Finding Key Influencers and Viral Topics in Twitter Networks Related to ISIS and to the 2016 Primary...

Finding Key Influencers and Viral Topics in Twitter Networks Related to ISIS and to the 2016 Primary ElectionsSteve Kramer, Ph.D.President & Chief ScientistParagon Science, Inc.March 2016

Copyright © 2006-2016 Paragon Science, Inc. All rights reserved.

Overview Background Information about Paragon Science Example 1: ISIS Twitter Network Analysis Example 2: 2016 Election Twitter Network Analysis Q & A

Paragon Science, Inc. 2

About Paragon Science Advisory Board Company

• Analysis of Healthcare Data Digital Motorworks/CDK Global

• Vehicle Pricing Analytics Houston Law Firm

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• Web User Click Patterns


Founder: Dr. Steve Kramer• PhD in computational physics (nonlinear

dynamics)• Self-funded data science entrepreneur• 22 years of research and high-tech

experience• Manager and consultant at software

companies• Reviewer for scientific journals and

conferences• Member of StartOut Austin steering

committee

http://affinityincmagazine.com/paragon-science-puts-patented-technology-to-work-for-range-of-clients/

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http://affinityincmagazine.com/paragon-science-puts-patented-technology-to-work-for-range-of-clients


Using our patented anomaly detection software to find the “unknown unknowns”: unusual changes that represent revenue opportunities to exploit or risks to mitigate

Many possible application areas: • Social media alerting and sentiment change detection• Pricing and market trend analysis and alerting• Fraud prevention (banking, insurance, online auctions,…)

Key advantages• No machine learning or training required• Robust to missing or erroneous data• Highly scalable and parallelizable

What Are We Doing?


How Is It Done Today? Existing approaches

• Standard SNA metrics• Rule-based systems (transaction profiling, etc.)• Bayesian and other statistical/probabilistic models• Machine learning tools (neural nets, HMMs, etc.)

Some limitations of existing methods• Training requirements can be large for neural nets.• For rule-based systems, it is difficult to effectively predict or define

new “bad” anomalies or patterns in advance. • Many current methods are not scalable to real-world operational

requirements.


What Is New in Our Patented Approach? A powerful anomaly detection approach that

incorporates nonlinear time series analysis methods• US Patent #8738652 (1.usa.gov/1kkyVD9)

“Systems and Methods for Dynamic Anomaly Detection” Key questions answered:

• Which entities behave or evolve differently than others in the data set?

• Which entities have shifted their behavior unexpectedly?


What Is New in Our Approach? (Cont’d.) Our framework inherently captures the dynamics of the entities under

study, without having to specify in advance normal vs. abnormal behavior.

We can simultaneously analyze the time evolution of• Network structures• Any associated attributes (text terms, geospatial position, etc.)

Our technique is robust with respect to missing or erroneous data. As result, we can

• Find key players in rapidly changing networks• Provide early warning of viral videos and online documents• Focus attention on the most-anomalous events or transactions


Dynamic Anomaly Detection Overview A general approach that incorporates nonlinear time series

analysis methods• Complexity measures• Finite-time Lyapunov exponents (FTLEs)

Input data• Communications or transactional data streams• General time-dependent data sets

Key questions• Which entities behave or evolve differently than others in the data

set?• Which entities have shifted their behavior unexpectedly?


Finite-Time Lyapunov Exponents (FTLEs) General dynamical system

Flow map• Advects points in the state

space• Describes the time

evolution of the system


FTLEs characterize the amount of stretching or contraction about a point x0 during a time interval T• Stability• Predictability

Definition

Finite-Time Lyapunov Exponents (FTLEs)


Similarly, characteristic vectors derived from the flow map’s Jacobian can describe the generalized directions of the local stretching or contraction.

Possible derivation approaches:• Weight-based column sampling• Singular value decomposition (SVD)• Principal component analysis (PCA)

Derived Jacobian Vectors


Paragon Dynamic Anomaly Detection

Representation of Data at t=ti

Cluster Resolution

Feature Vector Encoding

Outlier Detectionat t=ti

3+Time Intervals?

Yes

No

Clustering /Segmentation

Dynamic Anomaly Detection

Nonlinear Time Series AnalysisFTLEs, Dynamic Thresholds, etc.

PatternClassification

Outlier Detection

Domain-Specific FilteringThreat Signatures,Risk Profiles, etc.

