Open Source Social Media Analytics for Intelligence and ... · Social Media Websites API Wikipedia Social Curation (Theme or topic based content sharing) Crowd Sourcing Websites Examples-

Open Source Social Media Analytics for Intelligence

and Security Informatics Applications

Invited Talk at 4th International Big Data Analytics Conference (BDA),

Hyderabad, India 2015

December 17, 2015

Swati AgarwalPhD Scholar at Information

Management and Data Analytics Group

IIIT Delhi, India

([email protected])

Ashish SurekaPrincipal Scientist at ABB Corporate

Research Center

Bangalore, India

([email protected])

Vikram GoyalAssociate Professor at IndraprasthaInstitute of Information Technology

New Delhi, India

([email protected])

Learning Objective

Agarwal S., Sureka A., Goyal V. - Invited Talk @ 4th International Big Data Analytics Conference, Hyderabad, India (December 17, 2015)

What

Why

How

• Open source social media Intelligence

• Focus of Tutorial (Sub-topics under the field of ISI Research)

• Need and Importance of research in the field of ISI

• Technical and Computational Challenges

• Case Studies showing the applications of CSR in the domain of ISI

• Future Directions

Learning Objective


Remember

Understand

Application

Research

keywords and terminology

What is Intelligence and Security Informatics

Need and Importance of research in the field of ISI

Current state-of-the art and scope of research in the domain

What ideas of computer science

research you can apply in ISI

domain

Applying

CSR

methods for

solving the

problems

raised by ISI

Tutorial Structure


Session I

Introduction to the field of Intelligence and Security Informatics

Need and Scope of Research in the field

Uniqueness of Problem/Area (Technical Challenges)

Session II

Technology Landscape and Current state-of-the-art

Case studies on open source social media intelligence applications in ISI

Leading and relevant venues for publication in the domain of ISI

Agenda

Importance of ISI

Introduction

Technical Challenges

Focus and Scope

Technology Landscape

Case Studies

ISI Leading Venues

Conclusions

Future Directions

References


Importance of Intelligence

and Security Informatics


TerrorismAn illegal use of violence, aimed against civilians in

order to achieve different kind of political ends.


Secret Intelligence

Service (MI6) in United Kingdom

Started after 9/11 attacks in United States

FBI, Nation’s Prime Federal

Law Enforcement Organization

Research and Analysis Wing(RAW), Started

after Sino-Indian and

Indo-Pakistan War


Image Sources: Wikipedia


Image Sources: RajComics, MarvelComics, Wikipedia

Intelligence and Security Informatics

Technologist

SociologistPsychologist

How Computer Science Research can be used

for border control, terrorism, crime prevention on

Cyber-Security


““The medium is the message”.

-Marshall McLuhan

Role of Social Media





Role of Social Media

Al-Qaida encourages “homegrown terrorism” and

recruitment of young people

Information shared on social media has access to

more individuals to carry out “lone wolf” operations

against Western targets

42% of teenagers (15-17 yrs) in United States use

social networking websites.


50M tweets per day, 640M users on Twitter

100 hours of videos uploaded on YouTube

every minute

124.3B posts, 264.6M blogs on Tumblr

Popular Social Media Websites

Statistics

>75M daily active users, 40B photos uploaded

on Instagram

For any government organization, law

enforcement agency or security analysts-

constant monitoring and manual

annotation of each post made on social

media portals is overwhelmingly

impractical



3 men from Mumbai, Maharashtra join

ISIS in Iraq (age group 20-25)

Jake Bilardi, manipulated, radicalized,

Jihadist (18 yrs. old, 10th std. student)

Image Sources:

