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Raubbau im Ökosystem der Daten? –Welche (Missbrauchs-‐)Möglichkeiten bietet Data-‐Mining in Social Media oder anderen persönlichen Datenräumen?

PD Dr. Georg Groh

Social Computing Research GroupFakulät für Informatik

Social Media

Social Media: Characteristics

● openness: admissability, low technical barriers

● emphasis on user generated content

● emphasis on supportinguserinteraction / communication(especially 1:n or n:m)

● fast dynamics

● users act as prosumers

● social informationprocessingparadigm: collectively solve problems beyond individual capabilities [Lermann2007 in Groh, 2012] → e.g. crowdsourcing, Wikipedia

● emergent social effects: e.g. ○ 2007 Southern California wildfire

[Sutton et al., 2008 in Groh, 2012];

○ Fukushima 2011 radiation levels measurements [par, 2012; in Groh, 2012]

○ Arab Spring phenomenon [DeLong-‐Bas, 2012 ; in Groh, 2012].

Social Media: Characteristics (contd.)

user user

item item

e.g. social relations

e.g. tags, ratings

e.g. tags, folksonomies,semantic metadata

● Users collaboratively explicate / model relations of various kinds:

e.g. tags, ratings

● user ←→ user relations (and someuser←→ item relations) : maybeinterpreted / labeled as Social Context

Social Media Characteristics: Social Context

● Social Context: models of any aspects of social interaction between usersin relation to IT systems (hardware, platforms, services etc.) (andinstantiationsof thesemodels)

○ explicitly provided (example: Facebook friendship) vs. won via sensors + instantiatingmodels (example: Social Situation)

○ short term (example: co-‐activity) vs. long term (example: social network)

○ „within“ the IT system itself (example: Facebook „like“) vs. „outside“ the IT systembut related to it (e.g. used in) (example: mutual emotional attitude of persons usinga tabletop-‐based creativity supportsystem)

○ binary (example: friendship) vs. n-‐ary (example: group)

○ explicit use (example: Facebook friendships controllingaccess) vs. implicit use (ex.: interruptibilitymanagementvia interactiondetection)

Social Media Characteristics: Ultra-‐short Essence

Social Media→easily editable / expandable , socially accessible

Web-‐content + social context

Ultra-‐short Essence:

Social Media Technologies

○ basic Web protocols (e.g. HTTP(S))

○ languages for declarative representation of structure, actual content, and format of content (e.g. HTML5, XML + related (e.g. XSLT)), specialized XML languages (e.g. GML))

○ Semantic Web languages (e.g. RDF(S), OWL, SPARQL), Social Semantic Web Ontologies (e.g. SIOC, FOAF)

○ client-‐side technologies (e.g. Flash, JavaScript, JSON, AJAX, Silverlight)

○ server-‐side technologies (e.g. PHP, JSP, ASP, Ruby on Rails, Spring, Databases)

○ syndication and mash-‐up of content (e.g. RSS, Atom)○ Social Software (e.g. Elgg, MediaWiki)○ …

[iNCBEAT 2013]

general enabler technologies for Social Media: technologies for building general Rich Internet Applications (RIAs) or Web-‐applications (see e.g. [Shklar and Rosen, 2009; in Groh, 2012]):

[SemanticFocus, 2013]

Social Media Classes

Blogs

Microblogs

Wikis

Discus-‐sionBoards

@ (Messa-‐ging)

(IP-‐Tele-‐phony)

(Chat)

Social Games

(Revision Control)

(Content Manage-‐ment)

Open Innova-‐tionplatforms

Collabo-‐rativeCreativity services

(Know-‐ledge Codifi-‐cation)

Social Networ-‐king platforms

Mobile Social Networ-‐king

Location-‐Based S.Netw.

Profes-‐sionalS.Netw.

Corpo-‐rate S.Netw.

PartnerFindingplatf.

C Com-‐munityplatf.

AltruisticCom-‐munityplatf.

PoliticalCom-‐munityplatf.

Eventplatf.

Newsplatf.

Social Search

Quest-‐ionAns-‐wering

Infor-‐mationAggre-‐gation

(Docu-‐mentMgmnt.)

