how the live web feels about events

George Valkanas Dimitrios Gunopulos

How the Live Web Feels about Events

Dept. of Informatics & TelecommunicationsUniversity of Athens, Greece

22nd ACM CIKM ConferenceBurlingame, CA, USA

October 29th, 2013

The Web evolves...

tEarly days…

Now

Forums

Online Services

WEB

USERSWho is he?

static dynamic

…and research questions emerge!

• Making sense of the content– Predictions– User profiling / Behaviorism– Event Detection

• Recommendations• Community Detection

Why Event Detection?

• Real-time news reporting – Middle East turmoil

• Emergency response / Disaster Management– Japanese Earthquakes– 2007 Southern California Wildfires

• Decision Making– Political Debates

– Stock Market

• Resource allocation

Formalizing an Event

• What is an event?An event e is a real-world phenomenon, that

occurred at some time t and is usually tied to a

location l*

• In addition to time and place, we want some textual description, to understand it

• Soft Constraint: Identify events as theyoccur, or as promptly as possible

*Y. Yang et al, "Learning approaches for Detecting and Tracking news events", IEEE Intelligent Systems Special Issue 1999

Event Detection, not so easy!

Voluminous Data*

– 200M active users– 400M tweets / day

Continuous input stream

Highly noisy

Personal writing style

Very short text

*Twitter Stats: https://business.twitter.com/audiences-twitter

Existing Techniques

• Trend detection– Prone to large

audiences, e.g. fan base

• Online clustering– Computationally

Expensive– Short messages

• Topic monitoring– Assumes event is known

Cognitive & Affective Theories of Emotions

• Events affect users

Cognitive & Affective Theories of Emotions

• Events affect users• Users feel compelled to externalize their

thoughts, as a result of such external stimuli

#@$*!!!

Thoughts convey emotion

Workflow Overview

Objective: Identify groups of users with sudden changes in aggregate emotional state

– Resembles outlier detection

Geographical Group Monitoring

• Geographical Groups– Covers the need for the event’s location l– Inherently dealing with large groups

Emotion Classification

7 Emotions*

– Anger– Fear– Disgust– Happiness– Sadness– Surprise

– None

* P. Ekman et al, “Emotion in the human face: guide-lines for research and an integration of findings”, Pergamon Press, 1972

Approximating Emotional Distributions (1)

at

U2

U1

U3 U1

U4

U2

U1

U1

U2

U3

U4

U3

U4

U3

U4

3a t3 7 2

• Approximate PDF w/ Kernels– Online technique – fast updating

Approximating Emotional Distributions (2)

at

U2

U1

U3 U1

U4

U2

U1

U1

U2

U3

U4

U3

U4

U3

U4

3a t3 7 2

• Approximate PDF w/ Kernels– Online technique – fast updating– Allows for Sampling– Basis for outlier detection

Event Extraction and Presentation

Experimental Setup

• Twitter data– Gardenhose access (10% public tweets)– April – May 2012 ( ~300M tweets )

• Keep users:– From: Canada, France, Greece, Germany, Ireland, Spain, UK, US– Speaking: English, Spanish, German, Greek– ~33.5M tweets, ~400K unique users

• Replay the stream, ordered by timestamp– No other pre-cleaning

• Technical specs– Java 1.6– Quad Core @3.5, 16Gb RAM– 3 runs average

Preliminary Experiments

• Efficiency (in ms)

• Classification Accuracy– Annotated Gold Standard (~7K tweets)

• (available)

– Majority class: 34%– C4.5: 64,39% 10-fold

Temporality of Emotions

1 minute aggregations

Message 1: Sampling plays its role!Message 2: Fast-paced medium, w/ mid-sized temporal momentum

Locality of Emotions5 minute aggregations

Global Aggregation

Message: Grouping by location makes sense!

Effectiveness in Event Detection

Effectiveness in Event Detection

• Sports

Effectiveness in Event Detection

• Sports• Politics

Effectiveness in Event Detection

• Sports• Politics

• Earthquake

Effectiveness in Event Detection

• Sports• Politics

• Earthquake• Celebrity

Effectiveness in Event Detection

• Sports• Politics

• Earthquake• Celebrity

• Music Contest

Conclusions

• Affective-based event detection in the Live Web

• Affective tweets exhibit temporal & spatial locality

• Our technique can detect various event types

• Annotated gold standard, which we make available to the community

Thank you!

Questions?