Data mining for analyzing social media

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Data mining for analyzing the social media Social

Networks

Video/picture sharing

Opinions

News websites

Blogs

Knowledge sharing Microblogging

eminar at 4/18/2013

PresentaCon: J. Velcin hGp://mediamining.univ-‐lyon2.fr/people/velcin

eminar at housie University – 4/18/2013 – ulien elcin

Ecosystem of ERIC Lab

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Axe Carrés 2 ter

BSc & MSc degrees

BI, data mining, staCsCcs 2 teams: SID & DMD

Academics

Companies

Context The big picture Online discussions ½ -‐sup. clustering ImagiWeb Conclusion

Lyon


Research landscape

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Data Data-‐

warehouse Knowledge

ETL

Online analysis

Data mining

Decision

Complex data integraCon

MulCdimensional modeling


Data Mining & Decision (DMD)


Data Mining & Decision (DMD)

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Social Networks

Microblogging

Video/picture sharing

Opinion sharing

News websites

Blogs

Knowledge sharing

e.g. Social Media -‐  heterogeneous -‐  voluminous -‐  interconnected -‐  evolving

RecommandaCon SummzarizaCon

InformaCon retrieval

MulCcriteria analysis

Machine learning Graph analysis

Complex data analysis

Topological learning Text mining

Prac<cal issue

Approach

Goal: coping with complex data


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Outline

"  The big picture "  Modeling and analyzing online discussions "  Semi-‐supervised clustering "  Focus on Project ImagiWeb "  Future lines of research

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Outline


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Section 1 The big picture


"   A long questioning "   Social representation through the media

[Lippman,22] [Moscovici,76] [Newman and Block,06]

"   Numeric watch on the Web [Chateauraynaud,03]

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Public event

From facts to people: the essential role of media


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Information overload

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Image credit: Go-‐Globe.com



Data journalism

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"   Crucial need to catch the meaning of voluminous data provided by modern social media, in order to design new search engine systems

"   In particular (MSND workshop@WWW’12)

"   “How to surface the best comments, videos and pictures from a variety of sources in real time and then how to verify them ?”

"   “How to quickly surface the best comments and work out which ones are worth investigating further ?”

"   “How to identify quickly the key influencers on any particular story, so they can get inside information or interview them for their news outlets ?”



Salvaged by (media) curation?

" Term originated from Art, appears ~2011 " Three-‐step process:

" Aggregation: gathering " Editorialize: sorting, categorizing,

summarizing, presenting… " Disseminate: contextualizing, sharing

"   Important role of the curator "   Difference between “full curation” and

automatic edition (e.g., paper.li) "   Many platforms (Scoop.it!, Storify, Storiful,

Hopflow, Stumbleupon, Patch…): http://socialcompare.com/fr/comparison/curation-‐platforms-‐amplify-‐knowledge-‐plaza-‐storify

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[Rosenbaum,11]



A case study: the “HuffPost”

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"   Linked with social networks "   Topically indexed "   Available on various devices "   Commented news

"   Community of bloggers

"   Journalist can play both the roles of curator and community manager


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Outline


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Section 2 Modeling and analyzing

online discussions


Online discussions

"   Motivation: "   Numerous available, often underused data "   Crucial to feel the opinion of people

"   Contributions:

"   Recommending key messages [Stavrianou et al.,09,10] "   Extracting the latent social network [Forestier et al.,11] "   Detecting celebrities from online forums [Forestier et al.,12] "   Surfacing roles with unsupervised mechanisms [Anukhin et al.,12]

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eminar at housie University – 4/18/2013 – ulien elcin 16 Julien Velcin - présentation ARC6 18 Octobre 2012

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Anatomy of an online discussion

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A

B

C

A

C

B

D D

A

B

C



Recommending key messages

"   “interesting” message: popular, opinionated, pioneer etc. " Formalization of 6 criteria + simple aggregation " Comparison to manually-‐labelled data on 8 french forums " Results for a priori evaluation:

"   F1-‐Measure ranges from 0.2 to 0.3 for a single criterion "   F1-‐Measure equals 0.48 for aggregated criteria (simple mean)

" Results for a posteriori evaluations:

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1 [Stavrianou et al.,09,10]



Extracting the (latent) social network

"   Latent SN = reply-to links + name citation + text quotation "   Name citation: bad spelling, compound names, abbreviations…

(what about “obama49”?) "   Our solution: edit distance, soundex, PoS to detect nouns

"   Text quotation: cut-paste without quotation marks, rephrasing… "   Our solution: string matching, locality principle (comparing close

messages), use quotation marks if provided

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2 [Forestier et al.,11]



