Twitter Users Behavioral Response to Toxic Replies

2025-05-06 0 0 1.61MB 11 页 10玖币

侵权投诉

Twier Users’ Behavioral Response to Toxic Replies

Ana Aleksandric

University of Texas at Arlington

Arlington, TX, United States

Sayak Saha Roy

University of Texas at Arlington

Arlington, TX, United States

Shirin Nilizadeh

University of Texas at Arlington

Arlington, TX, United States

ABSTRACT

Online toxic attacks, such as harassment, trolling, and hate speech

have been linked to an increase in oine violence and negative

psychological eects on victims. In this paper, we studied the im-

pact of toxicity on users’ online behavior. We collected a sample

of 79.8k Twitter conversations. Then, through a longitudinal study,

for nine weeks, we tracked and compared the behavioral reactions

of authors, who were toxicity victims, with those who were not.

We found that toxicity victims show a combination of the following

behavioral reactions: avoidance,revenge,countermeasures, and nego-

tiation. We performed statistical tests to understand the signicance

of the contribution of toxic replies toward user behaviors while

considering confounding factors, such as the structure of conversa-

tions and the user accounts’ visibility, identiability, and activity

level. Interestingly, we found that compared to other random au-

thors, victims are more likely to engage in conversations, reply in

a toxic way, and unfollow toxicity instigators. Even if the toxicity

is directed at other participants, the root authors are more likely

to engage in the conversations and reply in a toxic way. However,

victims who have veried accounts are less likely to participate

in conversations or respond by posting toxic comments. In addi-

tion, replies are more likely to be removed in conversations with a

larger percentage of toxic nested replies and toxic replies directed

at other users. Our results can assist further studies in developing

more eective detection and intervention methods for reducing the

negative consequences of toxicity on social media.

CCS CONCEPTS

•Social and professional topics →User characteristics.

KEYWORDS

social media, toxicity attacks, user online behavior, longitudinal

study

ACM Reference Format:

Ana Aleksandric, Sayak Saha Roy, and Shirin Nilizadeh. 2023. Twitter Users’

Behavioral Response to Toxic Replies. In Proceedings of The Web Confer-

ence(WWW) ’23 (The Web Conference (WWW) ’23). ACM, New York, NY,

USA, 11 pages. https://doi.org/XXXXXXX.XXXXXXX

Permission to make digital or hard copies of all or part of this work for personal or

classroom use is granted without fee provided that copies are not made or distributed

for prot or commercial advantage and that copies bear this notice and the full citation

on the rst page. Copyrights for components of this work owned by others than ACM

must be honored. Abstracting with credit is permitted. To copy otherwise, or republish,

to post on servers or to redistribute to lists, requires prior specic permission and/or a

fee. Request permissions from permissions@acm.org.

The Web Conference (WWW) ’23, April 30–May 4, 2023, Austin, Texas

ACM ISBN 978-x-xxxx-xxxx-x/YY/MM. .. $15.00

https://doi.org/XXXXXXX.XXXXXXX

1 INTRODUCTION

These days, social media is rampant with toxic attacks such as of-

fensive language, trolling, and using hate speech. The primary goal

of these attacks is to silence, insult or demoralize people, especially

those belonging to already marginalized groups [

]. These

attacks are usually targeted, e.g., as part of a smear campaign to dam-

age or call into question someone’s reputation [

They can also be coordinated using other communication mediums

and implemented by many users [8, 12, 56, 70].

Psychological research has studied the negative eects of online

harassment, cyberbullying, and trolling on individuals’ psycho-

logical states and well-being [

], showing that they often

cause overwhelming and stressful situations for the victims [

]. These studies found that victims are more prone to show

self-harming behaviors as well as suer from depression and anxi-

ety [

]. To counter online toxic attacks, many social media

platforms have started implementing content moderation mecha-

nisms [

] that block accounts [

] and remove content [

However, it is debatable whether they provide sucient mitigation

to the potential psychological damage caused to the victim as a

result of the online toxicity [24, 34].

