# The ComMA Dataset V0.2:
Annotating Aggression and Bias in Multilingual Social Media Discourse

###### Abstract

In this paper, we discuss the development of a multilingual dataset annotated
with a hierarchical, fine-grained tagset marking different types of aggression
and the “context” in which they occur. The context, here, is defined by the
conversational thread in which a specific comment occurs and also the “type”
of discursive role that the comment is performing with respect to the previous
comment. The initial dataset, being discussed here (and made available as part
of the ComMA@ICON shared task), consists of a total 15,000 annotated comments
in four languages - Meitei, Bangla, Hindi, and Indian English - collected from
various social media platforms such as YouTube, Facebook, Twitter and
Telegram. As is usual on social media websites, a large number of these
comments are multilingual, mostly code-mixed with English. The paper gives a
detailed description of the tagset being used for annotation and also the
process of developing a multi-label, fine-grained tagset that can be used for
marking comments with aggression and bias of various kinds including gender
bias, religious intolerance (called communal bias in the tagset), class/caste
bias and ethnic/racial bias. We also define and discuss the tags that have
been used for marking different the discursive role being performed through
the comments, such as attack, defend, etc. We also present a statistical
analysis of the dataset as well as results of our baseline experiments with
developing an automatic aggression identification system using the dataset
developed.

Keywords: aggression, bias, Meitei, Bangla, Hindi, Tagset

languageresourceLanguage Resources

The ComMA Dataset V0.2:
Annotating Aggression and Bias in Multilingual Social Media Discourse

|  Ritesh Kumar2,Enakshi Nandi1, Laishram Niranjana Devi1, Shyam Ratan2,
---
Siddharth Singh2, Akash Bhagat3, Yogesh Dawer2
1Panlingua Language Processing LLP, 2Dr Bhimrao Ambedkar University, 3Indian
Institute of Technology-Kharagpur
New Delhi, Agra, Kharagpur
<EMAIL_ADDRESS>

Abstract content

## 1\. Introduction

Aggression, bias, polarisation and hate are now commonplace phenomena on all
kinds of social media platforms. And so are the research efforts to
automatically identify these and process them in some meaningful way so as to
reduce their harmful impact on social communication and society, in general.
Two recent systematic surveys [Vidgen and Derczynski, 2020] and [Poletto et
al., 2021] have shown that over 60 datasets annotated with different aspects
of hateful and abusive speech have been developed in various languages of the
world in just around half a decade. While this rapid research is an
encouraging sign, it has also led to some non-trivial issues. Some of these
issues include (a) a bias towards resource-rich, largely European languages;
(b) incompatible, sparse efforts and datasets (as pointed out by [Vidgen and
Derczynski, 2020] as well) and; (c) relatively less reliability of the
annotation scheme and datasets (partially because of the lack of theoretical
rigour and a somewhat “folk” approach to the problem). While the only way to
promote research on more languages is to build datasets in more languages (we
are making an effort towards that by including Meitei and Bangla in this
dataset and we plan to include more languages as we move ahead), a possibility
to alleviate the other two problems is to use what we call the ’discursive’
methods of annotation for pragmatic phenomena like abusive and hateful
language. Fundamentally the discursive methods argue for (a) building tagsets
in a theoretically robust way, using ideas and concepts that have been
thoroughly investigated in the field of pragmatics (so that we already have a
good understanding of the phenomena that we are trying to annotate); (b)
explicit declaration of the social and communicative background of the
annotators as well as the overall ideological stand taken for the annotation
of the dataset so as to make the rationale behind decision-making somewhat
transparent; (c) documenting all the major decisions and points of discussion
during the annotation process and making them publicly available along with
the dataset such that they serve as guidelines for not just annotating new
datasets but also for understanding the process of arriving at the annotations
done in the dataset. This paper is a demonstration of this discursive method -
both in terms of the annotation process as well as the documentation and
reporting of this process.

In this paper, we discuss the development of dataset annotated with different
kinds of aggressive language and bias in four languages viz. Meitei, Bangla,
Hindi and English. We have used a modified version of the tagset discussed in
[Kumar et al., 2018b]. These modifications in the tagset are made considering
the need to reduce the complexity of the earlier tagset and also in accordance
with the needs of comprehensively annotating the available dataset, based on
the observation of trends and patterns of discursive behaviour in each of
these languages. For instance, the category of caste/class bias was included
upon observing the nature of the comments in Bangla that were directed against
the beggar-turned-singer Ranu Mondol, whose overnight success and subsequent
change in attitude led to a deluge of comments directed not just at her gender
but also at her lower class and caste identities. Similarly, the category for
ethnic/racial bias was included upon observing the online animosity in the
interactions between the Meitei and Kuki tribes in the Meitei data. We will
discuss these decisions in more detail in the following sections of the paper.

This paper has been organized as follows. Section 2 gives a brief literature
review on the frameworks and models that have been built to study the language
of aggression and detect hate speech. Section 3 discusses the process of data
collection and annotation. We also discuss the process and reasons motivating
the modification of the earlier tagset and arrive at the current tagset.
Section 4 discusses the annotation guidelines that have been developed and
used for annotating the current dataset. Section 5 presents an analysis of the
dataset and its limitations. We also briefly discuss the results of our
baseline experiment for automatic aggression identification. Section 6
presents some concluding remarks on the tagset and guidelines thus developed.

## 2\. Previous Studies on Aggression

The wide range of interrelated phenomena that has been used for annotating the
datasets include broad dimensions such as abusive language [Nobata et al.,
2016]; [Waseem et al., 2017], toxic language ([Kolhatkar et al., 2020];
[Kaggle, 2020]), aggressive language ([Haddad et al., 2019]; [Kumar et al.,
2018b]; [Bhattacharya et al., 2020]), offensive language ([Chen et al., 2012];
[Mubarak et al., 2017]; [Nascimento et al., 2019]; [de Pelle and Moreira,
2016]; [Schäfer and Burtenshaw, 2019]; [Zampieri et al., 2019a], [Zampieri et
al., 2019b]; [Zampieri et al., 2020]), hate speech (several including [Akhtar
et al., 2019]; [Albadi et al., 2018]; [Alfina et al., 2017]; [Bohra et al.,
2018]; [Davidson et al., 2017]; [Malmasi and Zampieri, 2017]; [Schmidt and
Wiegand, 2017]), threatening language ([Hammer, 2017]) or narrower, more
specific dimensions such as sexism ([Waseem, 2016]; [Waseem and Hovy, 2016]),
misogyny, Islamophobia ([Chung et al., 2019]; [Vidgen and Yasseri, 2020],
homophobia [Akhtar et al., 2019], etc. Some of the datasets include a
combination of these such as hate speech and offensive language ([Martins et
al., 2018]; [Mathur et al., 2018]) or sexism and aggressive language
[Bhattacharya et al., 2020]. In fact, [Jurgens et al., 2019] has rightly
recommended for the abusive language researchers to broaden their scope so as
to also study more subtle (such as condescension) as well as more serious
forms of abuse (such as doxxing) and also posit the research within the
broader framework of justice.

However, despite the well-established practices of defining annotation
schemes, providing detailed annotation guidelines and also reporting results
of the inter-annotator agreement experiments for any task in NLP, a large
number of researchers engaged in the abusive language research have chosen to
ignore these. [Poletto et al., 2021] notes that “each resource is built
referring to ad hoc definitions of the phenomena addressed, shaped so as to be
suitable for a specific purpose, but what often lacks is a wider view on the
topic and an eye towards interoperability of resources”. Furthermore, a large
number of papers describing the datasets do not give any information about the
guidelines that they have used for annotation nor the inter-annotator
agreement score is mentioned ([Davidson et al., 2017]; [Ross et al., 2017]).

It could only be assumed from this that folk, common-sensical or simply
convenient understanding of these phenomena are employed for preparing the
datasets and training the model. It is generally accepted that these
phenomena, viz. abusive, offensive, aggressive, toxic, hate(ful) and harmful
language, are interrelated in some way but majority of the research rarely
engages sufficiently well with their object of study to understand what
exactly it is that they are trying to annotate and recognise. Some notable
exceptions include [Waseem et al., 2017]; [Fortuna et al., 2019]; [Ousidhoum
et al., 2019]; [Mandl et al., 2020] and [Kumar et al., 2020]. [Waseem et al.,
2017] posits a generic abusive language typology which may be utilised for
annotating datasets according to broad categories of whether it is targeted or
not and whether it is explicit or implicit - the typology is useful across
annotating a wide range of abusive phenomena but may not very useful in
understanding the interrelationships across those. [Ousidhoum et al., 2019]
puts different phenomena such as abusive, offensive, hateful, disrespectful
and fearful as sub-categories of ‘hostility’ - however, there is no explicit
attempt at establishing a hierarchical or scalar or any other kind of inter-
relationship among these phenomena and are probably assumed to be independent
of each other. On the other hand, [Mandl et al., 2020] classifies hate speech,
offensive and profanity as sub-categories of ‘hate and offensive’ - in this
case also there is no explicit attempt at establishing their inter-
relationship except stating that these are mutually exclusive, which may or
may not be always true.

One of the main reasons for using aggression as the meta-concept for
annotating the dataset was our need to use a theoretically-motivated and well-
researched phenomena for this study. We expected this to enable us to
rigorously prepare the tagset, annotation guidelines and also analyse the
dataset. Furthermore, using something a well-defined concept was also expected
to be helpful in making the dataset more interpretable and interoperable.