Detecting celebrities

" Modeling the forum discussion with a graph G=(V,E) " vertice v = forum participant " edge e = link (implicit or explicit) between two participants

" Weighted in-degree of v: deg-(v) " Weighted out-degree of v: deg+(v) "   p(v) = set of messages posted by v "   p~ = average of messages " thr(v) = set of threads not initiated by v

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3 [ForesCer et al.,12]


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"   Extracting social roles from a SN is a key issue [Fisher et al.,06] [Himelboim et al.,09] [Forestier et al.,12]

"   Some examples of roles: "   Leader: very participative user, who initiates discussion threads and

makes the animation

"   Expert: user particularly active in a restrictive number of topics "   Celebrity: public person well known by the participants " Flammer: user with a negative behavior, who can generate conflicts "   Lurker: user who has a low participation in the discussion

"   In the following, we have chosen to focus on the explicit “celebrity” role within online discussion forums

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" Formalize the criteria given by [Golder and Donath,04]

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"   Based on these atomic criteria, we define 3 meta-criteria: "   meta-criterion 1: all the basic criteria must be satisfied (necessary

conditions), and we rank the interesting users in descending order relative to the total number of posts

"   meta-criterion 2: id. but with a ranking depending on the user’s average forum participation multiplied by the number of posts

"   meta-criterion 3: id. but taking into account name citation and text quotation

"   Evaluation measure: compare the ranking of our meta-criteria with the number of fans of each user (>800) = gold standard

"   Dataset: "   57 forums from the US version of the Huffington Post "   3 topics: politics, media, living "   Overall 11,443 unique users and 35,175 posts

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eminar at housie University – 4/18/2013 – ulien elcin 24

[Forestier et al.,12]


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Surfacing roles

"   New collaboration between and

"   Bottom-up “emerging” roles:

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Axe Carrés 2 ter

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Surfacing roles

"   Discussions about 6 popular TV shows from TWOP forums

"   Parent-child relationship is restored using “quote” mechanism: "   check previous 20 messages in the thread; "   a parent has to contain at least 95% of the quoted text.

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Surfacing roles

" Profiling users using temporal-aware features: " weighted in-degree, " weighted out-degree, " node in-g-index, " node out-g-index, " catalytic power, " number of posts, "   cross-topic entropy.

"   The role identification procedure is applied to the time series of feature vectors of 1 263 forum users.

" Using moving time windows (size=1 week, shift=1 day)

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Surfacing roles

"   Clustering time series "   Basic k-means algorithm " Hartigan’s index used for estimating the best k

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[Anokhin et al.,12]

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Surfacing roles

" Some observations:

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Outline


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Section 4 Semi-‐supervised

clustering


Temporal-‐driven clustering

"   Goal: detecting typical patterns over time

"   How to deal with temporally described entities?

"   Applications: "   Evolution of nation’s political

states (proof of concept) "   Trajectories over roles "   Evolution of entities’ images

(c.f. ImagiWeb) 32

φ2

φ1

t1

t2

t3

t1

t2

t3

x1d

x2d

x3d

x4d

x5d

x6d

t2 t3 t1


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Temporal-‐driven clustering

" Detect typical evolution patterns of individuals in the dataset: "   phases through which the entity

collection went over time

" trajectory of entities through the different phases

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Temporal-‐aware constrained clustering

"   The resulted partition must ensure: "   descriptive coherence of clusters; "   temporal coherence of clusters; " continuous segmentation of observations

belonging to an entity

"   Objective function to minimize (inspired by semi-‐supervised clustering clustering [Wagstaff and Cardie,00]) + use of K-‐Means-‐like algorithm:

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Temporal-‐aware dissimilarity measure

ConCguity penalty measure

(a)

(b)

(a) (b)



Experiments on political dataset

"   23 countries, 60 years "   207 political, demographic, social and economic variables "   Running TDCK-‐Means (8 clusters, β = 0.003 and δ = 3)

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Outline


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Section 5 Focus on Project

ImagiWeb hGp://eric.univ-‐lyon2.fr/~jvelcin/imagiweb

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Project ImagiWeb

"   Goal of Project ANR ImagiWeb: analyzing the life cycle (production, diffusion, evolution) of images through the Web 2.0

" Strong points: "   Joint analysis of opinions, topics, social networks… " Involvement of (true) researchers in LLSSH

" Partners: "   ERIC: data mining, machine learning "   LIA: text/opinion mining, information retrieval "   CEPEL: social scientists, specialist in politics study "   XRCE: information extraction, NLP "   AMI Soft.: numeric watch "   EDF R&D: end-‐user, semiology study

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Project ImagiWeb

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Platform for performing the annotation

"   Web applications designed for annotating ~10k tweets + 200 blog comments; 22 annotators are working on it right now!

"   Output: (mφ ; mt; mp ; ma ; mt ; ms )

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Platform for performing the annotation

"   Web applications designed for annotating ~10k tweets + 200 blog comments; 22 annotators are working on it right now!

"   Output: (mφ ; mt; mp ; ma ; mt ; ms )

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Catching image’s evolution over time

"   Input: set of tuples (mφ ; mt; mp ; ma ; mt ; ms ) "   Some good questions:

"   What is an image? "   How to sum up the bunch of (temporally-‐situated and spatially-‐located) opinions?

"   First insight: investigating time series analysis, temporally-‐driven clustering, graphical models…

"   Fortunately we’ll have a fulltime post-‐doc student to work on it!

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Recent work on opinion mining

"   Participation to Sem-‐Eval 2013 " Task 2.B: Discriminating positive (+) from negative (-‐)

opinions (+ neutral) " Very recent work: improving basic NB by using

background knowledge (seed lists) "   6/35 and 3/16 on the official tweet dataset! " Results on our own datasets:

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[paper just submiGed]



Outline


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Context The big picture Online discussions Topics Clustering ImagiWeb Conclusion

Section 6 Future lines of

research

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An integrated view

Research + tools + applications

"   Ongoing Research "   Structured temporal-‐driven clustering (M. A. Rizoiu, PhD student) "   Bridging the gap between topics and concepts (M. A. Rizoiu, PhD student) "   Multi-‐document summarization of online discussions (C. Cercel, PhD student, in

collaboration with the Polytechnic Institute of Bucharest) "   Bottom-‐up, dynamic extraction of roles (A. Lumbreras, PhD students, in

collaboration with Technicolor) "   Dynamic joint extraction of topics and opinions (M. Dermouche, PhD student, in

collaboration with AMI Software) "   Extracting opinionated images from tweets and blogs in an unsupervised way (Y.

Kim, post-‐doc student, in collaboration with LIA)

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An integrated view

"   Tools " MediaMining: a full open-‐access platform for analyzing online discussions

"   Applications "   Reputation Management services

=> Project ImagiWeb, with specialist in political studies (2012-‐2015, ~860k) "   Discourse analysis in public opinion

=> Project DANuM, with linguists (2013-‐2014, 23k) => Project ALICE, with social scientists and specialists in communication

(just-‐submitted) " The next step: datamining-‐based services for “curation support”, with specialist in

communication and journalists

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Focus on the collaboration DAL/Lyon

"   3 possible scientific contributions: " Labeling hierarchical topic models " Labeling dynamic topic models " Visualization of hierarchical/dynamic topic models

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ArCficial Neuronal Network

Neuroscience

OpCmizaCon

Efficiency (staCsCcs)

Learning theory

Vision chip GeneraCve

model

Graphical models

Neural networks

Background

Computer vision

Markov decision process

ComputaConal complexity

theory


References (excerpt)

" Anokhin N., J. Lanagan, J. Velcin (2012), Social Citation: Finding Roles in Social Networks. An Analysis of TV-‐Series Web Forums. Second International Workshop on Mining Communities and People Recommenders (COMMPER), in conjunction with ECML/PKDD, Bristol, UK.

" Dermouche M., J. Velcin, S. Loudcher, L. Khouas (2013), Une nouvelle mesure pour l'évaluation des méthodes d'extraction de thématiques : la Vraisemblance Généralisée. Actes de la 13ème Conférence Francophone sur l'Extraction et la Gestion des Connaissances (EGC). Toulouse, France.

"   Forestier, M., Stavrianou, A., Velcin, J. and Zighed, D.A. (2012), Roles in Social Networks: Methodologies and Research Issues. Web Intelligence and Agent Systems: An International Journal (WIAS).

" Musat, C., Velcin, J., Rizoiu, M.A. and Trausan-‐Matu, S. (2011), Improving Topic Evaluation Using Conceptual Knowledge. Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI). Barcelona, Spain.

" Rizoiu M.A., J. Velcin, S. Lallich (2012), Structuring typical evolutions using Temporal-‐Driven Constrained Clustering. Proceedings of the 24th IEEE Internatinal Conference on Tools with Artificial Intelligence (ICTAI). Athens, Greece. Best student paper award.

" Stavrianou, A., Velcin, J. and Chauchat, J.H. (2009), A combination of opinion mining and social network techniques for discussion analysis. Revue des Nouvelles Technologies de l'Information (RNTI), Cepadues.

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06/05/2013

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Thank you!

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Data mining for analyzing social media

Technology

Transcript of Data mining for analyzing social media