To the best of our knowledge, no work has conducted a longitu-

dinal data-driven study to examine the impact of toxic content on

victims’ online behavior. In this paper, we study how the victims

respond to toxic content in terms of their behavioral actions on

Twitter. Our goal is to identify key factors which accurately repre-

sent the scale at which toxic replies impact victims. A recent study

on online cyberbullying [

] targeting undergraduate and high

school students found that victims primarily engaged in four types

of behavioral reactions towards such attacks: avoidance,revenge,

employ countermeasures, and negotiation. We use this as a frame-

work for creating meaningful groupings of behavioral responses

to toxic content. For example, we examine if the victims try to

avoid further encounters with toxic content, e.g., by removing their

posts or even deleting their accounts (which are also signs of being

silenced), or if they tend to negotiate by posting comments in con-

versations, and even take revenge by responding in a toxic way, or

employ countermeasures by ignoring such content but unfollowing

the toxicity instigators. In our analysis, we consider factors that

might have an impact on users’ social media behavior, including

the number of toxic replies in conversations, the structure of con-

versations, the location of the toxic content in the conversations

tree, and the social relationship between conversations’ partici-

pants (i.e., if they follow each other). We also explore the eects

of account-specic attributes on users’ decisions, including their

online visibility, identiability, and activity level.

For our analysis, we collected a random sample of 79.8k Twitter

conversations from August 14th to September 28th, 2021. We used

this data to identify users involved in these conversations, identify

conversations with toxic replies, and nally collect longitudinal

arXiv:2210.13420v1 [cs.SI] 24 Oct 2022

The Web Conference (WWW) ’23, April 30–May 4, 2023, Austin, Texas Ana Aleksandric, Sayak Saha Roy, and Shirin Nilizadeh

data on users’ online behavior. We represented the structure of a

conversation using a reply tree, which encodes the relationships be-

tween tweets, where two tweets are connected if one is a reply to the

other. We used Google Perspective [

], a natural language-based

AI tool used to identify toxicity in text, to detect conversations that

received toxic replies. Finally, we analyzed and characterized the

behavioral reactions of root authors receiving toxic replies, who

we call toxicity victims, and compared their behavior with those of

root authors receiving no toxic replies, who we call random authors

and are our control group. We formulated the following hypotheses

and performed appropriate statistical tests to understand the sig-

nicance of the contribution of toxic replies toward user behaviors:

: Toxicity victims are more likely to deactivate their accounts

compared to random authors.

: Toxicity victims are more likely

to switch their accounts to private mode.

: Toxicity victims are

more likely to engage in conversations.

: Toxicity victims are

more likely to engage in conversations if receiving toxic replies

from a larger number of toxicity instigators.

: Toxicity victims

are more likely to respond back in a toxic way.

: Toxicity victims

are more likely to respond back in a toxic way if receiving toxic

replies from a larger number of toxicity instigators.

: Toxicity

victims are more likely to delete their original posts compared to

random authors.

: Replies in conversations with toxic replies are

more likely to be deleted compared to conversations without toxic

replies.

: Toxicity victims are more likely to unfollow toxicity

instigators compared to random authors.

Our study yields multiple important ndings. We observed that

dierent users respond dierently to toxic content and identied

groups of users who show similar reaction patterns. For example,

in terms of disregard and avoidance, we demonstrate that 30.96% of

victims ignored toxic content and did not show any reactions. In

terms of negotiation, we found toxicity victims compared to others

are more likely to engage in the conversations (60.3% vs. 47.4%),

and 12.4% of victims employed countermeasures by unfollowing

toxicity instigators. Analyzing the contributing factors to users’

behavior, our results suggest that users who receive toxic direct

replies are less likely to engage in the conversation, while users

who receive nested toxic replies engage more in conversations with

toxic replies and tend to have toxic responses. These show that

the location of toxic content in the conversation can aect victim’s

reactions. Interestingly, veried accounts are less likely to engage

in conversations with toxic replies, indicating that the social status

of users can have an impact on how they perceive toxic content

and react to them. Finally, identiable accounts are more likely to

engage in conversations by responding in a toxic way.

2 RELATED WORK

Generalized online abuse encompasses several unhealthy online

behaviors, including attacks of racism against minority communi-

ties [

], misogynistic hatred [

] and toxic masculin-

ity [

], which are aimed at groups of vulnerable individuals. In

recent years, online abuse have received a lot of attention. Here,

we focus on studies that characterize online abuse and investigate

its psychological impact of them on humans. To the best of our

knowledge, there is no work that has studied the consequences of

receiving toxic replies on users’ online behavior.

Prevalence of Social Media Victimization.