1. http://indiatoday.intoday.in/story/agencies-on-the-tail-of-

maharashtras-missing-jihadis/1/372442.html,

2. http://www.news.com.au/national/freshfaced-westerners-

are-being-lulled-into-terrorism-by-isis-propaganda/news-

story/8448148e3a0c33c01b95db4dc1bab492,

3. http://www.ibtimes.com/who-ali-shukri-amin-virginia-isis-

teenager-behind-pro-islamic-state-twitter-sentenced-

2073208

Ali Shukri Amin, (17 yrs. old) from

Virginia, sentenced to 11 yrs. of prison

for raising funds for ISIS through Twitter

@AmeerkiWitness

Introduction


“ Intelligence collected and inferred form

publicly available and overt sources of

information

Open Source Intelligence


Infochimps

Social Media Websites API

Examples- Open Source

Data Portals


Image Sources: Website Developer Page, Official Websites of Data Portals

“ A sub-field within OSINT with a focus on

extracting insights from publicly available

data in Web 2.0 platforms

Open Source Social Media

Intelligence


Social Media Websites API

Wikipedia

Social Curation (Theme or topic based

content sharing)

Crowd Sourcing Websites

Examples- Web 2.0 Open

Source Data Portals


Image Sources: Website Developer Page, Official Websites of Data Portals


“ Current generation of the web

Highly participative and collaborative

Allows users to post content, messages and

comments

Online Social Media


Online Social MediaPopularity

Anonymity

High Reachability

Social Networking

Low Publication Barriers


Image Sources: Google Images, http://maliaholleron.com/wp-content/uploads/2012/12/Social-Media-Icons-cloud-300x256.png

Video Sharing and

Hosting Website

Social Networking

Website

Micro-Blogging

Websites

Community Based

Questions and Answering

Social

Bookmarking


Image Sources: Official Websites

“ A field of study concerning investigation and

development of counter terrorism, national

and international security support systems

and applications

Intelligence &

Security Informatics



Online Radicalization

Image Sources: http://www.trbimg.com/img-5178e6b1/turbine/la-na-tt-internet-imams-20130425-001/600

“Online Radicalization

Hate and extremism promotion

Promoting certain ideology and beliefs

Forming virtual communities online

Recruiting members for hate promotion

Brainwashing people affected from several

incidents


boko$haram,$islam,$muslim,$radical$islam,$terrorist,$jihad,$isis,$liberals,$al$queda,$holy$quran,$je$suis$charlie,$Islamic$Jihad,$Israel,$ak47,$guns,$rifle,$hate,$an<=American,$violence,$ba@le,$weapon,$religion$of$peace$

Tags/Keywords-associated-with-the-post-



Image Sources: tumblr.com



Swarmcast: How Jihadist Networks Maintain a Persistent Online Presence- Ali Fisher (Perspectives on Terrorism, June 2015)

Image Sources: twitter.com


Example- Constructing

Communities

A Focused Crawler for Mining Hate and Extremism Promoting

Users and Communities on YouTube Swati Agrawal, Ashish Sureka

Indraprastha Institute of Information Technology, Delhi (IIIT-D)

{swatia, ashish}@iiitd.ac.in

!

!

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

Image Sources: Google Images,

antitrustlair.files

“ Planning and mobilizing of civil unrest related

events via web platforms

Examples: Protests, Public Demonstrations and

Riots

Online Civil Disobedience



Online Civil Disobedience


“ Machine Learning Techniques

Social Network Analysis

Visualization

Text Mining and Analytics


Technical Challenges

50M tweets per day, 640M users on Twitter

100 hours of videos uploaded on YouTube

every minute

124.3B posts, 264.6M blogs on Tumblr


1. Massive Size and High

Velocity

>75M daily active users, 40B photos uploaded

on Instagram

2. Rich User Interaction on

Social Media Websites

Following, Follower, Re-tweet, Favorite, Share

Like, Comment, Share, Subscriber, Subscription, Friend, Featured Channel

Ask, Follower, Follower, Like, Re-blog, Share, Submit, Reply


3. Multilingualism



4. Noisy Content

Presence of low quality messages and content of

low relevance



4. Noisy Content

Grammatical and spelling errors



4. Noisy Content

Presence of non-standard acronyms and

abbreviations



4. Noisy Content

Use of emoticons and incorrect capitalization-

informal nature of content



5. Data Annotation and Ground

Truth

Need/Importance

Effort Intensive

Imbalance Data

• basis for several machine learning algorithms- examining the performance

• Vast amount of data being uploaded on social media dataset in every second

• 1 in 100,000 posts is hate promoting or posted by extremist users


6. Manipulation, Fabrication

and Adversarial Behavior

Fake information





Rumors





Manipulative/Misleading Information

Commonly seen in videos

Textual

• Title

• Tags

• Description

Media

• Thumbnail

• Annotation




Image Sources: youtube.com


Focus and Scope


Focus of Tutorial

Exploring two sub-problems within the broader area of

Intelligence and Security Informatics:

1. Online Radicalization Detection (presence of

extremist content, users and communities on

social media websites)

2. Online Civil Unrest Prediction (an early

prediction of civil unrest related events using

social media content)


Technology Landscape



• KNN

• SVM

• Naïve Bayes

• Boosting

• Logistic

Regression

• Topical Crawler

• Decision Tree

• EDA

• SVM

• OSLOM

• Rocchio

• Naïve Bayes

• Keyword Based

Flagging

• Honeypots

• Face Recognition

• Rule Based Classifier

• Regularized Least

Square

• TC

• EDA

• BFS

• DFS Link

Analysis

• Clustering-

Blog Spider

• Topical

Crawler (TC)

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

• Clustering-

Blog Spider

• Exploratory

Data Analysis

(EDA)

• TREC

• EDA

• Support Vector

Machine (SVM)

• Blockmodeling

• Multi-dimensional

Scaling

• Spring Embedder

• SVM

• Naïve Bayes

• Adaboost

• EDA

• Naïve Bayes

• OSLOM

• N-gram

• KNN

• SVM

• Best First

Search

• Shark Search

• Language

Modeling

Content Identification

User and Community Identification

Online Civil Unrest Prediction


• Stochastic

Hybrid Dynamic

Model

• Avatar

ensembles of

decision trees

Colb

au

gh

et.

Al.

Hu

a e

t. A

l.

• Clustering

Algorithm Nare

n e

t.

Al. • Map Reduce

• Apache Pig

• Naïve Bayes

• Logistic Regression

• Maximum Likelihood

• Generalized Linear

Model

• Dynamic Query

Expansion Jie

jun

Xu

et.

Al.

Ch

en

et.

Al.

• Heterogeneous

Graph Modeling

Com

pto

n e

t. A

l.

• Logistic

Regression Filch

en

ko

v e

t. A

l.

• Mathematical and

Theoretical Model

Sath

appan

et.

Al.

• Probabilistic

Soft Logic

Case Studies

1. Identification of Online

Extremist Content, Users and

Hidden Communities on YouTube

S. Agarwal and A. Sureka, A Focused Crawler for Mining Hate and Extremism Promoting Users, Videos and Communities on YouTube, 25th ACM Conference on Hypertext and

Social Media (HT 2014) 1-4 Sep 2014, Santiago, Chile

YouTubeLargest and most popular free video hosting and

sharing website- Found in 2005






“ Mining user generated content on social

media platforms to identify topic based hate

promoting content and locating hidden &

virtual communities of extremist users.

Research Problem


Research Contributions


Application of focused crawler for identifying

extremist content and locating hate

promoting users on YouTube Best First Search

Shark Search

Content based characterization of hate

promoting videos Focus of the content shown in video

Targeted audiences

Keywords present in the contextual metadata and spoken in

the video

Focused/Topical Crawler


Source: Dongyang Hou et. al; 2014


1. Subscriber

2. Featured Channel

3. Public Contact


>th

0.55

0.850.35

{Subscriber, Featured Channel, Public Contact}

0.25

0.32

Threshold: 0.30

0.32

Proposed Framework


Titles'of'Videos:'



Experimental Setup Discriminatory Features Set:

YouTube Profile Summary

User Activity Feeds (Title of Videos Uploaded, Shared, Commented

and Favorited)

Training Data: Discriminatory Features for 35 Hate Promoting Channel

(HinduismIslam, IndiaEternal, ISIcyberAGENT etc.)

