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Presentation for the paper entitled: "A Weakly Supervised Bayesian Model for Violence Detection in Social Media" presented at the IJCNLP 2013
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Click to edit Master subtitle styleA Weakly Supervised Bayesian Model for Violence Detection in Social Media
Elizabeth Cano*, Yulan He*, Kang Liu+, Jun Zhao+
*School of Engineering and Applied Science Aston University, UK+Institute of Automation Chinese Academy of Sciences, China
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o Introduction
o Research Challenges
o Violence Detection Model
o Deriving word priors
o Experiments
Outline
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Introduction
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Introduction
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Introduction
Objectives
Identification of suspicious tweets
Violence-related Topic detection
Extraction of violent and criminal events appearing in social media
Objectives
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Violence-related content Characterised by the use of terms expressing aggression and
attitudes towards violence
Violence-related content Analysis Identifying violence polarity in piece of text (violence-related or
non-violence related) Involves the detection of particular types of sentiments not
necessarily negative (e.g. anger, shame, excitement)
IntroductionViolence-related content analysis
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Challenges
Restricted number of characters
Irregular and ill-formed words Wide variety of language
Evolving jargon (e.g. slang and teenage lingo)
Event-dependent vocabulary characterising violence-related content
• Volatile jargon relevant to particular events. While sentiment and affect lexicon rarely changes in time, words relevant to violence tend to be event dependent
E.g., “fire” and “flame” are negative during the UK riots 2011, but appear to be positive in the London Olympics 2012.
E.g. “#Jan25” violence-related during the Egyptian revolution
IntroductionCharacterising violence-related tweets
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Topic Classification of short texts Standard supervised machine learning methods [Milne-et-
al 2008][Gabrilovich-et-al 2006][Munoz-et-al 2011][Meij-et-al 2012]
Alleviate micropost sparsity by making use of external knowledge sources (e.g. DBpedia)[Michelson-et-al 2010][Cano-et-al 2013]
Weakly Supervised approaches JST model [Lin&He 2009][Lin&He2012]
Partially-Labeled LDA (PLDA) [Ramage et al., 2011]
Related WorkViolence-related classification in Social Media
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Rely on supervised classification techniques or do not cater for the violence detection challenges.
Do not perform discover topics with an associated document category.
Related WorkViolence-related classification in Social Media
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Topic Classification of short texts Standard supervised machine learning methods [Milne-et-al
2008][Gabrilovich-et-al 2006][Munoz-et-al 2011][Meij-et-al 2012] Alleviate micropost sparsity by making use of external
knowledge sources (e.g. DBpedia)[Michelson-et-al 2010][Cano-et-al 2013]
Rely on supervised classification techniques or do not cater for the violence detection challenges.
Do not perform discover topics with an associated document category.
Related WorkViolence-related classification in Social Media
Since violence-related events tend to occur during short to medium life-spans, methods relying only on labeled data can rapidly become outdated.
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How to characterise violence-polarity?
How to build a model to discriminate across documents to identify violence-related content?
How to provide overall information to understand the type of violence-related events?
Violence-related classification in Social MediaChallenges
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Violence Detection Model (VDM)Problem Formulation and Proposed Method
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Accessing Topics via Word Distributions
o Novel Bayesian Modelling Approach for: Identifying violent content in social media No need of labelled data Inspired by the previous work on sentiment analysis, in
particular on the JST model[Lin&He 2009][Lin&He2012]
o Use of knowledge sources (e.g. DBpedia) Priors derivation strategies
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Accessing Topics via Word Distributions
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Accessing Topics via Word Distributions
Each Tweet can involve multiple topics
Topics
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Accessing Topics via Word Distributions
Each tweet involves as well words with different violence-polarity
Violence Polarity
Casting these intuitions into a generative probabilistic process [Blei-et-al 2003]
- Each document is a random mixture of corpus-wide topics- Each word is drawn from one of those topics
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Accessing Topics via Word Distributions
Text
Violence polarity
Document
violence-related
non-violence-related
Text
Violence polarity
violence-related
non-violence-related
Document
non-violence-related
violence-related
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DNd
word topic
word
vioLabel Violence probability
Violabel/topiclanguage model
violenceLabel/ topic probability
Violence Detection Model (VDM)
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Violence Detection Model (VDM)
• Choose ω Beta(ε), φ∼ 0 Dir(β∼ 0), φ Dir(β). ∼
• For each category (violent or non-violent) c
For each topic z under the document category c
o Choose θcz ~ Dir(α)
• For each doc m
Choose πm ~ Dir(γ)
For each word wi in doc mo choose xm,n Mult (ω); ∼o If xm,n =0,
choose a word wm,n Mult(φ∼ 0); o if xm,n =1,
choose a tweet category label cm,n Mult (∼ πm ),
choose a topic zm,n Mult(θ∼ cm,n ), choose a word wm,n ∼
Mult(φcm,n ,zm,n ).