Since abuse and

toxicity are often carried out with the intention to humiliate or ma-

nipulate targeted individuals [

], social media is often

considered to be the chief outlet for housing such attacks due to

high visibility [

] and more opportunities to remain anony-

mous [

]. Online abuse and toxicity can be inuenced by

several socioeconomic factors. Prior work has examined the as-

sociation between abuse and on-the-ground “trigger” events, e.g.,

terrorist attacks, and political events [

]. The prevalence

and characteristics of hate speech have also been studied on spe-

cic web communities, such as r/Gab [

], 4chan’s Politically

Incorrect board (/pol/) [

], Twitter [

], and Whisper [

Some works have shown that online abuse and toxic comments are

normalized in several communities [

]. There have also been

a few eorts to understand the characteristic dierences between

hate targets and hate instigators [14, 15, 37, 50].

The Psychological Impacts of Online Abuse.

Established lit-

erature on the psychological impacts of online abuse is mostly

focused on cyberbullying. A study determined that middle and high

school students who had been cyber-bullied were more prone to

exhibit self-harming behavior as well as suer from depression,

anxiety, and lower self esteem [

]. Similar studies [

]

found the same patterns of psychological distress in young adoles-

cents [

] and adults [

], due to cyberbullying attacks. Victims

of online harassment might also respond by acceptance and self-

blame [

], with those having lower psychological endurance being

more vulnerable to emotional outbursts. Some studies explored the

eects of trolling on victims [

], and identied the circumstances

when victims are more likely to respond to trolls [55].

3 DATA COLLECTION

Figure 1 shows the pipeline used for collecting and processing our

datasets. Broadly, we aimed to (a) obtain a random set of conversa-

tions, and (b) track the twitter activities of root authors.

Daily Collection of a Random Sample of Twitter Conver-

sations:

We used the Twitter API [

] to collect a 1% random

sample of tweets for 46 consecutive days, starting from August

14th till September 28th, 2021, and extracted English tweets which

are not retweets or replies belonging to other conversations. For

each initial tweet, we waited at least two days before collecting the

replies of the conversation. For example, if we collected a random

sample of tweets on September 1st, we would start collecting replies

for each of these tweets on September 3rd. This is to give enough

time so that the initial tweet can turn into a conversation. However,

we discarded many tweets that did not receive any comment or had

been deleted by the time we attempted to collect their replies. We

also removed the conversations where the replies for the tweets

were all posted by the author of the initial tweet.

Conversations, Reply Trees and Some Denitions:

We used

Twarc [

], a Python wrapper for the ocial Twitter API to obtain

the entire conversation for each initial tweet, including its direct

replies and nested replies (replies to replies). As it is shown in

Figure 2, we represent each conversation as a reply tree, where one

tweet is child of another tweet when one is a reply to the other. The

initial tweets represent the roots of reply trees. We call the author

of the root tweet as root author. We also dene direct replies as the

Twier Users’ Behavioral Response to Toxic Replies The Web Conference (WWW) ’23, April 30–May 4, 2023, Austin, Texas

For 46 days:

(1) Obtain a random

sample of tweets

(3) Collect authors’

friend & follower lists

(2) Remove retweets,

replies, &

non-English posts

Day 1 Day 2

From day 2, everyday

check:

(1) If tweets exist

(2) If authors’

accounts are

deactivated or

private

Day 3

(1) Collect replies

and replies to replies

to tweets obtained

in the first day

(2) Discard

conversations

without replies or

other users

Day 49

At the end of data

collection

(1) Collect authors’

friend & follower

lists for the second

time

(2) Use Perspective

API to detect toxicity

of conversations

From day 4, everyday

check:

(1) If replies exist

Day 4

Figure 1: Data collection process

rst level replies in a conversation, which is the set of replies to

the root tweet. Nested replies refer to other levels of replies in a

conversation other than the rst layer, i.e., replies to replies.

As it is shown in Figure 2, each conversation as a reply tree has

the following properties: Size, which indicates the number of tweets

in the conversation; Depth, which is the depth of the conversation’s

deepest node; Width, which is the maximum number of nodes at

any depth in the tree.

Pre-processing and Filtering:

Getting the replies for tweets

of each day can take a long time, up to couple of hours. Therefore,

the rst tweets collected for the certain day would have much

less time to receive any comments than last tweets obtained from

that day. In order to solve this problem, we only kept replies sent

in the rst 48 hours after the root tweet was posted. Also, some

tweets contained only links, images, and videos instead of text.

Since our approach of detecting toxic tweets is text-based, such

conversations were removed from the dataset. Moreover, certain

reply trees were missing some replies due to the errors received

during their data collection. Since these errors can have multiple

reasons including, replies being deleted or hidden by their authors,

or root authors, we removed trees containing such errors from

our dataset. Furthermore, we noticed that not all the root authors

in our sample are unique, therefore to avoid duplication in our

statistical analysis, we randomly selected a single conversation

from each root author.