Dynamic Parameters: 10 different YouTube Channels as Seeds (PakistanRoxxx,

hiddenpakistani, GreaterPakistan etc.)

Character N-Gram (3, 5)

Threshold Value for Relevance Computation (-2.0, -2.5, -3.0)

Focused Crawler: Best First Search and Shark Search


Experimental Results-

Focused Crawler


Best First Search

Approach

Predicted

Relevant Irrelevant

Actual

Relevant 921 314

Irrelevant 125 67

Shark Search

Approach

Predicted

Relevant Irrelevant

Actual

Relevant 991 295

Irrelevant 55 29

TPR OR

RecallTNR

PPV OR

PrecisionNPV F1-Score Accuracy

BFS 0.75 0.35 0.88 0.18 0.81 0.69

SSA 0.77 0.35 0.95 0.09 0.85 0.74

Confusion Matrix for Best First Search and Shark Search Algorithm (60 iterations each)

Accuracy Results for Best First Search and Shark Search Algorithm (60 iterations each)

Social Network Analysis


Community Graph of YouTube Users After Applying Focused Crawlers: Best First Search

(Left), Shark Search (Right)


Videos YT CategoryAvg. Length

(Sec)

Content

FocusTarget Audience Keywords

43News, Non-

profit151.68

Honor

Killing,

Harassment

Women, Refugee

People, Children

Child Marriage, Rape,

Responsibility, Protest,

Women, Asylum, Arrested,

Brutal, Slave

93

News, Auto-

Vehicle,

Politics

2526.16Islam

Promotion

Jewish, Muslim

People

Taliban, Bombs, Battle,

Courage, Allah, Islam,

Courage, Belief, Macca,

Money, Shaheed, Enemies

Of Islam

30Entertainme

nt319.61 Anti-India India Haters

Kashmir, Poverty, Liberate,

Hindu, Beggars, Pundit,

Untouchable, Extremism,

Attack, Killed, Anti-Muslim,

Anti-Pakistani, Hatred,

Masks, Freedom.

Examples- Content Based

Characterization of Videos


Videos YT CategoryAvg. Length

(Sec)

Content

FocusTarget Audience Keywords

25

News,

Politics &

Education

1225.56Liberate

KashmirKashmiri People

Muslim, Army, Military,

1947, Partition, Azad

Kashmir, Liber- ate

Kashmir, Pakistan, India,

Killing, Murder, Border,

Fighting, Democracy,

Martyr, Torture.

83News &

Politics349.28

Anti-

MuslimsPakistan Haters

Kashmir, Jihad, Pakistan,

India, Quran, Muslim,

Hindu, Qatil, Zakir Naik,

Hate Speech, Masjid,

Pandit, Defence, Madarsa,

Tribute, Bharat, America,

Attack, Napak, Holy,

Kabba,

Examples- Content Based


Content Based



Type of Video Content: Speech, News Segments,

Drawing, Interviews, Group Discussion, Animated

Videos, Lectures, Cartoon and Comics, Debate,

Recorded Videos, Textual Messages, Pictures with

Background Music

2. Early Prediction of

Civil Unrest Related

Events on Twitter

TwitterLargest and most popular free micro-blogging and

social networking website- Found in 2006


An expression of a moment or idea

Text, Photos, Videos, Location, Poll, External URLs, hashtag, @

Maximum of 140 character lengthTweet


Comment on a Tweet and join the conversation

Share a Tweet with your followers and add your thoughts before sharing

Favorite a Tweet and let author to know that you like their post

Assigning a topic to the Tweet using ‘#’ and make Tweet easily searchable

Follow other micro-bloggers to receive their Tweets in your Newsfeeds

Activities


“Research Problem


A characterization study on open source

Twitter dataset to investigate the feasibility of

building event forecasting model

Evaluating the performance of machine

learning and statistical based forecasting

model on real world data

Research Problem


• location l where the protest is going to happenSpatial

• time expression ti (day, date and time) of the protestTemporal

• what the protest is about- to - root cause of the protestTopic


Research Contributions


A content based characterization and semantic

enrichment on raw tweets to classify “crowd-buzz &

commentary” and “mobilization & planning”

microposts

Investigating the application of trend analysis

(captured along the sliding window) for event

forecasting.