↔ Content Sharing

File Sharing

Video Sharing

Photo Sharing

TeachingMaterial Sharing

Social Book-‐marking

ProductRating

R Recom-‐mender Systems

Social Computing: Coarse Definition

Social Computing:Interdisciplinary field (mostly informatics) investigating, modeling

and using social context (i.e. all aspects of human socialinteraction in / with / around IT systems) in view of increasing

the utilityof the respective IT systems for the users

coarse definition:

Social Computing: Disciplines of Informatics with High Overlap

● Social Signal Processing

● Network Analysis and Social Network Analysis

● Social Network / Social Context Visualization

● Recommender Systems

● Social Media Analysis / Web Science

● Awareness Systems

● (Privacy Management)

● (Game Theory)

● (Robotics, Distributed AI (MAS), Distributed Systems)

● (AI, Machine Learning, „Big Data“ Data-‐Mining)

● (Mobile Computing)

Social Computing / Science w.r.t. to Social Context: Examples

Let‘s take a look at some examples of researchand reserachmethods in Social Computing and some societal issues regarding

social media and Social Computing research

now:

,

Example 0: Collaborative Filtering, Social Filtering

Collaborative Filtering:

𝑟 =

4 − −− − −− 5 5

− − −5 − −− − −

1 2 −1 − −1 − 5

− 9 −− − −− 8 5

− 9 −− − 3− 6 −

− − −3 − −− − 5

1 − −− − −−−−

7−−

6−−

1 − 3− − −11−

6−−

−−4

− 2 −0 − −−9−

−4−

6−6



𝑟 =

4 − −− − −− 5 5

− − −5 − −− − −

1 2 −1 − −1 − 5

− 9 −− − −− 8 5

− 9 −− − 3− 6 −

− − −3 − −− − 5

1 − −− − −−−−

7−−

6−−

1 − 3− − −11−

6−−

−−4

− 2 −0 − −−9−

−4−

6−6users

items



𝑟 =

4 − −− − −− 5 5

− − −5 − −− ? −

1 2 −1 − −1 − 5

− 9 −− − −− 8 5

− 9 −− − 3− 6 −

− − −3 − −− − 5

1 − −− − −−−−

7−−

6−−

1 − 3− − −11−

6−−

−−4

− 2 −0 − −−9−

−4−

6−6users

items

item i

user u



𝑟 =

4 − −− − −− 5 5

− − −5 − −− ? −

1 2 −1 − −1 − 5

− 9 −− − −− 8 5

− 9 −− − 3− 6 −

− − −3 − −− − 5

1 − −− − −−−−

7−−

6−−

1 − 3− − −11−

6−−

−−4

− 2 −0 − −−9−

−4−

6−6users

items

𝒩1 𝑢 : users that rated item i and that are similar to user u, e.g.:

𝒩1 𝑢 = 𝑣1 𝑠𝑖𝑚 𝑢,𝑣1 > 𝛼}where e.g. 𝑠𝑖𝑚 𝑢, 𝑣1 = cos 𝑢, 𝑣1 ~𝑢 ∗ 𝑣1

item i

user u

𝒩1(𝑢)

user u

𝒩1(𝑢)



𝑟 =

4 − −− − −− 5 5

− − −5 − −− ? −

1 2 −1 − −1 − 5

− 9 −− − −− 8 5

− 9 −− − 3− 6 −

− − −3 − −− − 5

1 − −− − −−−−

7−−

6−−

1 − 3− − −11−

6−−

−−4

− 2 −0 − −−9−

−4−

6−6users

itemsnow: predicted rating foritem i of user u

item i



see e.g. [Desrosiers, C., & Karypis, 2011]

user u

𝒩1(𝑢)



𝑟 =

4 − −− − −− 5 5

− − −5 − −− ? −

1 2 −1 − −1 − 5

− 9 −− − −− 8 5

− 9 −− − 3− 6 −

− − −3 − −− − 5

1 − −− − −−−−

7−−

6−−

1 − 3− − −11−

6−−

−−4

− 2 −0 − −−9−

−4−

6−6users

itemsnow: predicted rating foritem i of user u

item i



now: Social Filtering:

replace:rating similarity based 𝒩1 𝑢 and ratingsimilarities 𝑤DE of Collaborative Filtering with friends from social network and tie strengths