Example 1: ISIS-Related Twitter AnalysisInitial data set from Twitter API collected using twittertap:

Date range: 11/30/2015 – 12/10/20152,541,812 tweets7,802,210 generated links with hashtags, URLs, and user replies

Research planPerform k-core decompositionRun anomaly detection software on sub-networks of nodes in the

central core to find the most influential users and most viral URLsCarry out community detection, topic detection, and sentiment

analysis


Example 1: ISIS-Related Twitter Network


User A User B

User C

replies to

mentions

URL 1 URL 2

Hash Tag 1

Hash Tag 2

references

uses

uses

references

Link Type # LinksUser links to URL 2,014,572User mentions user 2,867,633User references hashtag 2,699,875User references symbol 2,636User replies to user 215,343

K-core DecompositionThe k-core of a graph is a maximal subgraph in which each

vertex has at least degree k. The coreness of a vertex is k if it belongs to the k-core but not to

the (k+1)-core. The k-core decomposition is performing by recursively removing

all the vertices (along with their respective edges) that have degrees less than k.

The k-core decomposition of a network can be very effective in identifying the individuals within a network who are best positioned to spread or share information. M. Kitska, et al., “Identifying influential spreaders in complex networks,”

arXiv:1001.5285v1 [physics.soc-ph] (2010).

16

K-Core Decomposition of the ISIS Network


http://sourceforge.net/projects/lanet-vi/

Central Core of the ISIS Network


Users at the center of the k-core decomposition are positioned well to spread information and influence the network.

Top URLs in the Central Core


URL Web Page Title Coreness # Linkshttp://www.mirror.co.uk/news/uk-news/isis-would-love-you-bomb-6941441#ICID=sharebar_twitter

ISIS would love you to bomb them to bring about apocalyptic final fight, says journalist who lived among terrorists - Jurgen Todenhofer - Mirror Online

89 398

https://www.youtube.com/watch?v=nVDiK3J9PKQ

How to Paralyse & Eliminate ISIS in Less Than 24 Hours - Younus AlGohar - YouTube

89 384

http://shr.gs/Um8lnCZ Jihadi BILLIONAIRES: ISIS top terror rich list“ but how are they blowing all the dough?

89 349

https://www.youtube.com/watch?v=FS9iPz-cPlY Humanity Under Attack! What Must Be Done Now? - Younus AlGohar - YouTube

89 331

http://is.gd/txNkng How to Paralyse & Eliminate ISIS in Less Than 24 Hours - Younus AlGohar

89 327

http://bbc.in/aggad Paris attacks: Bataclan third attacker identified - BBC News

89 317

http://ti.me/1XPKXcx London Subway Attacker Had ISIS Images on Phone: Officials

89 317

http://dailym.ai/1NFIp5L ISIS releases its latest video as they execute two ˜sorcerers” in Libya | Daily Mail Online

89 298

http://youtu.be/mXOSQj4xjPY Fitna-e-Khwarij - YouTube 89 259http://www.telegraph.co.uk/news/worldnews/northamerica/usa/12037849/Majority-of-Americans-support-sending-ground-troops-to-fight

Majority of Americans support sending ground troops to fight Isil

89 255

Top 5 URLs in the Central Core

20

Top Users in the Central Core


User Coreness # LinksMailOnline 89 6255David_Cameron 89 3330Telegraph 89 2072TarekFatah 89 1907BBCWorld 89 992younusalgohar 89 977mehdifoundation 89 830rafu007 89 791TIMEWorld 89 700niallboylan4fm 89 667

Topic Detection in the ISIS Twitter Network


User A User B

User C

replies to

mentions

URL 1 URL 2

references

Term 1

Term 2

Term N

Term 3

Topic 1

Topic 2

Topic M

146 Topics Detected

Title-to-Term Network for Topic Detection

23Paragon Science, Inc.

Title-to-Term Network for Topic Detection


Topic 3 Communities of Users


Topic 3 Top 10 Web Sites


Topic 3 Selected Users


Topic 4 Top Web Sites


Incorporating Sentiment Analysis

• Incorporate sentiment analysis scores as an input to dynamic anomaly detection in order to track the propagation of references to websites with particular emotions.

• Use the LIWC (Linguistic Inquiry and Word Count) tool to calculate the sentiment scores of the web pages.– Prof. James Pennebaker from UT Austin (http://liwc.wpengine.com/) – Sample categories

• Positive emotion• Negative emotion• Anger• Anxiety

Paragon Science, Inc.

http://liwc.wpengine.com/

Top Web Pages by AnxietyWeb Page Title URL Anxiety ScoreWatch Daniel Scavino Jr.'s Vine "POTUS on terrorism."