Proposed Framework


Learning(Models:(

Named(En2ty(Recogni2on:(• • •

WebObservatory: https://web-001.ecs.soton.ac.uk/wo/dataset

Experimental Setup Dataset

‘Immigration’ tweets dataset downloaded from Southampton University

2M tweets (October 1, 2013 to February 28, 2014)

Collection of tweets containing terms ‘immigration’, ‘migration’,

‘immigrant’, ‘migrant’

Civil Unrest Related Events FastForFamilies in National Mall, USA

Christmas Island Hunger Strike, Australia

Sliding Window 7 days

For 16th January event (9 to 15 January)


Available Locations of User Profiles for Christmas Island Hunger Strike Event


Lead Indicator Classifier

Crowd-Buzz and Commentary

Planning and Mobilization



Event Forecasting Model

Pairs of named entities: (ti, to), (to, l)

and (l, ti)

Extract all expressions of x entity that

are θ frequent

Extract all expressions of paired entity

y that are Ψ frequent for at least one x


US#AU#

Right#of#Asylum#

Migrant#Worker#

Refugee#

EU#


Experimental Results


Crowd-Buzz

Classifier

Predicted

C&C NA

Actual

C&C 719 109

NA 137 82007

Mobilization and

Planning

Predicted

M&P_E M&P_G NA

Actual

M&P_E 127 6 9

M&P_G 7 1236 22

NA 13 18 81541

Precision Recall F1-Score

C&C 0.84 0.86 0.85

M&P

0.89 0.81 0.85

0.96 0.98 0.97

0.99 0.99 0.99

Confusion Matrix for Crowd-Buzz and Mobilization & Planning Classifiers

Accuracy Results for Crowd-Buzz and Mobilization & Planning Classifiers

Experimental Results


Distribution of χ2 and p-value for Frequent Pairs of Locations (Australia (LEFT), U.S. (RIGHT)) and

Topics for 3 consecutive Days in Sliding Window (Christmas Island Hunger Strike)

ISI Leading Conferences

and Journals

SocialComm SASCVV

SocialComm, ISI, EISIC, WebSci, PAISI, ICWSM

Journals Security Informatics, CyberTerrorism, Criminal

Justice and Popular Culture

Machine Learning/NLP BDA, COMAD, ICDCIT

PAKDD, SIGKDD, CIKM, AAAI


Conclusions

OSSINT is a growing research field and has attracted the

attention of several researchers in past decade

We discuss two major and important applications of

OSSINT- Online Radicalization and Online Civil

Disobedience

YouTube is most widely used platform for Online

Radicalization, Hate and Extremism Promotion

KNN, SVM, Clustering, Naïve Bayes, KBF, EDA are

commonly used techniques for hate promotion detection


Conclusions Focused Crawler is an efficient approach to locate hate

promoting content on YouTube.

User activity feeds can be used as discriminatory features to

identify a hate promoting user channel.

SSA approach has higher precision, recall and f-score in

comparison to BFS approach.

Social Network Analysis helps us to locate hidden

communities and user playing major role in community.