Aggressive language is listener-oriented, influenced by the surrounding and
situational context of society, by community policy, social norms, social
demographies, structural positions and relations, gender, age etc. It thus
indicates the psychological, situational and structural perspective of the
individual and the group of people [Weingartner and Stahel, 2019]. The comment
or the post coming from the individual level becomes a matter of concern if it
hurts the sentiments of a community or the ethnic group. The online platform
with its diversity of user, their cultural differences and beliefs can be
prove to be a catalyst for hate speech [Al-Hassan and Al-Dossari, 2019]. Thus,
online aggression on social media have the tendency to cause harm and becomes
a source of creating social problems.

In the present paper, we try to define and classify aggression in a
theoretically-informed way, build a tagset out of it and use that for the
automatic identification of aggression.

## 3\. Building and annotating an extensive dataset: Methodology

Kumar, in [Kumar et al., 2018a], proposed a detailed classification and tagset
for marking aggression and bias, which included the distinction between overt
covert aggression as well as the target-based classification such as
misogynistic, communal, geographical, sexual, etc. While the tagset was quite
detailed, it posed two problems - (a) there were too many tags to be
comprehended and classified manually by annotators with an appropriate degree
of precision; (b) it clubbed together non-mutually exclusive categories at the
same level (for example, curse / abuse and non-threatening aggression were at
the same level) - while this was handled somewhat by allowing for multi-label
annotation, in principle, it was a non-rigorous scheme and posed several
problems at the time of annotation.

In order to present the classification in a more principled way and also to
practically ease the task of annotation, the scheme has been restructured and
also extended to include those aspects which were left out in the earlier
version of the tagset. This modified tagset now includes a gradation of the
intensity of aggression in a comment (physical threat, sexual threat, non-
threatening aggression, curse / abuse) at every level, the discursive role of
the given aggressive comment also include three new / modified roles
(counterspeech, abet / instigate and gaslighting), and the addition of two new
categories are added to tag biased speech targeted at individuals or social
groups on the basis of their caste / class and ethnic /racial identities given
in Table 1.

| Aggression
---|---
Code | Aggression Level | TAG
1.1 | Overtly Aggressive | OAG
1.2 | Covertly Aggressive | CAG
1.3 | Non Aggressive | NAG
1.4 | Unclear | UNC
| Aggression Intensity Level
Code | Attribute | TAG
1A.1.1 | Physical Threat | PTH
1A.1.2 | Sexual Threat | STH
1A.1.3 | Non-threatening Aggression | NtAG
1A.1.4 | Curse/Abuse Aggression | CuAG
| Discursive Role
Code | Attribute | TAG
1B.1.1 | Attack | ATK
1B.1.2 | Defend | DFN
1B.1.3 | Counterspeech | CNS
1B.1.4 | Abet and Instigate | AIN
1B.1.5 | Gaslighting | GSL
| The Gender Bias
Code | Attribute | TAG
2.1 | Gendered Comments | GEN
2.2 | Gendered Threats | GENT
2.3 | Non-Gendered Comments | NGEN
2.4 | Unclear | UNC
| The Religious Bias
Code | Attribute | TAG
3.1 | Communal Comments | COM
3.2 | Communal Threats | COMT
3.3 | Non-Communal Comments | NCOM
3.4 | Unclear | UNC
| The Caste / Class Bias
Code | Attribute | TAG
4.1 | Casteist/Classist Comments | CAS
4.2 | Casteist/Classist Threats | CAST
4.3 | Non-Casteist/Classist Comments | NCAS
4.4 | Unclear | UNC
| The Ethnicity / Racial Bias
Code | Attribute | TAG
5.1 | Ethnic/Racial Comments | ETH
5.2 | Ethnic/Racial Threats | ETHT
5.3 | Non-Ethnic/Racial Comments | NETH
5.4 | Unclear | UNC
Table 1: The ComMA Project Tagset

This dataset was built over multiple stages of the project, in concordance
with the stages of development of the tagset. In the initial stages, the data
was collected from online news websites and were tagged on the basis of two
parameters: aggression and misogyny. In the second stage, this tagset was
expanded to include communal bias, and the term “misogyny” was altered to
“gender bias” so as to take into account bias directed at all genders
(including transgenders) as well as people with different sexual orientations.
The data was collected from the popular social media app YouTube, and included
data in English, Hindi, Bangla, and Meitei.

This data was collected from YouTube, Twitter and Telegram after identifying
specific videos and channels that attracted a great deal of hate speech and
aggressive speech in the comments section, which were directed at women and
minority religious groups, especially Muslims. The process of identifying
these videos and channels involved typing keywords and hashtags related to
controversial socio-political, religious, or cultural events in the recent and
not-so-recent past, which were covered in one of the four languages mentioned
above. The keywords used for searching the videos and the amount of raw data
(number of comments) they yielded are included in Table 2.

| Meitei
---|---
Sources: Videos/Channels | Search Keywords | Number of Comments
| #koubru | 33
#lawaimacha | 1
Twitter | #manipurdaCAB | 2
| #manipurdaILP | 19
| #meiteimuslim | 4
|  triple talaq manung hutna | 390
|  minister bishorjit nupi | 348
|  ccpur nupi | 138
|  paktabi diana | 107
|  non-manipuri | 77
|  pangal nupi | 320
|  utlou case | 206
|  manipur da CAB | 1118
|  Naga Accord | 304
YouTube |  Manipurdagi Meitei Furup mutpa | 59
|  Potti Kappe | 59
|  Pangal gi identity | 668
|  Muslim macha meitei na hatpa | 509
|  Momoco gi thoudok | 66
|  Koubru Conflict | 39
|  Rani Sharma | 164
|  Brinda Kanano | 87
|  M.U Normalcy | 577
|  ADC Bill | 115
| Bangla
Sources: Videos/Channels | Search Keywords | Number of Comments
| #SaveBangladeshiHindus | 257
| #TripurarJonnoTrinamool | 452
| #kutta | 42
| #khanki | 19
Twitter | #magi | 2
| #sala | 66
| #shuor | 17
| #harami | 62
| #hijra | 22
| Police Files | 936
| Khela hobe | 1147
YouTube | Bengal Ram Navami | 687
| Nusrat Jahan baby | 964
| Ranu Mondol | 457
| Pori moni | 329
| Hindi & English
Sources: Videos/Channels | Search Keywords | Number of Comments
| BJP | 848
Youtube | Ripped Jeans | 903
| Nehru-Gandhi | 947
| Hindutva | 2461
Telegram | Love jihad | 249
| Feminism | 21
| #MuslimVirus | 1422
Twitter | #BengalBurning | 651
Table 2: Sources of Raw Data, Keywords and Number of Comments

This dataset was then manually annotated by multiple annotators using a method
named the ‘Discursive Methods of Annotation’. It has been demonstrated in
several pragmatic and social science studies that the judgment of speakers on
socio-pragmatic phenomena like aggression or bias (or even hate speech) is a
function of:

  * •

Contextual factors, or more specifically the discursive experiences of the
speaker, including what kind of discourses the speaker has been a part of,
with whom, how many times, under what circumstances, and multiple other
factors (see [Agha, 2006] and his theory of enregisterment for some details on
this). At the time of annotation the speakers continuously discuss their
decisions with each other, whereby they modify each other’s discursive
experience(s) and possibly arrive at a space of mutual (dis)agreement.

  * •

Co-textual factors such as what else is included in the text as well as in its
immediate context.

At this point, the three co-textual factors included aggression, communal
bias, and gender bias. While these are individually evaluated, they are also
evaluated in the presence of each other during the process of annotation.

The socio-political position and ideological leanings of the annotators
working on the dataset also play a significant role in determining how the
data is analysed and tagged. While attempts are made after extensive
discussions within the team of annotators to draw clear guidelines and
definitions, supported by relevant examples from each language, for each tag
and subtag so as to bring personal differences of opinion to a minimum, human
differences between the socio-political, cultural, and ideological contexts of
the annotators still manage to draw out some degree of difference in tags
between the annotations of those working on the same dataset.

The details of annotators who worked on annotating the datasets is included in
the Data Statement attached in Appendix I of the paper.

During the course of annotating this dataset, we came across various other co-
textual factors that triggered bias and aggression that had not yet been
accounted for by our dataset. For instance, in the Bangla data, we observed
caste and class based bias and aggression directed at the beggar-turned-singer
Ranu Mondol, while in the Meitei data, we observed bias and aggression
directed at various ethnic groups. This led to the inclusion of the categories
of caste/class bias and ethnic/racial bias in the tagset, respectively.

Another major point of discussion that emerged from the annotation process was
to do with the aggression tagset, which was divided into OAG (overtly
aggressive), CAG (covertly aggressive), and NAG (non-aggressive speech).
However, these sub-tags did not capture the range and intensity of aggression
that was on display in the comments - from curse words to threats of a
physical and sexual nature. This necessitated the introduction of a subtag
under aggression titled Aggression Intensity Level (with tags for physical
threat (PTH), sexual threat (STH), non-threatening aggression (NtAG), and
curse/abuse based aggression (CuAG)), which would only be marked if the
comment was tagged OAG or CAG.

Along with this, another optional subtag was added under Aggression, titled
Discursive Effects, which is marked what the intended effect of a given
comment is - attack (ATK), defend (DFN), counterspeech (CNS), abet/instigate
(AIN), or gaslight (GSL). This particular subtag is used in a nuanced manner
to help us identify the nature or discursive effect of comments that can be
found on a thread, thus giving us an analytical tool to distinguish comments
in threads from independent comments directed at the video or content creator.
Each of the comments under a video is identified by a unique sequence of
numbers, which help the annotators identify an independent comment from a
thread in the spreadsheet they are working on. Using that knowledge, the
discursive effects of a conversation in a thread are marked accordingly. It is
to be noted that the annotators are discouraged from tagging comments as DFN,
CNS, AIN, and GSL unless they feature in a thread, and are in response to a
previous comment on the same thread. This particular subtag thus helps us
distinguish the ways in which aggressive speech plays out when a commenter is
engaging in a dialogue with another commenter in real time versus the kind of
one-way conversations that characterize independent comments.

