online reputation monitoring in twitter from an information access perspective
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Online Reputation Monitoring in Twitter
from an Information Access Perspective
Damiano Spina
UNED NLP & IR Group
damiano@lsi.uned.es @damiano10
January 29, 2014 FdI UCM, Madrid, Spain
In Collaboration with
● Julio Gonzalo
● Enrique Amigó
● Jorge Carrillo de Albornoz
● Irina Chugur
● Tamara Martín
University of Amsterdam
● Maarten de Rijke
● Edgar Meij (Yahoo! Barcelona)
● Mª Hendrike Peetz
Llorente & Cuenca
● Vanessa Álvarez
● Ana Pitart
● Adolfo Corujo
LiMoSINe EU Project
www.limosine-project.eu
Arab Spring in Egypt, Jan 2011
Online Reputation Monitoring (ORM)
● Reputation/public image is key for entities:
– Companies, Organizations, Personalities
Online Reputation Monitoring (ORM)
● Reputation/public image is key for entities:
– Companies, Organizations, Personalities
● Social Media:
– Necessity (and opportunity) of handling the public image
of entities on the Web
Online Reputation Monitoring (ORM)
● Reputation/public image is key for entities:
– Companies, Organizations, Personalities
● Social Media:
– Necessity (and opportunity) of handling the public image
of entities on the Web
– Online Reputation Managers/Analysts
● Handle the reputation of an entity of interest (i.e., customer)
● Among other tasks, monitoring Social Media (manually!)
– Early detection of issues/conversations/topics that may damage the
reputation of the entity of interest
Automatic Tools for ORM
Information Access (IA) techniques for -Tracking Relevant Mentions - Sentiment Analysis - Discover Keywords/Topics
Problem
● Lack of standard benchmarks
for evaluation
Problem
● Lack of standard benchmarks
for evaluation
● It is hard for the analysts to know
how automatic tools will perform
on their real data
Goals
● Formalize the Online Reputation Monitoring
problem as scientific challenges
Goals
● Formalize the Online Reputation Monitoring
problem as scientific challenges
– Build standard test collections
– Organize International evaluation campaigns
– Bring together ORM and IA experts from Industrial and
Academic communities
Goals
● Formalize the Online Reputation Monitoring
problem as scientific challenges
– Build standard test collections
– Organize International evaluation campaigns
– Bring together ORM and IA experts from Industrial and
Academic communities
● Propose automatic solutions that may assist the
reputation manager, reducing the effort in their daily
work
Outline
● Online Reputation Monitoring in Twitter
Outline
● Online Reputation Monitoring in Twitter
● Formalization from an Information Access perspective
– Tasks Definition
– Evaluation Framework
Outline
● Online Reputation Monitoring in Twitter
● Formalization from an Information Access perspective
– Tasks Definition
– Evaluation Framework
● How much of the problem can be solved automatically?
– Filtering
– Topic Detection
Outline
● Online Reputation Monitoring in Twitter
● Formalization from an Information Access perspective
– Tasks Definition
– Evaluation Framework
● How much of the problem can be solved automatically?
– Filtering
– Topic Detection
● Putting the Human in the Loop: A Semi-Automatic ORM
Assistant
Online Reputation Monitoring in
● Analysts' daily work
– Focus on a given entity of interest
Online Reputation Monitoring in
● Analysts' daily work
– Focus on a given entity of interest
– Recall oriented
● They have to check all potential mentions!
● Also filter out not relevant mentions manually
Online Reputation Monitoring in
● Analysts' daily work
– Focus on a given entity of interest
– Recall oriented
● They have to check all potential mentions!
● Also filter out not relevant mentions manually
– They make a summary to report to the client periodically
– Summary
● What is being said about the entity in Twitter?
What are the topics that may damage its reputation?
Why Twitter?
● (Bad) news spread earlier/faster/more unpredictable
than any other source in the Web
● Most popular microblogging service
– >230M monthly active users
– 5k tweets published per second
Why Twitter?
● (Bad) news spread earlier/faster/more unpredictable
than any other source in the Web
● Most popular microblogging service
– >230M monthly active users
– 5k tweets published per second
● Challenging for Information Access
– Little context (only 140 characters)
– Non-standard, SMS-like language
Online Reputation Monitoring in
Online Reputation Monitoring in
?
Problem Formalization
ORM from an Information Access Perspective
Filtering Task
● Is the tweet related to the entity of interest?
● Example: Suzuki
related unrelated
Filtering Task
● Is the tweet related to the entity of interest?
● Example: Suzuki
● Input: Entity of interest (name + representative
URL) + tweets that potentially mention the entity
● Output: Binary classification at tweet-level
(relevant/not relevant)
related unrelated
Polarity for Reputation Task
● Does the tweet affect negatively/positively to the reputation
of the entity?
● Example: Goldman Sachs
Polarity for Reputation Task
● Does the tweet affect negatively/positively to the reputation
of the entity?
● Example: Goldman Sachs
● Input: Entity of interest (name + representative URL) +
Stream of tweets that potentially mention the entity
● Output: Multi-class classification at tweet-level
(positive/negative/neutral)
Topic Detection Task
● What are the topics discussed in the tweets?
Topic Detection Task
● What are the topics discussed in the tweets?
● Input: Entity of interest (name + representative URL) +
Stream of tweets that mention the entity
● Output: Topics (Cluster of tweets)
Topic Priority Task
● What is the priority of each topics
in terms of reputational issues?