Hate promoting users upload videos targeting some specific

audiences who are affected by the incidents shown in the

video


Conclusions Twitter plays and important role in facilitating mobilization

and planning of civil unrest related events

Clustering, Logistic Regression and Dynamic Query

Expansion, Ensemble Learning are most commonly used

techniques for civil unrest event prediction

We present an approach for early detection of civil unrest

related events evaluated on real world dataset (open source

Twitter data)

We perform semantic enrichment on raw tweets and filter

event related tweets (crowd-buzz and mobilization)


Conclusions We develop a frequency based model on enriched tweets

and find those pairs of location, topics and time that are

significantly correlated

Detecting trend analysis of spatial, temporal and topic based

entities in sliding window is an efficient approach for event

forecasting

Early identification of crowd-buzz and mobilization tweets is

value added for event forecasting


Future Directions

Investigating the application of parallel corporal for

detecting online radicalization communities over cross

platforms

Detecting protest related events in real time and events

with overlapping sliding window (multiple events occurring

in same time frame)


References


1. Agarwal, S., Sureka, A.: Copyright infringement detection of music videos on youtube by mining

video and uploader meta-data. In: Big Data Analytics (BDA). pp. 48–67 (2013)

2. Agarwal, S., Sureka, A.: A focused crawler for mining hate and extremism promoting videos on

youtube. In: 25th ACM Conference on Hypertext and Social Media (HT). pp. 294–296 (2014)

3. Agarwal, S., Sureka, A.: Learning to classify hate and extremism promoting tweets. In: Intelligence

and Security Informatics Conference (JISIC). pp. 320–320 (2014)

4. Agarwal, S., Sureka, A.: Topic-specific youtube crawling to detect online radicalization. In:

Databases in Networked Information Systems (DNIS), 2015. pp. 133–151 (2015)

5. Agarwal, S., Sureka, A.: A topical crawler for uncovering hidden communities of extremist micro-

bloggers on tumblr. In: 5th Workshop on Making Sense of Micro- posts (MICROPOSTS) (2015)

6. Agarwal, S., Sureka, A.: Using common-sense knowledge-base for detecting word obfuscation in

adversarial communication. In: Workshop on Future Information Security (FIS) (2015)

7. Agarwal, S., Sureka, A.: Using knn and svm based one-class classifier for detecting online

radicalization on twitter. In: Distributed Computing and Internet Technol- ogy (ICDCIT). pp. 431–442

(2015)

8. Aggarwal, N., Agarwal, S., Sureka, A.: Mining youtube metadata for detecting privacy invading

harassment and misdemeanor videos. In: Privacy, Security and Trust (PST). pp. 84–93 (2014)

References


9. Budak, C., Georgiou, T., Agrawal, D., El Abbadi, A.: Geoscope: Online detection of geo-correlated

information trends in social networks. Proceedings of the VLDB Endowment 7, 229–240 (2013)

10. Compton, R., Lee, C.: Detecting future social unrest in unprocessed twitter data:emerging

phenomena and big data. In: Intelligence and Security Informat- ics (ISI). pp. 56–60 (2013)

11. Fu, T., Huang, C.N., Chen, H.: Identification of extremist videos in online video sharing sites. In:

Intelligence and Security Informatics, 2009. ISI ’09. IEEE Inter- national Conference on. pp. 179–

181 (June 2009)

12. Hua, T., Lu, C.T., Ramakrishnan, N.: Analyzing civil unrest through social media. Computer 46(12),

80–84 (Dec 2013)

13. Kwok, I., Wang, Y.: Locate the hate: Detecting tweets against blacks. In: AAAI (2013)

14. Qazvinian, V., Rosengren, E., Radev, D.R., Mei, Q.: Rumor has it: Identifying mis- information in

microblogs. In: Proceedings of the Conference on Empirical Methods in Natural Language

Processing. pp. 1589–1599. Stroudsburg, PA, USA (2011)

15. Ramakrishnan, N., Butler, P., Muthiah, S.: ’beating the news’ with embers: Fore- casting civil unrest

using open source indicators. In: Proceedings of the 20th ACM SIGKDD International Conference

on Knowledge Discovery and Data Mining. pp. 1799–1808. KDD ’14, ACM, New York, NY, USA

(2014)

16. Wang, M., Alan, C.G.: Intelligence and security informatics. Pacific Asia Workshop (PAISI) (2011)

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Open Source Social Media Analytics for Intelligence and ... · Social Media Websites API Wikipedia Social Curation (Theme or topic based content sharing) Crowd Sourcing Websites Examples-