This tagset is thus being developed during the course of annotating the raw
data such that each is contributing to the development of the other. The new
tagset is being used to annotate newer data, with the annotators conscious to
highlight any shortcomings and flaws in the tagset that can be improved upon
in the course of annotating the data, so that we can build a tagset that can
be most optimal at identifying various forms of aggression and bias in social
media interactions with the least margin for error.

The complete tagset is given in Table 1 and their definitions and examples are
discussed in the following subsections. We are reproducing the definitions and
examples of different levels of aggression and misogyny from [Bhattacharya et
al., 2020] since they have not undergone change since then.

### 3.1. Aggression

Aggression is classified on the basis of three broad levels, which have been
discussed below with suitable examples. It is to be noted that we are
annotating the way aggression is expressed in language but NOT the intensity
of aggression expressed via the text at this level. There is a common tag for
all categories, which is unclear (UNC) included with other tags in Table 1.

#### 3.1.1. Overtly Aggressive (OAG)

Any post/comment/text in which aggression is overtly expressed – either
through the use of specific kinds of lexical items or lexical features which
are considered aggressive and/or certain syntactic structures is to be
annotated using this label. It includes direct attacks against the victim, and
involves the use of commands, directives and specific kinds of lexical items
such as curse words. Some examples of overt aggression are given below:

  1. 1.

Abe Kanhaiya, suna inshallah (lal chaddi) gang Delhi MCD me 31 seat pe chunaav
lada aur inhe kul 51 voton se azadi mili hai. Kamaal o gaya be.

Oye Kanhaiya, I have heard that Insaallah (red chaddi) gang contested election
on 31 seats in Delhi MCD and they got azadi (freedom) by a total of 51 votes.
It is amazing.

  2. 2.

BJP wale jyada dhindhora pitate h hindutva ka…aur hindu me hi equality nhi de
pa rhe.isliye bjp ka virodh.baki states k compare me bjp ruled state me ye
jyada hota h.isliye v.

The BJP people brag about Hindutva more than others and they are not able to
ensure equality among Hindus. That is why this opposition against BJP. And
also because this happens more in the BJP-ruled states.

#### 3.1.2. Covertly Aggressive (CAG)

Any post/comment/text in which aggression is not overtly expressed is
classified as covert aggression. It is an indirect attack against the victim
and is often packaged as insincere, polite expressions (through the use of
conventionalised polite structures), and includes sarcasm and satirical
attacks. Some examples of covert aggression are given below:

  1. 1.

Harish Om kya anti-national ko bail mil sakti hai?

Harish Om can an anti-national get bail?

  2. 2.

PhD kab poori karoge? Kitne year tak scholarship loge, humlog k tax ka?

When will you complete your PhD? For how may years will you take scholarship
out of our tax?

  3. 3.

Beta teri ghar waapsi zaruri hai tu kuch zyaada hi shikshit ho gaya hai.

Son you need to return home, you have become a little too educated.

  4. 4.

Ebungo ngse Meitei Ningol mchani kaonu… Pakistani mcha Muslim ntte ko… Don’t
used such wahei wata.

Don’t forget that you are the son of a Meitei woman… you are not a Pakistani
Muslim… don’t use such words.

#### 3.1.3. Non-aggressive (NAG)

This label should be given to all those human speech samples which do not
exhibit aggression in any form. Some examples of non-aggressive speech are
given below:

  1. 1.

Desh ka agla Jaiprakash..Agla Ambedkar yehi hai..

This is the next Jaiprakash and Ambedkar of the country.

  2. 2.

Desh Ko tumhari jarurat hai…

The country needs you

  3. 3.

Tum aage badho .. Hamari subhkamnaey Tumhare sath hai…

You move ahead..Our best wishes are with you.

#### 3.1.4. Unclear (UNC)

In rare instances where it is not possible to decide how to tag a text
displaying any kind of bias, it is tagged as unclear (UNC). Annotators are
highly discouraged from using this tag and they are required to explicitly
mark the text based on the other tags. However, in cases where this is indeed
employed, all attempts will be made to resolve it through discussion and/or
majority voting among multiple annotators. Under no circumstance will this tag
make it to the final tagged document. It only serves as an intermediary tag
for flagging and resolving really ambiguous and unclear instances in the
annotation guidelines (discussed in [Bhattacharya et al., 2020]).

### 3.2. Aggression Intensity Level

This category marks the intensity of aggression in the current post/comment.
This feature is to be used only for aggressive posts/comments. As discussed
earlier, all the sub-types for judging the intensity of aggression in the
posts/comments discussed in [Kumar et al., 2018b] are included in this version
under aggression intensity level, which is illustrated in Table 1.

#### 3.2.1. Physical Threat (PTH)

A post/comment is potentially physically aggressive (verbal aggression
transforming into physical aggression) when it directly threatens to
physically harm, hit or even kill someone (an individual or the community).
Some examples of physical threat in comments are:

  1. 1.

abey saaley haarami teri aukaat kya hai tu hug dega aaja bihar tewra wIT KAR
RAHA HU ….MAREGA TU PAKKA .. JUB TAK DELHI ME HAI APNI KHUSIYA MANA MAI
THOKUNGAA TERE KO

You moron, illegitimate son, what is your standing, you will pee in your
pants. You come to Bihar, I am waiting for you. You will definitely die. You
celebrate the time till you are in Delhi. I shall shoot you.

  2. 2.

Muh kala hai dogle ka dil bhi kala gaddar hai mujhe tum dikh jaye sala juta
marunga dogala deshdrohi

This hypocrite has lost his face, his heart is also bad, as soon as I shall
see you moron, I will hit you with a shoe, you hypocritical anti-national.

#### 3.2.2. Sexual Threat (STH)

Any post/comment that contains words expressing sexual coercion and assault is
marked as sexual threat.

  1. 1.

Bhosri ke jab kuchh pata nahi hai to bolta kyu hai ja ke apni gaar marwa
halala me.

You fucker, when you do not know anything then why do speak. Go and get
yourself fucked in Halala.

  2. 2.

Tok chudi

Let me fuck you

  3. 3.

Momoco Fucker girl u looser fuck off

#### 3.2.3. Non-threatening Aggression,(NtAG

Any act of aggression targetted at personal attributes like one’s
intelligence, physical features, various identities, or anything else but not
containing a threat is classified as non-threatening aggression. Examples
include:

  1. 1.

Bhut kam jankari h babu tmko.general caste ki aabadi 15 percent v kam h.
general cat ka MATLAB hi h open category.

You know very little, boy. The population of general caste is less than 15
percent. The meaning of general category is open category.

  2. 2.

Tum sirf patar patar bolna jaante ho aur kuch nahi. Kuch acche karm bhi kar
liya karo, gareeb dil se dua denge, sukuun milega tujhe.

You only know how to blabber and nothing else. You do some good work also,
people will bless you from their heart, you will get peace.

#### 3.2.4. Curse/Abuse Aggression (CuAG)

Any comment containing an act of aggression that involves cursing or abusing
the victim is tagged as curse/abuse aggression. Some examples of curse/abuse
aggression are given below: 111All the examples given in this document are
‘real-life’ examples and they are reproduced exactly as they appear in the
actual post/comment and form part of our corpus. The comments written in the
Devanagari and Bangla scripts have been transcribed in the Roman script for
the benefit of the readers.

  1. 1.

Mujahid Irfan meri kaonsi pol khol Di iss ghade kanhaiya ne

Mujahid Irfan what has this ass Kanhaiya revealed about me?

  2. 2.

Abe kutte.… Tere pichwade me jo suvar baithe hai voh bhi tuje sun nahi rahe
hai saale to apni gandi jubaan bandh rakh …..koi bhi sun nahi raha

Oye dog the pig in your ass is also not listening to you you moron. So keep
your dirty mouth shut..nobody is listening to you.

  3. 3.

Manipur se kasuba kasubi gi makon natte khngbra nama napana nahei tamhandana
warktra jatlo

Manipur is not a place for prostitutes and gigolos. Did your parents gave you
proper education?

### 3.3. Discursive Role

This category refers to the role of the current post/comment in the ongoing
discourse. It could be one of five kinds: attack (ATK), defend (DFN),
counterspeech (CNS), abet / instigate (AIN), and gaslighting (GSL). Attack,
defend, and abet were included earlier in [Kumar et al., 2018b]. Now, it has
been extended to include counterspeech and gaslighting, and has combined abet
and instigate to form the tag abet / instigate (AIN). Each of these tags have
been given in Table 1 and discussed in detail below:

#### 3.3.1. Attack (ATK)

Any comment / post which attacks a previous comment / post is tagged as
attack. It can only be tagged for an aggressive comment. Some examples of
attack are given below:

  1. 1.

Muh kala hai dogle ka dil bhi kala gaddar hai mujhe tum dikh jaye sala juta
marunga dogala deshdrohi

This hypocrite has lost his face, his heart is also bad, as soon as I shall
see you moron, I will hit you with a shoe, you hypocritical anti-national

  2. 2.

tera samdhi mulayam singh yadav bolta tha rape koi badi galti nahi baccho se
galti ho jaati hai. phaasi ki kya jarurat hain. pravachan aur updesh khatam
kaha hotey hain. 100 chuhe khaakar billa chala haj ko.

