This document summarizes research on developing a method to detect potential bullies in online social networks. It outlines analyzing a dataset of over 9 million tweets to identify bullying messages and traits of individuals who send negative or cruel messages to others. Preliminary results found over 1,000 accounts that sent 4 or more negative messages to a specific user. Future work involves training machine learning classifiers to better identify bullying in a real-time manner.

1. “Application of Actor Level
Social Characteristic
Indicator Selection for the
Precursory Detection of
Bullies in Online Social
Networks”
April 19th
, 2016
Holly White, Jeremy Fields
Robert Hall, Joshua White

2. 2

Introduction

Background on Cyberbullying

Bullying Traits

Dataset Selection

Analysis
– Pre-processing
– Determining Negativity

Initial Results

Conclusion and Future Work

References / Contact Info.
Overview

3. 3

This work presents a precursory method for
detection of “potential” bullies in social
networks. We still have an unknown number of
false positives that we have yet to quantify.
Disclaimer

4. 4

Cyberbully occurs globally and impacts people of
all ages and walks of life
– Effects include depression, low self-esteem, physical
and psychological conditions and suicide (3,4)
– Early detection can mitigate effects

Cyberbullying occurs within a wide range of
technologies
– Analyzing social media for cases of cyberbullying
requires methods isolating key factors within large
samples of data
Introduction

5. 5

Cyberbullying effects both victims and offenders (8)
– victims are 1.9X more likely to attempt suicide
– Offenders are 1.5X more
– Attacks are personal & focus on sexuality, race,
intelligence, and appearance

Courts insist schools have a legal & moral
responsibility to take action
– 49 states prohibit cyberbullying (8,9)
– The Safe Schools Improvement Act will require all
schools prohibit bullying to acquire funding
– Dignity For All Students Act (NYS) (10)
Cyberbullying

6. 6
Cyberbullying

While there are numerous forms of cyberbullying, Chisolm created
the top 11
– Catfishing
– Use of MMOGs (Massive Multi-player Online Gaming-typical of males)
– Material or messages dehumanizing, attacking, or threatening the target
– Flaming (hostile & insulting)
– Impersonating
– Slamming (by-standers are crucial)
– Ratting (utilization of victim's hardware)
– Relational aggression (mean girl & by-standers)
– Sexting (large repercussions)
– Shock Trolling
– Online Stalking

7. 7

Aside from the chart below, cyberbullies are
primarily teenaged and female (9)

Gender aside, bullies typically have low
effective & cognitive empathy (13)

Bystanders play a major role (DASA)
Bullying Traits

8. 8

This work applies to many different social
networks

In 2010 the Department of Homeland Security
proposed classifications for social networks [17]

Twitter is Unique:
– Allows for non-accepted Follower Relationships
– 140 Character Limit
– Easy Access API
– * The Twitter Rules *
Dataset Selection

9. 9

Started with a series of political hashtags that
were collected as part of a previous research
project, researchers at SUNY Polytechnic
collected 9Million+ tweets from the trickler API.
Dataset Selection

This dataset is
available upon
request in full or
summarized form,
under a data sharing
agreement. A
complete summation
of the dataset is also
available in report
form.

10. 10

Build a process that can be highly parralellized
using mapreduce

Reduce the dataset through various, easy to
compute mechanisms

Eliminate as much “noise” as possible

High confidence selection of bullying messages
Analysis Goals

11. 11

Removal of Bots/SPAM/etc.

Plot (Entropy over Time)
– Previous work showed messages scoring under
4.9 to be bots/SPAM 99% of the time
– 325,396 messages removed from 189,263
accounts.
Analysis [Entropy]

12. 12

Analyzed 2 groups (Male, Female)
– Assumption: Males more negatively polarized,
• A. Sifferlin stated that men share more negative
emotion online than women [21]
• Our Findings:
– (All Messages - Male Negative: 17.048%)
– (All Messages - Female Negative 14.742%)
– Difference (2.306%)
Male Female
Analysis [Polarity]

13. 13

Next we analyzed negative messages directed
at another user
– Twitter denotes directed messages as
@username
– We found 725,572 messages were directed
– Within this data we found a much smaller
gender difference (0.55%)
Analysis Cont.

14. 14

We have not built the classifiers (discussed in
future work)
– Instead we set an arbitrary threshold for
selection of potential bully accounts
• Manual analysis shows accounts containing more
than 4 negative messages directed at a particular
user was a good threshold choice
• 1,035 individuals fell into the 4 or more category
• Sample messages from 1 such account are
shown:
Initial Results

15. 15

Goal: Create a variable “real-time” threshold by
training two probability based machine learning
classifiers:
– 1.) Assess negativity and “cruelty” of messages
along with demographics of an individual
compared to users of the same demographics
– 2.) Compare the overall negativity and “cruelty”
of an individual, to the amount of negativity and
“cruelty” shown to a specific user
Future Work / Next Steps

16. 16

Despite our arbitrary threshold, our method
located bullying within the dataset

This method shows potential as a tool to
combat cyberbullying

We aim to enhance this capability

Planned future work will also create more “real-
time” recognition of cyberbullying

Future work with Rutgers University under NSF
grant is underway
Conclusion

17. 17
Contact:
Holly M. White
hwhite@cvalleycsd.org
Citations / Contact