● Input: Topics
● Output: Ranking of Topics
– Alerts go first
Evaluation Framework
● Reusable Test Collections
● Evaluation Measures
– Compare systems to annotated ground truth
Evaluation Framework
● Reusable Test Collections
● Evaluation Measures
– Compare systems to annotated ground truth
● Evaluation Campaigns
– Involve community
– Compare different approaches
RepLab: Evaluating Online Reputation
Management Systems
● Organized as CLEF Labs
Cross-Language Evaluation Forum
RepLab: Evaluating Online Reputation
Management Systems
● Organized as CLEF Labs
Cross-Language Evaluation Forum
● 2 editions so far (+1 this year)
– RepLab 2012
● Filtering and Polarity for Reputation
● Topic Detection and Topic Priority as Monitoring Pilot Task
– RepLab 2013
– RepLab 2014 (in progress)
E. Amigó, J. Carrillo de Albornoz, I. Chugur, A. Corujo, J. Gonzalo, T. Martín, E. Meij, M. de Rijke, D. Spina Overview of RepLab 2013: Evaluating Online Reputation Monitoring Systems Proceedings of the Fourth International Conference of the CLEF initiative. 2013.
Building Test Collections
Annotation Process
RepLab 2013 Annotation Tool
The RepLab 2013 Dataset
Evaluation
Why we Need All this Stuff?
● To Evaluate Automatic Systems
● To be able to answer the questions:
– Which system performs better?
– Can tasks be solved automatically?
Automatic Solutions for ORM:
Filtering + Topic Detection
Evaluation: Filtering Task
Automatic systems can significantly help when there is enough training data for each entity (750 tweets)
Evaluation: Filtering Task
Automatic systems can significantly help when there is enough training data for each entity (750 tweets) How? * Supervised learning POPSTAR (Univ. of Porto): Features: Twitter metadata, textual features, keyword similarity + external resources such as the entity’s homepage, Freebase and Wikipedia.
Evaluation: Topic Detection
Much more difficult than the Filtering Task
Evaluation: Topic Detection
Much more difficult than the Filtering Task
What performed better in RepLab? UNED_ORM: Clustering of wikified tweets Tweets are represented as Bag of Wikipedia Concepts Tweet content linked to Wikipedia concepts based on intra-Wikipedia links
Topic Detection Approach
● Tweet -> Set of Wikipedia Concepts/Articles
● Clustering: Tweets sharing x% of identified
Wikipedia articles are grouped together
D. Spina, J. Carrillo de Albornoz, T. Martín, E. Amigó, J. Gonzalo, F. Giner UNED Online Reputation Monitoring Team at RepLab 2013 CLEF 2013 Labs and Workshops Notebook Papers. 2013.
Wikification: Commonness probability
WP concept c, n-gram q
q=“ferrari”
Wikification: Commonness probability
WP concept c, n-gram q
q=“ferrari”
Wikification: Commonness probability
WP concept c, n-gram q
COMMONNESS "Ferrari S.p.A.", "ferrari" =4
(4 + 2 + 1)= 0.57
q=“ferrari”
Putting the Human in the Loop
Building Semi-Automatic Tools for
ORM
ORMA: A Semi-Automatic Tool for
Online Reputation Monitoring
J. Carrillo de Albornoz, E. Amigó, D. Spina, J. Gonzalo ORMA: A Semi-Automatic Tool for Online Reputation Monitoring in Twitter 36th European Conference on Information Retrieval (ECIR). 2014.
Basic Filtering Approach
Basic Filtering Approach
Training tweet
Test tweet (unknown label)
Related/Unrelated
Bag of Words: Tokenization + Preprocessing + Term Weighting
Support Vector Machines (SVM)
Filtering Classifier
0.42 F: Similar to best RepLab
Active Learning for Filtering
M. H. Peetz, D. Spina, M. de Rijke, J. Gonzalo Towards an Active Learning System for Company Name Disambiguation in Microblog Streams CLEF 2013 Labs and Workshops Notebook Papers. 2013.
Active Learning for Filtering
● Margin Sampling (confidence of the classifier)
● After inspecting 2% of test data (30 out of 1500 tweets):
– 0.42 -> 0.52 F(R,S) (19.2% improvement)
– Higher than the best RepLab contribution
Active Learning for Filtering
● Margin Sampling (confidence of the classifier)
● After inspecting 2% of test data (30 out of 1500 tweets):
– 0.42 -> 0.52 F(R,S) (19.2% improvement)
– Higher than the best RepLab contribution
● The cost of initial training data can be reduced
substantially:
– Effectiveness:
10% training + 10% test for feedback = 100% training
Conclusions
Conclusions
● Online Reputation Monitoring in Twitter
Conclusions
● Online Reputation Monitoring in Twitter
● Formalized as Information Access Tasks
– Reusable Test Collections
– Systematic Evaluation
Conclusions
● Online Reputation Monitoring in Twitter
● Formalized as Information Access Tasks
– Reusable Test Collections
– Systematic Evaluation
● Can tasks be solved automatically?
– Filtering: Almost solved with enough training data
(0.49F, 0.91 accuracy)
– Topic: Systems are useful but not perfect
Conclusions
● Online Reputation Monitoring in Twitter
● Formalized as Information Access Tasks
– Reusable Test Collections
– Systematic Evaluation
● Can tasks be solved automatically?
– Filtering: Almost solved with enough training data
(0.49F, 0.91 accuracy)
– Topic: Systems are useful but not perfect
● We need the expert in the loop
– With a substantial reduction of manual effort
Online Reputation Monitoring in Twitter
from an Information Access Persepective
Damiano Spina
UNED NLP & IR Group
damiano@lsi.uned.es @damiano10
January 29, 2014 FdI UCM, Madrid, Spain
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