Your child’s father-in-law Mulayam Singh used to say that rape is not a big
mistake and children make such mistakes. What is the use of hanging till
death. The preachings and speech hardly ends. He is now pleading innocence
after committing hundreds of crimes.

  3. 3.

Eikhoisu ymna tukutchei cgumbase adubu haothu haiba gumba cde haina roise
mikhrana touraga eikhoi dasu panba dudi nungai Jade makhoi na touriba lichat
tuge matung Ena makhoi da haikh loire. Haothu gaina…..keino cbu

We also don’t like the nature but don’t use the word Haothu (derogatory word
used towards tribal community). You cannot blame whole of us for the
wrongdoing by some people. Say it to them. What is this????

#### 3.3.2. Defend (DFN)

Any comment / post which defends or counter-attacks a previous comment / post
is tagged as defend. The previous comment / post must be an attack and the
current one should be in support of the victim. It could be both aggressive as
well as non-aggressive, but it is imperative that it be in the same thread as
the previous comment. Examples include:

  1. 1.

Kitna dukhi hai bhai tu, lagta hai teri pool kholdi kanhaiya ne. Agar tu jo
ilzam uspe laga raha hai wo sach hai toh wo kiyon jail me nahi hai. (Covertly
aggressive)

How sad you are bro. It seems that Kanhaiya has shown your true face. If the
accusation that you are levelling on him is true then why is he not in prison.

  2. 2.

Av tak chargesheet file nhi kar payi h Delhi police…Aur lab ne is bat Ko
confirm kiya ki anti-national slogan me Kanhaiya ki aawaz nhi h.ye dusre logo
ne kiya.kon kiya hoga …Samjhte hi hoge. (Non-aggressive)

Delhi Police has not been able to file chargesheet till now. And the lab has
confirmed that the anti-national slogan does not contain the voice of
Kanhaiya. It has been done by other people. You must have an idea who has done
it.

#### 3.3.3. Counterspeech (CNS)

Counterspeech is any direct response to hateful or harmful speech which seeks
to undermine it. Just as influential speakers can make violence seem
acceptable and necessary, they can also favorably influence discourse through
counterspeech. The most direct way it can succeed is to have a positive effect
on the speaker, convincing him or her to stop speaking dangerously now and in
the future. It can also succeed by having an impact on the audience – either
by communicating norms that make Dangerous Speech socially unacceptable or by
‘inoculating’ the audience against the speech so they are less easily
influenced by it. There are two types of counterspeech: organized counter-
messaging campaigns and spontaneous, organic responses (from Counterspeech —
Dangerous Speech Project). Here, we will tag both kinds of counterspeech as
CNS. Counterspeech is always non-aggressive in tone and content. Examples of
each case is given below:

  1. 1.

Main bolna chahta hun Sabhi se a ki jo galat Karen use Gali do pure bhartiyon
ko Gali na do please nahin to Sabhi Ke Munh Mein Gali aati hai aur aata hai
aapko bhi Dena chalu kar diya Jaega isliye Jo galat Karen uske sath sajae ho
ja ham log mante Hain vah karo Jo Sabke hit mein ho aap unke Upar Ishara Karke
pure bhartiyon ko Gali de rahe ho ya galat kar rahe ho please yah video ko
Sudhar ke bolo Gali dekar mat bolo please.

I want to tell everybody that you must abuse those who do wrong, but please do
not abuse all Indians, otherwise everyone will abuse and it will be directed
at you too. That’s why we ask that those who do wrong must be punished. Do
what we feel is right, do what is good for everybody. You are targetting (him)
and abusing all Indians, this is wrong. Please improve this video and don’t
abuse, please.

  2. 2.

Please do not defame Nehru. His personal life was his private affair. Judge
him by his contribution to the country.

Please do not defame Nehru. His personal life was his private affair. Judge
him by his contribution to the country.

#### 3.3.4. Abet / Instigate (AIN)

Any comment / post which supports or encourages a previous (negative) comment
and instigates an individual or group to perform an aggressive act is tagged
as abet/instigate. The difference between abet and instigate lies in how the
speech relates to the act of aggression. Instigation happens before the event
and its purpose is to trigger or provoke an act of aggression. Abetting is
speech that occurs during or after the act of aggression and its purpose is to
praise, support, and / or encourage that act as well as other such acts in the
future. In many cases it may be difficult to distinguish whether a comment is
abetting or instigating an act of aggression, as praise or support (abetment)
for an act of aggression can be interpreted as instigating such acts in the
future. The purpose of both is to enable and validate aggressive speech and
actions, hence the two have been included under one category in this tagset.

  1. 1.

Great sachchai likha aapne

You have written a great truth.

#### 3.3.5. Gaslighting (GSL)

Any comment/post that seeks to minimize the trauma or distort the memory of a
trauma faced by another person (usually mentioned in the previous
post/comment) is tagged as gaslighting. It is not the same as offering
consolation when someone expresses their pain or traumatic/negative
experience. Gaslighting is a form of manipulation; it seeks to present things
in a better light than it actually is. The intention of gaslighting is to make
the previous poster / commenter to feel like what they are feeling, seeing,
hearing, and saying is wrong, a misunderstanding, an exaggeration, an
overreaction, or attention seeking behavior.

One way to identify gaslighting is: it is always targeted at comments that
talk about one’s personal experiences (including traumatic and painful
memories). Gaslighting looks very much like fact-checking at times and can be
difficult to separate from the latter. It should only be marked in cases where
it is clearly gaslighting. While it is possible to gaslight comments that
present verifiable facts, unless it is very clear that it is in fact
gaslighting, it is best not to tag it as such. Examples include:

  1. 1.

Tar cheye bhalo garur mangsho khao tomra. Tomra napak jinish bokkhon korcho.
Tomra opobitro. Tomaderke pobitro korar cheshta korteche. Huzoor tomader bhalo
chacche.

(This is in response to a previous comment that says that the Maulvi
referenced in the video is asking people to stop drinking cow’s urine) It’s
better that you guys have cow’s meat (than cow’s urine). You people are
consuming impure things. You are impure people. (The Maulvi) is trying to make
you pure. Huzoor wants the best for you.

  2. 2.

Ei Huzoor Sharia protishthar kotha bolche, e Hindu der humki dicche na. Aar
hya, eti jene rakhun kono Muslim Hinduder otyachar kore na, borong Hindu ra
Muslim der otyachar kore. Aapni Kashmir er dike takiye dekhen Hindura
Muslimder otyachar korche, aapni Delhir dike takiye dekhen Hindura Muslimder
otyachar korche, Muslimder masjid dhongsho korche.

This Huzoor is talking about Sharia law, he is not threatening Hindus. And
yes, you should know that no Muslim tortures Hindus, rather Hindus torture
Muslims. Look at Kashmir, look at Delhi, they are destroying the Muslims’
masjids.

  3. 3.

Amar Qarni wakhal du da ne ngaorise mi na ngang li ba mayam si kari leige
khanda ba makhei eikhoigi di alah hairadi loire haina sum tou leiradi mi
loinaba wagada ba manli.

Amar Qarni the mind of yours is confused because you are not ready to hear
anything what the other person is trying to say but you are in into ’Allah is
everything’ concept.If you still believe Allah can do everything than no good
will happen.

The intention of the first comment is to manipulate the readers into believing
that what the Maulvi, Huzoor, is asking for is for the good of the people,
when it is actually a call to declare a Muslim sect, the Kadiyanis, as un-
Muslims due to their ”un-Muslim” beliefs and practices.

The second comment intends to draw a picture that paints Muslims as eternal
victims and Hindus as eternal torturers without taking into account the whole
picture and acknowledging that both communities have harmed the other at
various points in history. It intends to alter the cultural memory of
historical events, and thus qualifies as a classic example of gaslighting.

The third comment is a reply to earlier comment of portraying Islam as the
best religion of all and no other people of different religion will
understand. The intention is to express that every Muslim has the mindset of
portraying ”Allah as the best of all” and they should not forget that they are
in Meiteiland.

### 3.4. Gender Bias

Any post / comment expressing bias based on the gender identity of its target
comes under gender bias. It includes bias based on gendered stereotypes,
traditional gender roles of the speaker or addressee, and biased references to
one’s sexual orientation (discussed in [Bhattacharya et al., 2020]). This
tagset also includes Gendered Threat (GENT) and Non-Gendered (NGEN) comments.
It has been given in Table 1 and discussed in detail below:

#### 3.4.1. Gendered Comments (GEN)

Any text expressing bias based on the gender identity or stereotypical gender
roles of the speaker or addressee is tagged as GEN. It also includes biased
references to one’s sexuality and sexual orientation, homophobic or
transphobic slurs, and comments of a sexual nature . Examples include:

  1. 1.

tere ma se puch sale tera bap kon h

Go and ask your mother who your father is.

  2. 2.

Shk ka d nupi cgi huithu ga mnle

The face of the woman looks like hui(dog)-thu(vagina) (dog-vagina).

  3. 3.

Rate ktw magir

What is the rate of this slut?

#### 3.4.2. Gendered Threats (GENT)

Any comment that can be read as a gendered threat to the well-being of the
victim (such as rape threats or threat of physical violence owing to their
gender role / identity) can be tagged GENT. Some examples of comments tagged
with gendered threats are given below:

  1. 1.

She must be sent to some ranga billa first. Those who think these are funny
names must read about these guys first before calling yourself by such names.

  2. 2.

Nupi do hatok khro mahik mapini.

Kill the woman because she is the mother of all viruses.

#### 3.4.3. Not Gendered Comment (NGEN)

Any post / comment which does not display any gender bias will be marked non-
gendered. Examples include:

  1. 1.

Magise chinpha faraga lakpado kaothokhibagine twrise

She came memorising the line and forgot the lines.

  2. 2.

Muhammad was a demoniac person like this maula.

The first comment is a sarcastic comment but tagged as NGEN as it is not
gendered bias.

The last comment is communal in nature but does not display gender bias, hence
it has been tagged NGEN.

### 3.5. Communal/Religious Bias

Any post/comment that targets the real/presumed religious
affiliation/identity/beliefs of the victim is tagged as communal (COM). These
comments can be aimed against attempts to establish religious harmony and
cooperation, can discriminate against religious practices, propagate false
religious ideas, or (negatively) stereotype an individual/community based on
their religious identity.

The labels for tagging communal/religious bias is displayed in Table 1. These
annotation guidelines are discussed below in detail.

#### 3.5.1. Communal Comments (COM)

Any post/comment that targets the real/presumed religious
affiliation/identity/beliefs of the victim is tagged as communal (COM). These
comments can be aimed against attempts to establish religious harmony and
cooperation, can discriminate against religious practices, propagate false
religious ideas, or (negatively) stereotype an individual/community based on
their religious identity. Some examples of communal comments are given below:

  1. 1.

Is nalayk ko koi ye bataye ki jaha bhi Muslim bahut jyada ho jate h waha
hinduo ko kyo nahi jine dete Pakistan or bagladesh me hindu ka kya hal h
kashmiri pandit bisthapit kyo h karana or godhara me hinduo par atyachar kisne
kiya

Somebody tell this idiot that wherever there is a large number of Muslim, why
are Hindus not allowed to survive there. What is condition of Hindus in
Pakistan and Bangladesh. Why were Kashmiri Pandits forced to migrate Who has
victimised the Hindus in Karana and Godhara.

  2. 2.

(Don’t bring your burrowed Christainity in the land of Kangleipak). Hill don’t
belong to tribal. Govt. didn’t handover any land to any particular
people.(Stupid people already destroy the forest even plant illegal poppy.
History cannot be destroy by bringing refugees). Truth will always reveal one
day. God will know who is doing the wrong things and the lies. They are
getting all the benifit from the govt. and use the govt rule. (Then they will
say the govt cannot control the hills area as hills belong to them.) The mind
of the hill people are filled with hate and act as a enemy of all other
community…. We Meetei is also tribal of this land. Accept the history. Accept
the truth. (If they really concern about the environment they shouldn’t plant
the illegal poppy.. Everyone knows their evil mindset…)

The intention of the comment indicates a target towards the tribal community
following Christianity. It targets the religious affiliation of the hill
people. The comment also counts as overtly aggressive.

  3. 3.

Hoi adunabu huranba, acid attack amdi drug yonbagi chtnabi asi nongma2
ghenktlakeecdi.. Noi mohammad to rapist ne kwno, paedophile.. Seitan hido
adune.. . Kasubi mcha

For that reason only, drug case, acid attack cases are increasing. The
Mohammad that you people believe in is actually a rapist, a paedophile and he
is the Satan. Son of a bitch.

#### 3.5.2. Communal Threats (COMT)

Any comment/post that carries a threat to the well-being of the victim (such
as threat of rape, riot, or physical violence) owing to their religious
identity is tagged as a communal threat. Some examples of communal threat are
given below:

  1. 1.

Dekho mere dalit bhaiyyo aap muslim ko support karte ho lakin muslim hamesha
vistarvadi niti ke hote Unke hisaab se koi dusra dharm nahi jee sakta Unka bus
chaleto wo sabka gala katde tab to wo apko bi nahi chodenge Bus wo apka fayda
utha rahe he

Look my Dalit brothers, you support the Muslims but the Muslims believe in
being exclusive. They believe no other religion must exist. If it suited them,
they’d slit everyone’s throats. Then they won’t spare you either. They are
just taking advantage of you.

  2. 2.

Echal eche singse takpise Islam gi mrmda.. Kari fttaba ywge hibado uthokse..
Nttrad manipur se khra leiradi kangleistan ongrani.. Meetei di mcha pokpsu
hndre moidi machasu 6 tei tade.. Chekchlhwdrad akhoi se loina
yotkhrani..Religion, civilization loina mngkhrni..Islamaphobia santokse

Lets educate our sisters about the Islam and its wrong teachings. Else Manipur
will become Kangleistan. They give birth to more than six children and Meetei
are controlling birth. If we still ignore and not take necessary steps we will
be eaten by them. Our religion and civilisation will vanish. SO, let us spread
Islamphobia.

#### 3.5.3. Non-Communal Comments (NCOM)

Any post/comment that is not communal in nature will be tagged as non-
communal. Examples include:

  1. 1.

Awesome she is!!!

  2. 2.

Musolmanra kono dhormoke chhoto korena.

Muslims do not cast aspersions on any religion.

### 3.6. Caste/Class Bias

Any comment/post that targets the caste or class identity of the victim and
discriminates against them on the basis of it is marked as caste/class bias.
While caste and class are two distinct categories of social segregation that
function in diverse and specific ways, they have been subsumed under one
category in this tagset for two reasons.

The first reason has to do with the data which led to the addition of this
category to the tagset. The Bangla data on Ranu Mondol contained comments
directed at her low caste and class position in society. However, it was often
difficult to discern which aspect of her class and caste identities was
actually being targetted, as the comments were often derogatory but vague. It
made sense to argue that the cumulative inferior position she occupied in
society had partly triggered the vitriol against her (the others had to do
with her conduct in the public eye); had she been only of lower caste or lower
class, the nature of the comments might not have been much different.

The other reason for clubbing caste and class together had to do with ease of
annotation. The previous reason necessitated that we keep caste and class
under one category to avoid mistagging a comment that was vague in nature. An
allied reason to support this decision was the need to keep the tagset compact
and prevent it becoming too extensive and unwieldy. Hence, in this tagset,
caste and class have been merged under one broad category. However, that does
not mean that every comment tagged by this tag demonstrates both class and
caste bias/ discrimination. In some cases it can flag caste bias, in others
class, and in yet others, both.

These labels (Casteist/Classist Comments (CAS), Casteist/Classist Threats
(CAST), Non-Casteist/Classist Comments (NCAS), and Unclear (UNC) have been
shown in Table 1.

#### 3.6.1. Casteist/Classist Comments (CAS)

Any comment/post that targets the caste or class identity of the victim and
discriminates against them on the basis of it is marked as caste/class bias.
Examples include:

  1. 1.

Central govt k cabinet me dekho top k 10 ya 20 ministers ko.1-2 ko chor yahi
15 percent wale h.

Look in the cabinet of central government. Among the top 10 – 20 ministers,
besides 1 – 2, they are only these people with 15% reservation.

  2. 2.

Uni matite boshe kheto na, uni dining table e boshe kheto. Kukurer pete ghee
porle ja hoy aar ki

She never ate on the floor, she ate at a dining table. This is what happens
when you give a dog a taste of ghee.

#### 3.6.2. Casteist/Classist Threat (CAST)

Any post/comment that poses a threat to the victim based on their caste/class
identity is tagged as Casteist/Classist Threat.

  1. 1.

ei Ranu magi nijeke ki mone kore? Eke aar er meyeke chuler muthi dhore
Ranaghat station e bhikarir bati dhoriye boshate hobe. Tokhoni giye era thik
hobe.

What does this Ranu think of herself? She and her daughter need to be dragged
by their hair and made to sit in Ranaghat station with beggars’ bowls. Only
then will they be put in their place.

#### 3.6.3. Non-Casteist/Classist Comments (NCAS)

Any post/comment that is not casteist or classist in nature is tagged as Non-
Casteist/Classist. Examples include:

  1. 1.

Hindu sobai amra ek ekhane Brahman, Namashudra abar ki? Amra sobai Sanatani
etai amader porichoy hok.

All of us Hindus are one, what is this Brahman, Namashudra etc.? We are all
Sanatani, let this be our identity.

  2. 2.

Seta lekha nai but ata dekte Hobe borno babosta k Kara koreche

That is not written but we must see who made the caste system.

### 3.7. Ethnic/Racial Bias

Any post/comment directed at and discriminating against the ethnic or racial
or tribal identity, culture, language, skin colour, physical features, place
of origin, nationality, etc. of the victim can be tagged as ethnic/racial
bias. The annotation labels (Ethnic/Racial Comments (ETH), Ethnic/Racial
Threat (ETHT), Non-Ethnic/Racial Comments (NETH) and Unclear (UNC)) are given
in Table 1.

#### 3.7.1. Ethnic/Racial Comments (ETH)

Any post/comment directed at and discriminating against the ethnic or racial
or tribal identity, culture, language, skin colour, physical features, place
of origin, nationality, etc. of the victim can be tagged as ethnic/racial
bias. Examples include:

  1. 1.

Kanglup Khudingmak hourakpham loina lei. Aduna Kuki singi hourakfam kaidano
amta haibirak o.

Every community has its own origin. So,please tell us the origin of Kuki.

  2. 2.

Ye chinkis ki bohat jal rahi he hamare desh ki Badti Military Power dekha kar.
Sale China valo khud ke desh me original mal banana sikh lo pehle fir hume
samjao.

These chinkis (Chinese) are so jealous of the increasing military power of our
country.You moron Chinese first learn to manufacture the original stuffs then
teach us.

