 reports both the average incidence rate of types of vandalism and
whether or not the differences in the reported means are statistically
significant as assessed at the $\alpha=.05$ level via a one-way ANOVA. I find
partial support for H2. Groups that vary in their identifiability also vary in
their vandalism type, with low identifiability associated with higher risk
types of vandalism. Tor-based users are substantially more likely than other
groups to engage in large-scale vandalism and least likely to engage in the
lowest risk type of vandalism, that which communicates friendly and sociable
intent. Although other potential high-risk types of vandalism, such as
blanking, misinformation, link spamming, or attacking people, was higher among
Tor-based editors, this difference was not statistically significant.

This analysis also supports H3. Excluded groups are more likely to vandalize
in ways that strike against the community: Tor-based editors target the
community itself at a much higher rate as seen in Table 4.

Table 4. Relative prevalence of observed vandalism types. Percentages do not sum to 100% because multiple codes were applied to individual instances of vandalism (n=141). Bold text indicates that the difference in means among the groups is statistically significant at the .05 level as determined via a one-way ANOVA. Registered editors were eliminated as a comparison group due to their low overall incidence rate of vandalism in the sample. No instances of “pro group” graffiti were identified in the sample, hence the category does not appear here. Group | _Blank_ | Large | _Misinfo._ | _Image_ | _Link_ | _Format_ | _Political_ | _Attack_ | Wikipedia | Social | _Tag_
---|---|---|---|---|---|---|---|---|---|---|---
IP-based | 13% | 4% | 9% | 0% | 0% | 2% | 2% | 26% | 0% | 55% | 9%
Tor-based | 19% | 14% | 13% | 2% | 11% | 2% | 0% | 36% | 17% | 19% | 9%
First-time | 13% | 0% | 7% | 3% | 7% | 0% | 0% | 17% | 3% | 57% | 17%

##  6\. Limitations & Future Work

This study is limited in important ways. First, I rely on a qualitative coding
process conducted by a single investigator. Of course, assessment of vandalism
in Wikipedia is often a series of judgment calls from vandal-fighters working
at high speed on their own. I am familiar with both the empirical context and
several of the results are consistent with independent findings in other work.

Future work should not only use a larger number of observations and a multi-
investigator coding process but also should consider additional potential
constraints, such as the effort required to enact a specific act of vandalism
(e.g., by counting keystrokes).

Additionally, while I have assessed each act of vandalism independently and
from a random sample, some vandalistic acts are committed by serial vandals.
These serial vandals may learn from the community response and adjust their
approach in order to make their actions more effective. For example, Matsueda
et al.’s (Matsueda et al., 2006) study of at-risk and delinquent juveniles
found evidence for the influence of learning. Future research on social
deviance in Wikipedia looking for evidence of learning might yield interesting
results. For example, a vandal seeking to spread misinformation about a
celebrity death might observe that their efforts were immediately reversed and
fine-tune their language to avoid words which trigger automated vandalism
detection systems, or make use of conventions which build the confidence of
other editors, such as citing phony sources.

This work is also limited because the study does not include direct discussion
with vandals. Past studies of crime have benefited tremendously from
interviewing, for example at-risk adolescents as in Matsueda et al. (2006), or
people observed as trolling in an online community as in Cruz et al. (2018).
Although such conversations might be enlightening, the generally anonymous
Wikipedia vandal population might be tremendously difficult to contact since
no contact or communication mechanism is required in order to contribute to
Wikipedia. Finally, this study makes only a preliminary use of RCT and VET and
a future investigation could both ground the phenomena more deeply in these
perspectives as well as develop expanded empirical models.

##  7\. Discussion

Taken together, the results for my three hypotheses add texture to the more
general explanation often offered for antinormative behavior online in terms
of online disinhibition: the notion that being more distant and less
identifiable may reduce one’s reluctance to give in to one’s worst impulses
(Suler, 2004; Suler and Phillips, 1998; Joinson, 1998). Instead, we see that
the least identifiable group (Tor-based editors) does not have the highest
rate of vandalism. The supportive evidence found for H1, H2, and H3 reinforce
the utility of applying Rational Choice and Value Expectancy theories and
suggest that identifiability and time may serve as operative constraints on
vandal behavior.

One way to use these results to counter vandalism is to consider how potential
interventions might change the kinds of vandalism a community receives. For
example, adding a CAPTCHA (von Ahn et al., 2003) to slow down contributions
increases costs for would-be contributors. However, given that the two groups
for whom contributing is most costly (new editors and Tor-based users) seem to
have a different pattern of vandalism types than other groups, increased time
requirements alone may not change the rate or quality of the vandalism.
Friedman and Resnick (2001) proposed an alternative approach to the question
of deterring antinormative behavior without requiring identifiability. They
suggested anonymous certificates, using cryptographic techniques both to
protect identity and to guarantee a single individual will only have one
anonymous certificate. Another intervention might be to increase the
sophistication of tools that detect and filter contributions before they
become visible: for example, disallowing some groups from making large-scale
edits or blanking pages might be desirable in some communities. Tools seeking
to detect vandalism may find that the patterns that can be identified within
each category allow for more accurate targeting algorithms. Although the
differences in the prevalence of several other categories in this sample were
not statistically significant, a larger-scale analysis or additional analytic
variables to represent preferences and constraints may reveal additional
trends.

For privacy advocates seeking policy changes, anti-community vandalism poses a
difficult challenge. Although frustration from banned users is not surprising,
targeting the community is disruptive and the experiences of harassment
victims should not be discounted. Given that many of Wikipedia’s defense
mechanisms against damage make use of IP-based identification to block abusers
as precisely as possible, accommodating those who seek IP privacy poses a
substantial challenge. However, viewing vandals as rational and having diverse
motives opens potential avenues for dialogue.

##  8\. Conclusion

This study characterized vandalism in Wikipedia using Rational Choice Theory
and the specific considerations of Value Expectancy Theory. Based on
consideration of preferences and constraints, this study assessed the types
and prevalence of vandalism and found that both vary among groups of vandals
in ways that are predicted by hypotheses drawn from RCT and VET.

These findings contribute to the emerging understanding of online norm
violations that are low-risk to violators but harmful to victims, communities,
and public goods. Identifiability and effort, together with exclusionary
policies that affect some vandals, offer some explanation for both the rates
of vandalism and types of vandalism perpetrated. Interventions that target
these factors independently may have unintended consequences and could deter
newcomers and valuable casual contributions (Halfaker et al., 2013, 2011).

This study contributes to the study of online vandalism by applying theories
of Rational Choice and Value Expectancy in order to develop a framework for
understanding and modeling vandal behavior. Doing so required an extension and
synthesis of two vandalism ontologies and bringing together perspectives
rooted in online and offline vandalism. This work suggests that treating
vandalism as the product of an antinormative but rational decision-making
process supports further consideration of different strategies for countering
and preventing it.

###  8.1. Acknowledgements

I am tremendously grateful for the guidance of Karl-Dieter Opp; all errors are
mine, but his encouragement and mentorship were invaluable. Three anonymous
reviewers provided helpful suggestions and the resulting manuscript is much
improved by their feedback. This work would not have been possible without the
support of Chau Tran, who kindly shared his dataset of Tor users with me and
assisted in sample preparation. This work was born out of early discussions
with Nora McDonald, Stephanie Bankes, and Joseph Zhang as we puzzled out what
qualified as vandalistic behavior in Wikipedia. This work was supported by the
National Science Foundation (awards CNS-1703736 and CNS-1703049).

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