→ comparable or better results!→ social serendipity

see e.g. [Desrosiers & Karypis, 2011]

see e.g. [Groh & Ehmig, 2007]

Other Examples fromOur Research

[Perey, 2013]

● Social Information Retrieval

● Social Interaction Geometry

● AvailabilityManagement via Audio-‐BasedSocial Context

● Topical Social Influence

● Social Context and NLP

● Social Capital Management

● Privacy in Social Networking

● Sociotechnical Systems forHealthy Living

Machine Learning / Data-‐Mining

Goal: Find interesting patterns in large sets of data

extract

Find clusters, predict values, classify

train

Data

Patterns / Features

(probabilistic) Model

sensors

Data: Feature-‐/Pattern-‐Extraction Example I

“I like to dance samba, bake pizza, watch tv and plant trees in the garden. I also like to bake cakes.”

I 2 like 2 to 2 dance 1 samba 1bake 2 pizza 1 watch 1 tv 1and 1 plant 1 trees 1in 1 the 1garden 1 also 1cakes 1

Often: Instead of term-‐frequency (tf) alone: use term-‐frequency * inverse document frequency (idf);idf = log (#of docs where t occurs / #of docs)

● here: (abstract) sensor: download fromWeb, direct input, etc.

● feature extraction: tf-‐idf

Data: Feature-‐/Pattern-‐Extraction Example II

[Wikipedia 2016]

● sensor: cameraà images

● feature extractionà Eigenfaces

other example:

● sound à 30ms framesà FFT, filteringàMFCCs

Training Data

extract


train

Data

Patterns / Features


sensors

cases:

● 𝑥1 GHIJ (unsupervised learning)

● (𝑥1 ,𝑦1) GHIJ (regression, supervised

learning)

● (𝑥1 ,𝑦1) GHIJ (classification, supervised

learning)

Training Data

extract


train

Data

Patterns / Features


sensorscases:

● parametricmodels (GMMs, Random Forests, Linear Regression, SVMs, Neuronal Networks etc.) vs non-‐parametricmodels (KNN, DBScan etc.)

● probabilistic vs. non-‐probabilistic

● generative vs. discriminativemodels

● etc.

Linear Regression

[Bishop, 2005]

y y

y y

General Model:

Classification with KNN

[Bishop, 2005]

Classification: Decision Trees

[Bishop, 2005]

Breast Cancer Decision Tree

Classification: Logistic Regression

[Bishop, 2005]

Classification: Naive Bayes

𝑝 𝑥,𝑦 𝜃 = 𝑝 𝑥 𝑦, 𝜃 𝑝 𝑦 𝜃 =N𝑝 𝑥E 𝑦, 𝜃 𝑝 𝑦 𝜃O

EHI

Clustering: K-‐Means

[Bishop, 2005]

Clustering: GMMs

[Bishop, 2005]

𝑝(𝑥|𝜃)

(Deep) Neural Networks: Supervised Learning

[Bishop, 2005]

(Deep) Neural Networks: Unsupervised

[ML1, 2016]

Auto-‐Encoder Network

Example: Tripartite Graphs and Predicting Personality

Facebook Profile

Facebook User

Facebook Item

owns

likes

[Kosinski et al.2013]



pointed to via [Golbeck, 2013]

from

ALONE!!!

Example: Privacy in Social Networking

[CBC, 2013] [Golbeck 2013]

-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐

Bibliography -‐-‐ Main -‐-‐

(1) Jennifer Golbeck: Two Sides of Profiling, keynote talk at SCA 2013, Karlsruhe, Germany, 2013

(2) Georg Groh: „Contextual Social Networking“, Habilitation thesis, TUM Informatics

(3) Kevin Murphy: Machine Learning: a Probabilistic Perspective, MIT Press

(4) Patrick van der Smagt, Georg Groh et al.: Material of Lecture Machine Learning I, TUM, 2013-‐2015

Bibliography -‐-‐ Further Citations -‐-‐

[Wikipedia 2013] Wikipedia article on “Social Media” http://en.wikipedia.org/wiki/Social_media (checked May 2013)[O’Reilly 2005] T. O’Reilly “What is Web2.0” (2005) http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-‐is-‐web-‐20.html (checked May 2013)[O’Reilly 2006] T.O’Reilly Web 2.0 Compact Definition: Trying Again http://radar.oreilly.com/archives/2006/12/web-‐20-‐compact-‐definition-‐tryi.html (checked May 2013)[Lermann 2007] Kristina Lerman (2007), Social Information Processing in Social News Aggregation, Extended version of the paper in IEEE Internet Computing special issue on Social Search 11(6), pp.16-‐28, 2007 http://www.isi.edu/~lerman/papers/lerman07ic.pdf (checked May 2013)[Open Social, 2012] (Google) Open Social Initiativehttp://opensocial.org/ (checked May 2013)[Peerson, 2013] Peerson P2P Social Networking Initiative http://www.peerson.net/ (checked May 2013)[Bizer, 2009] Bizer, C., Heath, T., & Berners-‐Lee, T. (2009). Linked data-‐the story so far. International Journal on Semantic Web and Information Systems (IJSWIS), 5(3), 1-‐22.http://eprints.soton.ac.uk/271285/1/bizer-‐heath-‐berners-‐lee-‐ijswis-‐linked-‐data.pdf (checked May 2013)[NN, 2013] http://winfwiki.wi-‐fom.de/index.php/Anwendungsm%C3%B6glichkeiten_von_Semantic_Web_in_sozialen_Netzen (checked May 2013)[SIOC, 2013] SIOC Project Website http://sioc-‐project.org (checked May 2013)[OPO, 2013] Online Presence Ontology Website http://online-‐presence.net (checked May 2013)


[iNCBEAT, 2013] iNCBEAT Websitehttp://www.incbeat.com/resources/web-‐technologies-‐businesses (checked October 2013)[SemanticFocus, 2013] Semantic Focus Website http://www.semanticfocus.com/blog/entry/title/introduction-‐to-‐the-‐semantic-‐web-‐vision-‐and-‐technologies-‐part-‐1-‐overview/ (checked October, 2013)[Perey, 2013] PereyWebsitehttp://www.perey.com/images/social_networking.jpg (checked October, 2013)[Desrosiers & Karypis 2011] Desrosiers, C., & Karypis, G. (2011). A comprehensive survey of neighborhood-‐based recommendation methods. In Recommender systems handbook (pp. 107-‐144). Springer US.[Groh & Ehmig, 2007] Georg Groh and Christian Ehmig. 2007. Recommendations in taste related domains: collaborative filtering vs. social filtering. In Proceedings of the 2007 international ACM conference on Supporting group work (GROUP '07). ACM, New York, NY, USA, 127-‐136. [URI, 2013] http://www.math.uri.edu/~merino/fall06/mth215/Adjacency.html (checked October, 2013)

[Granovetter, 1973] Mark Granovetter: The Strength of Weak Ties. In: American Journal of Sociology 78 (1973), S. 1360–1380.

[CBC, 2013] CBC News Articlehttp://www.cbc.ca/news/canada/montreal/depressed-‐woman-‐loses-‐benefits-‐over-‐facebook-‐photos-‐1.861843(checked October 2013)

[Groh 2012] Georg Groh: Contextual Social Networking, Habilitation thesis, TUM Informatics, 2012[Golbeck 2013] Jennifer Golbeck: Two Sides of Profiling, keynote talk at SCA 2013, Karlsruhe, Germany, 2013

[Kendon, 1990] Adam Kendon: Conducting Interaction: Patterns of Behavior in Focused Encounters, CUP Archive, 1990

[Kosinski et al.2013] Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15), 5802-‐5805.


[Wikipedia, 2016] https://de.wikipedia.org/wiki/Datei:Eigenfaces.png (URL, 2016)[ML1, 2016] Machine Learning 1 Lecture TUM, 2015 / 2016[Bishop, 2005] C. Bishop: Pattern Recognition andMachine Learning, Springer 2005[Murphy, 2013] K. Murphy: Machine Learning: a Probabilistic Perspective, MIT Press, 2013

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