  3. 3.

Another in different places Tripuris were killed by Bengali.What about that?

#### 3.7.2. Ethnic/Racial Threat (ETHT)

Any post/comment that expresses the intention to inflict harm on an individual
or community based on their ethnic or racial or tribal identity is tagged as
ethnic/racial threat. Examples include:

  1. 1.

Kuki, Naga and Meitei, all came to the soil of the present day Manipur much
before Inida got independence. There is no way one can be termed refugee.
Meitei and Nagas should think twice when they term one community as refugee
otherwise, u r inviting bloodshed.

  2. 2.

Kuki Koubru touganu sigumba mathok oaktaba thabakse gyan tana hingmin na c mi
amagi limit hyba amasu leibane madu keidw nungda pairage sanage kha ganu
pokkani sathina sabani.

Kuki people don’t talk about Koubru.Lets live in a peaceful manner because
everybody has a limit and if you are thinking to play with our patience then
you will get burned.

#### 3.7.3. Non-Ethnic/Racial Comments (NETH)

Any text which does not contain any ethnic or racial bias will be marked NETH.

  1. 1.

Amra shobai manush ager kotha bhebe luv nei amra mile thakte hobe tripura ke
shundor korar jonno

We are all humans, no point thinking about the past. We have to live in
harmony to make Tripura a beautiful place.

  2. 2.

It’s true. We should come out of communalism and live in peace. Think about it
this way, we all have bad pasts but that doesn’t mean we cannot have a good
future. Being educated we should stop ethnic riots. Ultimately, it’s human
lives that are lost.

It must be noted here that these tags of gender, communal, caste/class, and
ethnic/racial bias are only used in comments that demonstrate some kind of
bias against that subcategory. Mere mention of these social groups in a
post/comment will not entail that they be tagged as such if there is no bias
at play in the given post/comment. This point is amply at display in the
examples furnished in the above sections.

## 4\. Further Annotation Conventions

Annotation based on this tagset is at the document level - it could be a
comment or any one segment of the discourse, similar as (discussed in [Kumar
et al., 2018b] and [Bhattacharya et al., 2020]). Further, this tagset is
extended with the intensity of aggression and discursive role of any
comment/post. Tagging of intensity is based on the use of aggressive vocab
commentor used in comment/post. While, discursive roles are mainly marked
where conversation is in thread as well as in normal comments/posts where it
is looking like dialogue conversation.

## 5\. Inter-annotator agreement

The first phase of the annotations was done by 9 annotators - 5 for Meitei, 3
for Hindi and 2 for Bangla. The Meitei, Bangla and Hindi has 140, 275 and 230
comments respectively. Comments are taken from YouTube and Twitter. The
annotation is done using the aggression, gender bias and religious bias
tagset. The Krippendorff’s Alpha is used to measure inter-annotator agreement
for the and Meitei, Bangla and Hindi, given in Table 3. We did not find any
gendered comment in Meitei and communal comment in Bangla, thus the Table 3
has not any IAA value for them. Overall from the Table 3 we can say that the
IAA is very less in first phase.

Language | Meitei | Bangla | Hindi
---|---|---|---
Aggression | 0.28 | 0.41 | 0.42
Gender | - | 0.26 | 0.28
Religious | 0.38 | - | 0.41
Number of Annotators | 5 | 2 | 3
Number of Comments | 140 | 275 | 230
Table 3: Inter-annotator agreement Phase 1

Annotators were instructed to follow the annotation guidelines but keeping the
option open for annotator’s own interpretation while doing the annotation. The
discussions based on the inter-annotator agreement experiment we have tried to
reduce the disagreement among the annotators and reaching at an agreement in
most of the instances. During the discussion sessions we felt need to add new
categories and subcategories and tagset and updated the annotation guidelines.

Language | Meitei | Bangla | Hindi
---|---|---|---
Aggression | 0.51 | 0.66 | 0.93
Aggression Intensity | 0.49 | 0.69 | 0.77
Discursive Role | 0.66 | 0.74 | 0.75
Gender | 0.56 | 0.81 | 0.80
Religious | 0.27 | 0.80 | 0.82
Caste / Class | - | 0.49 | -
Ethnicity / Racial | 0.75 | - | -
Number of Annotators | 3 | 3 | 2
Number of Comments | 205 | 208 | 209
Table 4: Inter-annotator agreement Phase 2

The results of Phase 2 of the inter-annotator agreement shown in Table 4. It
was conducted on 205 comments for Meitei, 208 and 209 comments for Bangla and
Hindi respectively. There were 3 annotators in Meitei and Bangla and 2 in
Hindi. There is significant improvement in Phase 2 of IAA when compare to
Phase 1. In all categories of Bangla (except Cast) and Hindi we get IAA of
more than 65%. We did not find any casteist comments in Meitei and Hindi and
ethnic comment in Bangla and Hindi in the dataset.

## 6\. Dataset

### 6.1. Training Set

The training dataset contains a total of 12200 comments in four languages -
Meitei, Bangla, Hindi and English. Language-wise distribution is given in
Figure 1, which is precisely 26.27% Meitei, 27.76% Bangla, 45.97% Hindi &
English.

Figure 1: Languages in the Dataset | Aggression
---|---
TOTAL | OAG | CAG | NAG
Meitei | 3,209 | 456 | 1,495 | 1,258
Bangla | 3,391 | 1,782 | 494 | 1,115
Hindi & English | 5,615 | 3,052 | 969 | 1,594
Multilingual | 12,211 | 5,289 | 2,956 | 3,966
| Gendered
TOTAL | GEN | NGEN |
Meitei | 3,209 | 203 | 3,006 |
Bangla | 3,391 | 1,271 | 2,120 |
Hindi & English | 5,615 | 1,175 | 4,440 |
Multilingual | 12,211 | 2,647 | 9,564 |
| Communal
TOTAL | COM | NCOM |
Meitei | 3,209 | 242 | 2,967 |
Bangla | 3,391 | 416 | 2,975 |
Hindi & English | 5,615 | 1,213 | 4,402 |
Multilingual | 12,211 | 1,869 | 10,342 |
Table 5: The ICON Training Dataset

In Figure 2 the distribution of overt, covert and non-aggression in Meitei
14.21%, 46.59% and 39.20%; in Bangla 52.55%, 14.57% and 32.88%; and in Hindi &
English 54.35%, 17.26% and 28.39%; respectively. The overall distribution of
three major categories of aggression are 43.31% overt, 24.21% covert and
32.48% non-aggression. In Meitei 46.59% comments are covertly aggressive while
in Bangla, Hindi and English more tan 50% comments are overtly aggressive.

Figure 2: Aggression in the Dataset

Overall 21.68% portion of the comments are gendered and language-wise
percentage in Meitei and Hindi is 6.33% and 20.93% respectively and 37.48% for
Bangla, shown in Figure 3. Total communal comments are more than 15% and
language-wise communal comments distribution in Meitei, Bangla, and Hindi are
7.54%, 12.27% and 21.60% respectively, given in Figure 4. On broader scale
Bangla has most number of gendered comments, whereas Hindi and English has
most of communal comments.

Figure 3: Misogyny in the Dataset Figure 4: Communal in the Dataset

Co-occurrence graphs of Aggression, Misogyny and Communal categories given in
Figure 5, Figure 6, Figure 7 show that most of the communal comments are
overtly or covertly aggressive in all languages. Similar pattern could be seen
for gendered comments.

Figure 5: Co-occurrence heatmap for Meitei Dataset Figure 6: Co-occurrence
heatmap for Bangla Dataset Figure 7: Co-occurrence heatmap for Hindi Dataset

In the wider sense the distributions in co-occurrence graph in Figure 8, we
can say that if a comment is gendered or communal, possibility is that the
comment has some kind of aggression.

Figure 8: Co-occurrence heatmap In The Dataset Word |  Frequency |  Frequency Agg./Gen./Com.
---|---|---
muslimvirus |  989 |  809
muslimsspreadingcorona |  146 |  122
india |  341 |  167
muslim |  338 |  178
meitei |  326 |  77
desh |  220 |  84
desh (in Devanagari) |  209 |  89
people |  193 |  92
hindu |  169 |  75
nupi |  166 |  83
covid |  163 |  128
modi |  191 |  -
muslims |  184 |  153
nehru |  173 |  -
pangal |  266 |  -
vote |  193 |  -
ram |  165 |  -
congress |  163 |  -
manipur |  257 |  -
bjp |  795 |  -
corona |  - |  105
corona (in Devanagari) |  - |  105
maa |  - |  100
women |  - |  87
movie |  - |  85
coronajihad |  - |  82
randi |  - |  81
teri |  - |  81
Table 6: Union of Top 20 Most Frequent Words In The Dataset And In
Aggressive/Communal/Gendered Or Combined Comments In The Dataset

When we do a union of the 20 most frequent words taken from comments which are
marked aggressive/gendered/communal or combination of these and 20 most
frequent words in the dataset we get the words shown in Table 6. The words
like ‘muslim’, ‘hindu’222Major religious group in India, ‘covid’,
‘congress’333Indian National Congress is a second major political party in
India, ’corona’, ‘bjp’444BJP is a Hindu Nationalist Organisation currently
heading the central government of India, ‘vote’ are mostly use in the context
of religion and politics were present in communal comments.

The words like ‘maa’(mother), ‘randi’(prostitute), ‘nupi’(girl), ‘women’ were
used in the comments which are misogynistic and gendered, the word
‘teri’(your) frequently occurred with these words.

The comments in which the above mentioned words occurred, also turn out to be
aggressive. Most gendered/communal comments are aggressive in the dataset.

When we look at the count of the common words (the word which has some count
in both the columns ‘Frequency’ and ‘Frequency Agg./Gen./Com.’) and if we take
the word ‘muslims’, it has occured 184 times in the dataset out of which it
has occured 153, about 83% times in aggressive/gendered/communal comments. If
we take the word ‘hindu’, it has occured 169 times in the dataset out of which
it has occured 75, about 44% times in aggressive/gendered/communal comments.
Similarly, the word ‘nupi’(girl) has occured 50% of the times in
aggressive/gendered/communal comments. The words like ‘modi’, ‘nehru’, ‘ram’,
‘congresss’, ‘bjp’ which do not have any count in the ‘Frequency
Agg./Gen./Com.’ column , they mostly occur in non-aggresive/non-gendered/non-
communal comments. The words like ‘maa’(mother), ‘women’, ‘coronajihad’,
‘randi’(prostitute), which do not have any count in the ‘Frequency’ column ,
they mostly occur in aggresive/gendered/communal comments.

Figure 9: Top 20 Most Frequent Words In The Dataset Figure 10: Top 20 Frequent
Words In The Dataset For Comments Which Are Either
Aggressive/Communal/Gendered Or Combination Of These

### 6.2. Test Dataset

Test data consists total of around 3000 comments, precisely 34.1% Meitei,
32.4% Bangla, and 33.5% Hindi shown in Figure 11.

Figure 11: Test Dataset Proportion

The sources for the test data comments are YouTube, Twitter and Telegram. The
distribution of aggressive comments in the test data given in the Table 7, in
Meitei most of the comments are ‘covertly aggressive’. The ‘overtly
aggressive’ and ‘non - aggressive’ are in equal proportion. Bangla has
‘covertly aggressive’ and ’non - aggressive’ comments in equal proportion and
‘overtly aggressive’ are more in number. Hindi has least number of ‘covertly
aggressive’ comments.

| Aggression
---|---
TOTAL | OAG | CAG | NAG
Meitei | 1,020 | 315 | 391 | 314
Bangla | 967 | 465 | 244 | 258
Hindi & English | 1,002 | 440 | 85 | 477
Multilingual | 2,989 | 1,220 | 720 | 1,049
| Gendered
TOTAL | GEN | NGEN |
Meitei | 1,020 | 317 | 703 |
Bangla | 967 | 303 | 664 |
Hindi & English | 1,002 | 204 | 798 |
Multilingual | 2,989 | 824 | 2,165 |
| Communal
TOTAL | COM | NCOM |
Meitei | 1,020 | 141 | 879 |
Bangla | 967 | 106 | 861 |
Hindi & English | 1,002 | 362 | 640 |
Multilingual | 2,989 | 609 | 2,380 |
Table 7: The ICON Test Dataset

Overall 28% of the comments are gendered and 20% comments are communal. Hindi
has most number of communal comments given in Figure 14.

If we look at co-occurence of different tags in the Meitei test data
illustrated in the Figure 15, out of 141 communal comments of Meitei 131 are
aggressive, and out of 317 gendered comments 306 has aggression. In Figure 16,
Bangla test set has 97 communal comments which are also aggressive, out of
total 106 communal comments and 295 out of 303 gendered comments are
aggressive. In Hindi test data set co-occurence of the tags shown in the
Figure 17, 102 out of 204 gendered comments are communal and 341 out of 362
communal comments have some kind of aggression. From Figure 18, we can say
that most of the gendered and communal comments are ‘overtly aggressive’.

Figure 12: Aggression Test Dataset Figure 13: Misogyny Test Dataset Figure 14:
Communal Test Dataset Figure 15: Co-occurrence heatmap for Meitei Test Dataset
Figure 16: Co-occurrence heatmap for Bangla Test Dataset Figure 17: Co-
occurrence heatmap for Hindi Test Dataset Figure 18: Co-occurrence heatmap In
Test Dataset

## 7\. Baseline Experiments and Results

We have tried different basic preprocessing of the train data such as
lowercasing of characters, remove stopwords of english available in NLTK,
hindi and bangla stopwords taken from555https://github.com/WorldBrain/remove-
stopwords/tree/master/lib, replacing ’url’, ’@someusername’, with ’URL’,
’@username’ respectively and remove ’#’ in ’#tags’. Improvement in results
could only be seen when character caselowering was done and stopwords have
been removed, all other preprocessing and their combination did not improve
the results. We have used combination of character[3, 4, 5] and word[1, 2, 3]
n-gram as features and tf-idf as weight scheme to train SVM with different C
values [0.001, 0.01, 0.1, 1, 5, 10]. Training and testing of the model is done
on train and test data set details given in Table 5 and Table 7 respectively.
Evaluation of the model is done using two standard evaluation metrics used for
multi-label classification. instance-F1: It is the F-measure averaging on each
instance in the test set i.e. the classification will be considered right only
when all the labels in a given instance is predicted correctly. micro-F1: It
gives a weighted average score of each class and is generally considered a
good metric in cases of class-imbalance. Results for all languages and label
class (category) is shown in Table 8.

Language | Category | micro-F1 | Instance-F1
---|---|---|---
| Aggression | 0.47 | -
| Gender Bias | 0.70 | -
Meitei | Communal Bias | 0.86 | -
| Overall | 0.68 | -
| - | - | 0.34
| Aggression | 0.40 | -
| Gender Bias | 0.75 | -
Bangla | Communal Bias | 0.88 | -
| Overall | 0.68 | -
| - | - | 0.26
| Aggression | 0.56 | -
| Gender Bias | 0.80 | -
Hindi | Communal Bias | 0.68 | -
| Overall | 0.68 | -
| - | - | 0.26
| Aggression | 0.47 | -
| Gender Bias | 0.77 | -
Multilingual | Communal Bias | 0.82 | -
| Overall | 0.68 | -
| - | - | 0.29
Table 8: Baseline Results

## 8\. The Way Ahead

In this paper we have presented a multilingual dataset annotated with
different levels of annotation and bias. Currently the dataset consists of
over 15,000 data points in Meitei, Bangla, Hindi and English, collected from
various sources including YouTube comments, Facebook, Twitter and Telegram.

We are currently working on augmenting the dataset with more data points from
more Indian languages from different language families and also from more
sources. We are also experimenting with different kinds of models for building
an automatic identification system for aggression and bias.

## 9\. Acknowledgments

This research is funded by Facebook Research under its Content Policy Research
Initiative.

We would like to thank Mohit Raj, Shiladitya Bhattacharya, Afrida Aainun
Murshida, Sanju Pukhrambam and Diana Thingujam for helping us out with
annotations at different points in the project.

## 10\. Bibliographical References

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##  Appendix A Data Statement

### A.1. Header

  * Dataset Title: ComMA Dataset v0.2

  * Dataset Curator(s):

    * –

Akash Bhagat, Indian Institute of Technology-Kharagpur

    * –

Enakshi Nandi, Panlingua Language Processing LLP, New Delhi

    * –

Laishram Niranjana Devi, Panlingua Language Processing LLP, New Delhi

    * –

Mohit Raj, Panlingua Language Processing LLP, New Delhi

    * –

Shyam Ratan, Dr. Bhimrao Ambedkar University, Agra

    * –

Siddharth Singh, Dr. Bhimrao Ambedkar University, Agra

    * –

Yogesh Dawer, Dr. Bhimrao Ambedkar University, Agra

  * Dataset Version: Version 0.2, 2nd October 2021

  * Dataset Citation: NA

  * Data Statement Authors:

    * –

Enakshi Nandi, Panlingua Language Processing LLP, New Delhi

    * –

Laishram Niranjana Devi, Panlingua Language Processing LLP, New Delhi

    * –

Shyam Ratan, Dr. Bhimrao Ambedkar University

  * Data Statement Version: 1, 17th November 2021

  * Data Statement Citation and DOI: NA

  * Links to versions of this data statement in other languages: NA

### A.2. Executive Summary

The objective of working on this dataset is to identify and tag aggression and
various kinds of bias (gender, communal, caste/class, ethnic/racial) in social
media discourse. To that end, this dataset has been compiled by collecting
over 15,000 comments from YouTube, Facebook, Twitter and Telegram in Meitei,
Bangla, Hindi and English, with around 5000 comments in Meitei, Bangla and
Hindi each, which were all mixed with English data. The data was collected
from videos and posts that were politically, socially, sexually, religiously,
racially, or otherwise polarized or controversial in nature, so as to elicit a
wide and extensive range of hateful, aggressive, gendered, communal, casteist,
classist, and racist speech data for our dataset.

### A.3. Curation Rationale

This dataset was created with the ultimate goal of developing a system that is
able to identify and tag aggression, gender bias, communal bias, caste/class
bias, and ethnic/racial bias in social media discourse. To that end, this
dataset has been manually annotated by multiple annotators in order to
identify the linguistic and pragmatic features that characterize aggression,
gender bias, communal bias, caste/class bias, and ethnic/racial bias in the
comments on posts, videos, and articles posted on social media sites such as
YouTube, Facebook, Twitter, and Telegram.

The specific social media posts and articles whose comments we collected were
selected manually, and then crawled with the help of their respective web
crawlers. This selection process was contingent on many factors, the chief of
which was the need to collect as many aggressive, gender biased, communal,
casteist, classist, and racist comments as possible to create a robust
dataset. To that end, we focused on identifying controversial posts of a
politically, socially, sexually, communally, and racially charged nature that
have elicited a significant number of the kind of comments described above. We
have then followed similar suggested posts, videos, or articles on the
platform to collect more data of a similar or comparable nature. The second
factor was language: the comments needed to be in Meitei, Bangla, and Hindi
for the most part, with English comments included because they are ubiquitous
in the context of Indian social media.

The dataset was organized in the form of a spreadsheet, with each comment
identified by a unique comment code that would help the annotators distinguish
an independent comment from comments featuring in a thread, and each comment
posted under an article or video constituting one data instance, regardless of
its length, language, or content. In other words, a data instance can be a
single letter or an essay-length comment, can be written in a single language
or a combination of languages, and can contain text (in any script), numerals,
and emojis individually or all in one comment. The data instance is annotated
taking the entire comment as one single, compact unit.

### A.4. Documentation for Source Datasets

This dataset has been developed from a source dataset that marked only
aggressive speech collected from public Facebook and Twitter pages, and a
subsequent source dataset that developed the tagset to include speech with
aggression and gender bias, collected from Facebook, Twitter, and YouTube.

The links for the research papers published and the workshops conducted on the
respective source datasets are listed below:

  1. 1.

http://arxiv.org/abs/1803.09402

  2. 2.

Trac - 1666First Workshop on Trolling, Aggression and Cyberbullying,
https://aclanthology.org/W18-4401

  3. 3.

https://arxiv.org/abs/2003.07428

  4. 4.

Trac - 2777Second Workshop on Trolling, Aggression and Cyberbullying,
https://aclanthology.org/2020.trac-1.1

The current dataset was built on the foundation laid down by these source
datasets, and has added several new, finely-grained tags, including two
primary tags marking caste/class bias and ethnic/racial bias, and two
secondary tags that mark the discursive roles and discursive effects of
(overtly and covertly) aggressive speech.

### A.5. Language Varieties

The languages included in this dataset, listed with their respective BCP-47
language tags, include:

  * •

code unavailable: Meitei as spoken by the Meitei community in Manipur, India.

  * •

bn-IN and bn-BD: Bangla (and its varieties) as spoken in India and Bangladesh.

  * •

he-IN: Hindi (and its varieties) as spoken in various parts of India.

  * •

en-IN: English (and its varieties) as spoken in India, otherwise known as
Indian English.

Since this dataset has been exclusively collected from online sources, the
users writing the comments are assumed to be multilingual and may be based in
any part of the world, not just in the places these languages are primarily
spoken in. However, the language varieties used in the dataset are primarily
those mentioned in the list above.

### A.6. Speaker Demographic

This dataset has been sourced exclusively from the internet, hence the speaker
demographic of the dataset cannot be identified beyond the language they
speak. It is assumed that the speakers could be of any age, gender, sexual
orientation, educational background, nationality, caste, class, religion,
race, tribe, or ethnicity.

The speakers are probably multilingual as well, with the language they post in
being one of the many they would know or be fluent in. It is a safe assumption
to make that many of these comments are made by Indians (specifically people
who have Meitei, Bangla, and Hindi as their first or primary language) and
Bangladeshis given the nature and reach of the topics selected, but this
assumption is not backed by any data or statistical findings.

### A.7. Annotator Demographic

The annotation scheme and guidelines for this dataset has been developed by Dr
Ritesh Kumar, the principal investigator of the ComMA Project and a faculty at
the Department of Transdisciplinary Studies, Dr. Bhimrao Ambedkar University,
Agra, India. He was assisted by the co-PIs of the project - Dr. Bornini
Lahiri, Assistant Professor at IIT-Kharagpur and Dr. Atul Kr. Ojha and
Akanksha Bansal, co-founders of Panlingua Language Processing LLP - and
annotators of this dataset, who have been listed below. Further, these
annotators have manually identified the appropriate posts and videos to work
on, crawled the data, and then annotated and analysed the processed data in
their respective languages.

  * •

A 31-year-old Bengali Muslim woman working from Gangtok, Sikkim. She has a PhD
in English, speaks Bangla, Hindi, and English, and her ideological leanings
are centrist. She is annotating the Bangla data.

  * •

A 29-year-old Bengali Hindu man working from Malda, West Bengal. He has an MA
in Linguistics, and speaks Bangla, English, Hindi and Bhojpuri. He is
annotating the Bangla data.

  * •

A 33-year-old Bengali Hindu woman working from Kalyani, West Bengal. She has a
PhD in Linguistics, speaks English, Hindi, Bangla, and Sylheti, and her
ideological leanings are leftist. She is annotating the Bangla data.

  * •

A 30-year-old Meitei Hindu woman working from Imphal, Manipur. She is pursuing
a PhD in Linguistics, speaks English, Hindi, and Meitei, and her ideological
leanings are centrist. She is annotating the Meitei data.

  * •

A 28-year-old North Indian Hindu man working from Patna, Bihar. He is pursuing
a PhD in Linguistics, speaks English, Hindi, Magahi, and Bhojpuri, and his
ideological leanings are centrist. He is annotating the English and Hindi
data.

  * •

A 25-year-old North Indian Hindu man working from Agra, Uttar Pradesh. He is
pursuing an M.Phil in Linguistics, and speaks Braj, Hindi and English. He is
annotating the English and Hindi data.

  * •

A 27-year-old North Indian Hindu man working from Agra, Uttar Pradesh. He is
pursuing an M.Sc. in Computational Linguistics, speaks Hindi, Bhojpuri,and
English and his ideological leanings are centrist. He is annotating the
English and Hindi data.

  * •

A 32-year-old Punjabi Hindu man working from Agra, Uttar Pradesh. He is an MA
in Journalism and in Linguistics, speaks English, Hindi, and Punjabi, and his
ideological leanings are leftist. He is annotating the English and Hindi data.

### A.8. Speech Situation and Text Characteristics

This dataset comprises of online comments written by users of various social
media platforms. The comments collected range from 2012 to 2021 (and
continuing), and form part of an extensive and intensive social media
discourse.

  * •

Time and place of linguistic activity \- Online

  * •

Date(s) of data collection \- April to September 2021

  * •

Modality \- Written

  * •

Scripted/edited vs. spontaneous \- Spontaneous

  * •

Synchronous vs. asynchronous interaction \- Asynchronous (online comments)

  * •

Speakers’ intended audience \- Other users of the respective social media
platforms and channels

  * •

Genre \- Social media

  * •

Topic \- Socially or politically polarizing or controversial topics

  * •

Non-linguistic context \- The videos which provide the context for the
comments generated

  * •

Additional details about the cultural context \- The sociopolitical climate
and cultural context in which the commenters live have a huge influence on the
nature, tone, and ideological underpinnnings of the comments they write on
social media

### A.9. Preprocessing and Data Formatting

The preprocessing of the raw data involves deleting all duplicates of a data
instance, deleting data instances with urls and texts with less than three
words, and removing data instances which occur in languages apart from Meitei,
Bangla, Hindi, and English. In the Telegram data, all translations of texts
have been deleted manually. The data instances are listed without the names of
the commenters, but when someone has replied to a previous comment by tagging
them with the ’@’ symbol, that information is available to the annotator
within the text itself.

Next, the processed data is arranged on a Google spreadsheet, columns are made
with the relevant headings and tags (using the option for data validation),
and copies of the spreadsheet are shared amongst the annotators working on a
particular language so they can annotate the files individually and without
consultation with each other. This is to ensure that no annotator is
influenced in their annotation by the ideas of another. However, at no stage
in the process are the annotators anonymous to each other or anyone else in
the team.

### A.10. Capture Quality

As with any other dataset, we have faced quality issues in data capture. The
primary of these is the difficulty in finding every kind of data in every
language. For instance, in Bangla, it is very difficult to find racist or
communal data, because most conversations that we have come across in social
media platforms that are of a communal or racist nature and involves Bangla
speakers occur in English. Similar challenges have been faced in Meitei with
regard to casteist data, and in Hindi with regard to ethnically and racially
biased data. These discrepancies can be explained when the social, political,
and cultural contexts of each of these language and speech communities is
taken into consideration, which are significantly different from each other.

### A.11. Limitations

Following the point in the previous section, another limitation in the data is
the dearth of comments that can be tagged by the discursive effects of
counterspeech, abet and instigate, and gaslighting. In contrast, the
discursive effect of attack is very well-represented, not so closely followed
by defend. All of these factors combine to make it challenging for the dataset
in each language to be equally representative of each of the primary tags,
thus making it difficult for the researchers to embark on intensive
comparative analyses of the characteristics of each of these phenomena across
all of the languages being analysed.

This tagset also does not allow us the option to distinguish a personal attack
from an identity based one, to mark national/regional or political bias, and
to distinguish sexual harassment from aggression, sexual threat, and gender
bias. These are shortcomings that will have to be addressed and resolved in
subsequent versions of the tagset.

### A.12. Metadata

The relevant links to the metadata for this dataset have been provided below:

  * License: CC BY-NC-SA 4.0

  * Annotation Guidelines:

    1. 1.

http://arxiv.org/abs/1803.09402

    2. 2.

https://arxiv.org/abs/2003.07428

    3. 3.

https://drive.google.com/file/d/1ZUZxDaYIfotVur-
cJF30cfqIY1dSzJ6K/view?usp=sharing

  * Annotation Process: Manual annotation

  * Dataset Quality Metrics: Krippendorff’s Alpha for IAA

  * Errata: NA

### A.13. Disclosures and Ethical Review

This dataset has been funded by Facebook Research under Content Policy
Research Initiative Phase 2.

### A.14. Other

NA

### A.15. Glossary

NA