https://vine.co/v/i71FvOKlYgv 11.11

*WARNING: New ISIS VIDEO: Muslim Children Execute Captives, Obama, we will behead you, as we will do to all the Jews | Pamela Geller

http://bit.ly/1TMcgif 6.51

The Mastermind Of The San Bernardino Massacre Has All The Hallmarks Of An ISIS Terrorist Attack... - Linkis.com

http://ln.is/shoebat.com/2015/12/PGcNB

5.56

The Far-Reaching Effects of Global Terrorism - YouTube

http://youtu.be/L_qr01yHoQs 4.85

Terrorism isn't scaring Americans; Obama is by Andrew Malcolm - Investors.com

http://news.investors.com/politics-andrew-malcolm/120715-784023-obama-isis-speech-no-new-strategy.htm

4.03

57 Paris airport workers on terror watch list, “Allahu akbar” scrawled on fuel tank

http://www.jihadwatch.org/2015/12/57-paris-airport-workers-on-terror-watch-list-allahu-akbar-scrawled-on-fuel-tank

3.03

DIA Emails: ISIS was deliberately armed and funded by Obama & Hillary Clinton

http://ian56.blogspot.com/2015/06/the-terrorist-threat-has-been.html?m=1

2.94

Paragon Science, Inc.

Top Web Pages by Negative Emotion RatioWeb Page Title URL Negative/

Positive Emotion Score

Russian airstrike 'kills family in their car' as bombs obliterate ISIS oil convoy | Daily Mail Online

http://dailym.ai/1IIU2Yz 21.9

Study: Unprecedented support for ISIS in the U.S. - CNNPolitics.com

http://cnn.it/1XF0p61 13.3

US-led coalition not striking ISIS oil trucks despite evidence – Russia’s General Staff” RT News

http://on.rt.com/6y9c 12.1

ISIS PARIS TERRORIST Recruited Fighters at Hungarian Refugee Camp - YouTube

https://www.youtube.com/watch?v=88TJBvH1zzg

11.9

U.S. rejects Russia’s claim of Turkey’s cooperation with ISIS

http://goo.gl/Q9MWGk 11.8

Islamic State's Sinai chief said in Gaza to coordinate with Hamas | The Times of Israel

http://bit.ly/1N6bqZa 10.0

Is ISIS Entering US Through Mexico? Amid Islamic State Fears, Border Patrol Captures Afghan, Pakistani Men Being Smuggled Into Country

http://bit.ly/1l9Mxo1 9.8

Why Can't White House Just Say ISIS Beheaded Christians? - Investors.com

http://ift.tt/1zMpWNz 8.6

For the Record: How Stubborn U.S. Leaders May Be Hurting the Fight Against ISIS on Vimeo

https://vimeo.com/147860012 8.4

Just 0.4 Percent of Syrian Refugees Admitted to U.S. Since Paris Attacks Are Christian - Breitbart

http://www.breitbart.com/big-government/2015/12/08/just-0-4-percent-syrian-refugees-admitted-u-s-since-paris-attacks-christian/

8.1


Mapping Anomalies to Source DataAnomalies Discrete/Continuous

Attribute DistributionsRelated Source

Data

Where and when are the hotspots of changes?

Which nodes and attributes were involved in each anomalous peak?

Anomaly Detection Results for Websites with Negative Emotions


Surge of Twitter user links to web page with high negative emotion score: “The ISIS Trail of Death - NBC News”

Summary of Top 50 Negative Emotion Anomalies

34

Web Page Title Peak Start Peak End Max Change Metric

# Anomalies

The ISIS Trail of Death - NBC News 2015-12-08 03:36:39

2015-12-09 13:36:39

3.01 24

Russia strikes ISIS targets in Syria from sub in Mediterranean for first time (VIDEO) RT News

2015-12-09 07:36:39

2015-12-09 16:36:39

2.33 8

US Air Force running out of bombs to fight ISIS | Fox News

2015-12-06 07:36:39

2015-12-06 21:36:39

2.10 2

If you keep saying Saudi Arabia is like ISIS, you might get sued - The Washington Post

2015-12-02 04:36:39

2015-12-07 09:36:39

2.01 11

Everyone knows what’s going on: Istanbul residents on Turkey-ISIS oil trade Ã¢Â€Â” RT News

2015-12-04 15:36:39

2015-12-04 16:36:39

1.96 2

Is ISIS Entering US Through Mexico? Amid Islamic State Fears, Border Patrol Captures Afghan, Pakistani Men Being Smuggled Into C

2015-12-03 15:36:39

2015-12-03 15:36:39

1.91 1

Iran news in brief, 30 November 2015 - YouTube

2015-12-01 17:36:39

2015-12-01 17:36:39

1.90 1

No Christians: All 132 Syrian Refugees Admitted to U.S. Since Paris Attacks Are Sunni Muslims