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Violence Detection Model (VDM)
• Single document category-topic distribution shared across all the documents.
• Assumes words are generated either from a category-specific topic distribution or from a general background model.
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Deriving Word Priors
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• Violence Lexicon Preparation• DBpedia articles from violent related topics• Twitter Data for Jan-Dec 2010 (10% Twitter Firehose)
Non-Violence-related
twilightsandwich
awardmoonrecord
commonexcitedgreat
Violence-relatedfightwar
protestriots
conflictbomb
troublefear
Violence Lexicon
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Deriving PriorsUsing DBpedia Categories• Structured Semantic Web
Representation of data derived from Wikipedia
Maintained by thousand of editors Evolves and adapts as knowledge
changes [Syed et al, 2008]
• Cover a broad range of topics
• Characterise topics with a large number of resources
DBpedia* Yago2 Freebase
Resources 2.35 million 447million 3.6 million
Classes 359 562,312 1,450
Properties 1,820 253,213,842
7,000
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Deriving PriorsUsing DBpedia Categories
Violence
Terrorism
War
…
Revolutionary Terror
Military Operations
….
Guerrilla Warfare
….
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• Business & Finance• Disaster & Accident • Education • Entertainment & Culture• Environment• Health & Medical• Hospital & Recreation• Labor • Law &Crime
Obtaining Priors from Tweets
1 million Tweets annotated with OpenCalais derived topics including:
•Politics• Religion & Belief• Social Issues• Sports• Technology &Internet• War & Conflict 8,338 tweets
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Datasets for Priors
Tweets (TW) DBpedia (DB) DBpedia chunked (DCH)
Violent-related 10,432 4,082 32,174
Non violent-related 11,411 11,411 11,411
• Use OpenCalais to annotate tweets• Extracted tweets labelled as “War & Conflict” and
considered them as violence-related annotations• OpenCalais has low F-measure of 38% when evaluated on
our manually annotated test set
• DBpedia abstracts have longer sentences than tweets• Generated tweet size documents by chunking the abstracts
into 9 or less words
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• Corpus Word Entropy captures the dispersion of the usage of word w in the corpus SD
• Class Word Entropy characterises the usage of a word in a particular document class
• Relative Word Entropy provides information on the relative importance of that word to a given document class
Relative Word Entropy
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DBpedia-Chunked Priors
DBpedia-derived Priors Tweets-derived Priors
Violent NotViolent Violent NotViolent Violent NotViolent
group customer group gop rebel ey
alleg win power lov destro nnw
armour diff suffer back sectar vot
resid good soc good anti soc
cult sen palest twees mortat aid
separat eat knif interest amnest job
influ surve rebel right drug good
democr afford campaign answer fighter congrat
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Word Priors Obtained using RWE
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Experiments
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Datasets for Experiments
Training set Testing set
Violence-related10,581
759
Non violence-related 1,000
• TREC Microblog 2011 corpus• Comprises over 16 million tweets sampled over a two week
period (January 23rd to February 8th, 2011)• includes 49 different events
• violence-related ones such as Egyptian revolution, and Moscow airport bombing
• non-violence related such as the Super Bowl seating fiasco
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• Learned from labelled features• Word priors are used as labelled feature constraints
• Train MaxEnt classifier with Generalized Expectation (GE) [Druck et al., 2008] or Posterior Regularization (PR) [Ganchev et al., 2010]
• Joint Sentiment-Topic (JST) model [Lin&He 2009][Lin&He2012]
• Set the number of sentiment classes to 2 (violent or non-violent)
• Partially-Labeled LDA (PLDA) [Ramage et al., 2011]
• Assume that some document labels are observed and model per-label latent topics
• Supervised information is incorporated at the document level rather than at the word level
• The training set is labelled as violent or non-violent using OpenCalais
Baselines
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• ME-GE and ME-PR perform poorly
• Best result obtained using VDM with word priors derived from TW using RWE
• Source data for deriving word priors• DB does not improve over TW
• DCH boosts F-measure in JST and is close to TW for VDM
• RWE consistently outperforms IG for both JST and VDM
Violence Classification Results
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Varying Number of Topics
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Violence-related topics Non violence-related topics
Topic Coherence Evaluation
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Topic 1 Topic 2 Topic 3 Topic 4
egypt middle internet crash
tahrir east egypt kill
cair give phone moscow
strees power block bomb
police idea word airport
protester government service tweets
square spread government injure
arm uprise shut arrest
report fall facebook dead
Protest in Tahrir Square
Middle East uprise
Government shut down Facebook
Moscow Airport bombing
Example Violence-Related Topics
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Questions?
Yulan He [email protected] Kang Liu [email protected] Jun Zhao [email protected]
Elizabeth Cano [email protected]
Slides available at http://www.slideshare.net/ampaeli