Our Conversation Dataset:

Finally, our

dataset consists of 79,799 conversations with 528,041 tweets, posted

by 328,390 unique users, out of which 79,799 are root authors.

Tracking activity of root authors:

For each root author and

root tweet, we kept track of the following activities: account deac-

tivation,account privatization, and tweet deletion, as well as root

authors’ lists of followers and friends. In detail, as it is shown in

Figure 1, from the second day of our data collection, we checked on

a daily basis if the root tweets and authors still exist using Twitter

API. Similarly, from the fourth day of our data collection, after

collecting the replies to a root tweet, every day we were checking

if the replies still exist. When a certain tweet or account is not

accessible, API returns helpful error codes, e.g., Sorry, you are not

authorized to see this status,User not found, and No status found with

that ID [

]. These error codes indicate that the user account has

become private, been deactivated and the tweet has been deleted,

respectively. Note that we cannot certainly identify who deleted

a particular tweet. For instance, the root tweet can be deleted by

the root author, or in case the deleted tweet is a comment, it can

be deleted by the user who posted that specic comment or by the

root author. Even though this process was repeated daily during the

entire process of data collection, the data collection failed on some

days. More precisely, in the analysis, we focus on the presence of

the tweets and accounts three days after the corresponding root

tweets were collected, but the data about their presence is miss-

ing in 7.6% of the conversations, with very similar distributions

in conversations with and without toxic replies. Therefore, in the

statistical models related to tweet deletion, account deactivation,

and account privatization, we omit these conversations.

As illustrated in Figure 1, we collected the followers and friends

lists of all root authors every day, once the daily random sample is

obtained. Thus, the lists collected at this time represent a snapshot of

the authors’ friends/followers lists before their tweets turned into a

conversation. Furthermore, around 49 days after the rst day when

root tweets were collected, we again collected the list of friends

and followers for all root authors. This is to analyze the impact of

toxicity on users’ online relationships. Also, note that in the analysis

we report the distribution of the percent of toxicity instigators that

the victims unfollowed. As toxicity instigators do not exist in other

conversations, comparing the unfollowing ratio between victims

and other random users was not applicable. We could not collect the

relationships, i.e., follower and friends’ lists, right after obtaining

the conversations, because of the number of API calls we could

issue every day. This delay in collecting relationships imposes some

limitations as users might end followership and friendships due

to other events during this time. However, in our analysis, we

compare these variables of toxicity victims and random authors

and seeing a dierence can be an indicator of the impact of toxicity

on Twitter relationships. From our unfriend analysis, we had to

discard 2,488 conversations, where 403 and 2,085 are conversations

with and without toxic replies respectively because we were not

able to obtain the friends’ list for their root authors due to the

authors either making their accounts private or deactivating them.

Identifying Conversations with Toxic Replies:

We used Google’s

Perspective API [

] to detect toxic replies in our dataset. This AI-

based tool investigates whether a provided text contains language

indicating abusive or inappropriate attributes such as Severe tox-

icity,Profanity,Sexually expletives,Threats,Insults and assigns a

文档加载中……请稍候！
如果长时间未打开，您也可以点击刷新试试。

下载文档到电脑，查找使用更方便

10 玖币 0人已下载

立即下载

摘要：

TwitterUsers’BehavioralResponsetoToxicRepliesAnaAleksandricUniversityofTexasatArlingtonArlington,TX,UnitedStatesSayakSahaRoyUniversityofTexasatArlingtonArlington,TX,UnitedStatesShirinNilizadehUniversityofTexasatArlingtonArlington,TX,UnitedStatesABSTRACTOnlinetoxicattacks,suchasharassment,trolling,an...

展开>> 收起<<

Twitter Users Behavioral Response to Toxic Replies.pdf

共11页,预览3页

还剩页未读，继续阅读

声明：本站为文档C2C交易模式，即用户上传的文档直接被用户下载，本站只是中间服务平台，本站所有文档下载所得的收益归上传人(含作者)所有。玖贝云文库仅提供信息存储空间，仅对用户上传内容的表现方式做保护处理，对上载内容本身不做任何修改或编辑。若文档所含内容侵犯了您的版权或隐私，请立即通知玖贝云文库，我们立即给予删除！

Twitter Users Behavioral Response to Toxic Replies

相关推荐

开通VIP享超值会员特权

作者详情

相关内容

热门标签

举报选择: