Research frontiers in online social media studies

Understanding content, user behaviors and information diffusion

Emilio Ferrara Center for Complex Networks and Systems Research

School of Informatics and Computing Indiana University Bloomington

August 8, 2013 Summer Workshop on Algorithms and Cyberinfrastructure for large scale optimization/AI

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

Data Collection • Twitter Streaming API (10% sample of total traffic)

• August, 2010 – present

• ~5TB Compressed

• Real-time access to data from last 9 months related

to 3 themes: US Politics, Social Movements, News

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

August 8, 2013

Detecting early signatures of persuasion in information cascades

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

Scope of the project • Data acquisition in streaming scenario from

Social Media (Twitter, FB)

• Extraction of information tokens, so-called

memes

• Clustering of memes

• Meme clusters classification

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

Architecture

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

Problem statement • Goal: clustering a large volume of tweets, in a

streaming scenario, in topics based on their

similarity.

• Challenges: tweets text is too sparse for

classification, we need to exploit further features:

• Network structure

• Temporal signature

• Meta-data

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

Meme definition

• @Mention: the user addresses another user mentioning its username

(Twitter syntax: @)

• #Hashtag: the user tags its message with a “concept” (syntax: #)

• URL: a message can include one/multiple URL(s) in extended or

shortened format

• Phrase: whatever remains after removing mentions, hashtags and

URLs, stemming verbs/nouns, removing stop-words and punctuation.

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

Advantages of using memes • More granularity: each tweet is assigned to at least one (or more)

memes

• Efficiency in real-time scenario: each incoming tweet is directly assigned to its meme/s without additional overhead

• Memes can be aggregated each other forming clusters of topics related by content/structure similarity

• We define a set of similarity measures:

• Common user similarity

• Common tweets similarity

• Common document similarity

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

Network, memes & content relations

• Social network

• Memes

• Content

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

Meme similarity measures

• Common users similarity

• Common tweet similarity

• Content similarity

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

CUS: Common User Similary Cosine similarity:

= 0.77

Example:

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

Example: Pmeme1 = {tweet1, tweet2, tweet5} Pmeme2 = {tweet1, tweet2, tweet5, tweet6}

CTS: Common Tweet Similarity

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

CDS: Common Document Similarity

We use once again the cosine similarity but using the TF-IDF matrix

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

Linear combination similarity score:

Example: CTS(i,j) = 0.8 CUS(i,j) = 0.7 CDS(i,j) = 0.9 Different weighting schema:

L(i,j) = 0.5 * 0.8 + 0.5 * 0.9 = 0.85 L(i,j) = 0.33 * 0.8 + 0.33 * 0.7 + 0.33 * 0.9 = 0.79 L(i,j) = 1 * 0.9 = 0.9 (MAX)

Linear Combination

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

Evaluation method Building a ground-truth dataset:

• Thematic, hand-picked keywords (Twitter trends)

• ~ 2K tweets with keywords classified as trending

• All tweets collected from a given day

• The dataset contains 9 different classes (imbalanced size)

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

Goals 1.Determining how much correct is our clustering

solution compared to the ground truth.

2.Determining the best trade-off in the clustering

algorithm configuration considering:

• Quality of obtained partitioning

• Number of obtained clusters

• Size of obtained clusters

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

Evaluation metrics

Mutual information: Normalized mutual information:

Individual entropies: H(X), H(Y) Joint entropy: H(X,Y) Conditional entropies: H(X|Y), H(Y|X)

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

Clustering algorithms • We investigated different clustering algorithms:

• Static hierarchical clustering

• Hierarchical stream-clustering

• Online K-means clustering

• Algorithms have been evaluated against each

other to determine:

• Tweet clustering vs. meme clustering

• Best clustering algorithm

• Content vs. network features

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

Stream-clustering quality

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

Cluster Number/Size

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

Summary • Efficient clustering must exploit meta-data, content

and network information

• Introduction of memes (hashtags, mentions, URLs, phrases) as a ‘pre-clustering’ step improves performance

• Efficient, scalable, robust clustering algorithm adaptable for working in streaming scenario

• Room for performance improvement adding further

features and exploiting parameter tuning for similarity measures

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI

Current/Future Work • Design of a distributed architecture supporting:

o Distributed MapReduce-based storage with replication; currently evaluating:

• Hadoop/Hbase

• Riak

• Cassandra/Solandra

o Large data-storage required:

• ~50M tweets/day (increasing) ~300G/day

uncompressed data

• Data compression (gz, lzw), support for JSON

o Low latency, data-storage & access & analysis in close-to-real-time scenario

August 8, 2013

Summer Workshop on Algorithms

and Cyberinfrastructure for large

scale optimization/AI