2015-12-01 19:36:39

2015-12-01 19:36:39

1.89 1

Most-Anomalous Negative Emotion ISIS Web Page Shared by Twitter Users

35

Animation of ISIS Twitter Network


Many thanks to Cambridge Intelligence for a trial license to their KeyLines software.

https://www.youtube.com/watch?v=j7Sof3BdDSY

Example 2: Election 2016 Twitter Network


Data set from Twitter API collected using twittertap:Date range: 2/24/2016-3/4/201613 M tweets sent by 2.8 M users22.6 M generated links with hashtags, URLs, and user replies

K-core decomposition:Performed once for each dayMaximum coreness of 88Central part of the network created by selecting the three

innermost shells for each day

K-Core Decomposition of the Innermost Election 2016 Twitter Network


Detail View of the Central Core


Top 10 Users in the Central Core


Top 10 URLs in the Central Core


URL Degreehttp://www.infowars.com/report-trump-supporters-in-texas-see-votes-switched-to-rubio/ 2334http://www.bostonherald.com/news/us_politics/2016/02/amid_trump_surge_nearly_20000_mass_voters_quit_democratic_party 1665http://newsninja2012.com/gov-nikki-haley-just-became-a-liability-for-rubio-after-this-was-published-to-social-media/ 1340http://www.thepoliticalinsider.com/donald-trump-quietly-helped-marine-whom-obama-ignored/ 1203https://www.donaldjtrump.com/press-releases/donald-j.-trump-demands-retraction-of-misleading-ads-produced-by-marco-rubi 1172http://m.washingtontimes.com/news/2016/feb/29/victims-illegal-immigrant-violence-gop-no-rubio/ 1136http://goo.gl/cTEFYR 978http://drudge.tw/1ngE3Mt 778https://www.washingtonpost.com/news/post-politics/wp/2016/02/21/donald-trump-consults-with-rudy-giuliani-as-he-builds-political-kitchen-cabinet/ 770

Top URL in the Central Core


Top 10 URLs in the Entire Network


URL Coreness Degreehttps://www.youtube.com/watch?v=DnpO_RTSNmQ 63 17524https://amp.twimg.com/v/077834f2-a406-49cd-bfd4-e6b64274e885 36 17162https://www.youtube.com/watch?v=DnpO_RTSNmQ 67 12703https://amp.twimg.com/v/077834f2-a406-49cd-bfd4-e6b64274e885 23 12229https://amp.twimg.com/v/077834f2-a406-49cd-bfd4-e6b64274e885 26 11687http://cnn.it/1RxbzsD 64 6795http://www.usatoday.com/story/news/politics/elections/2016/02/29/donald-trump-georgia-rally-valdosta/81129964/ 71 6692https://vine.co/v/i6AX96L7Xgi 12 6061https://vine.co/v/i6AX96L7Xgi 10 5962

Top URL in the Entire Network


Community Detection in the Central Core


10 communities detected

Hillary Clinton Sub-Network


Mark Rubio Sub-Network


Bernie Sanders Sub-Network


Donald Trump Sub-Network



What Are the Payoffs? Find the “unknown unknowns” in dynamic data sets Quickly identify key influencers and trends in online

networks Provide early warning of viral videos, anomalous web

events, or unusual network traffic Enable enhanced business intelligence without having to

specify normal vs. abnormal behavior in advance

Third-Party Software Acknowledgements Paragon Science gratefully acknowledges the following researchers and software

providers:• Cytoscape (http://www.cytoscape.org/) • KeyLines (http://www.keylines.com) • Lanet-vi (http://sourceforge.net/projects/lanet-vi/)

◦ J. Alvarez-Hamelin, et al. "Understanding Edge Connectivity in the Internet through Core Decomposition," Internet Mathematics 7 (1): 45–66, 2011.

• Louvain community detection software (http://perso.crans.org/aynaud/communities/)◦ V. Blondel, et al., “Fast Unfolding of Communities in Large Networks,” Journal of

Statistical Mechanics: Theory and Experiment, 10, P10008, 2008.• Networkx (https://networkx.github.io/)

◦ A Hagberg, D Conway, "Hacking social networks using the Python programming language (Module II - Why do SNA in NetworkX)", Sunbelt 2010: International Network for Social Network Analysis.


http://www.cytoscape.org/

http://www.keylines.com/

http://sourceforge.net/projects/lanet-vi/

http://perso.crans.org/aynaud/communities/

https://networkx.github.io/


Thanks for your interest!Steve Kramer

@ParagonSci_Inc


Finding Key Influencers and Viral Topics in Twitter Networks Related to ISIS and to the 2016 Primary...

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