Material to begin with Social Network Research

Here, I like to share with you some in my perspective very essential material to prepare for Social Network research projects.

Online Courses

Natural Language Processing: https://class.coursera.org/nlp/lecture

• Week 1 Basic Text Processing
• Week 1 Edit Distance
• Week 3 Text Classification
• Week 3 Sentiment Analysis
• Week 4 Named Entity Recognition
• Week 5 POS Tagging
• Week 7 Information Retrieval
• Week 8 Semantics

Machine Learning: https://class.coursera.org/ml-005/lecture/preview

• Week 1 Intro
• Week 1 Linear Regression
• Week 4 Neural Networks (optional)
• Week 5 Neural Networks (optional)
• Week 6 Advice
• Week 6 System Design
• Week 7 SVMs
• Week 8 Clustering
• Week 8 Dim Reduc 
• Week 9 Recommender Systems
• Week 10 Large Scale

Topics and Concepts

• Text Mining
• tf/idf value https://en.wikipedia.org/wiki/Tf%E2%80%93idf
• pagerank http://ilpubs.stanford.edu:8090/422/1/1999-66.pdf

• Clustering https://en.wikipedia.org/wiki/Cluster_analysis
• k-nearest neighbor https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm

• Learning https://en.wikipedia.org/wiki/Pattern_recognition
• Markov Chain https://en.wikipedia.org/wiki/Markov_chain
• Hidden
• Adaptiv

Social Networks https://en.wikipedia.org/wiki/Social_networking_service

Types

• Facebook https://en.wikipedia.org/wiki/Facebook#Website
• Twitter https://en.wikipedia.org/wiki/Twitter#Features
• Linkedin https://en.wikipedia.org/wiki/LinkedIn#Features
• Pinterest https://en.wikipedia.org/wiki/Pinterest#Use
• Blogs https://en.wikipedia.org/wiki/Blog
• Forums https://en.wikipedia.org/wiki/Internet_forum

APIs

• OAuth https://en.wikipedia.org/wiki/OAuth
• Twitter https://dev.twitter.com/docs/api https://dev.twitter.com/docs/streaming-apislinke
• LinkedIn https://developer.linkedin.com/apis
• Facebook https://developers.facebook.com/docs/reference/php/facebook-api

Crawling https://en.wikipedia.org/wiki/Web_crawler

• Apache Nutch https://nutch.apache.org/
• MapReduce http://research.google.com/archive/mapreduce.html
• import.io https://import.io/
• scrappy http://scrapy.org/imp

Papers

http://www.ijircce.com/upload/2014/august/6_AnEfficient.pdf

http://mba.americaeconomia.com/sites/mba.americaeconomia.com/files/feldmangoldenbergnetzer.pdf

Conference Submission Dates for Social Networking, Social Media Analytics, and Data Mining

I have composed a list of conferences by their usual submission month.
So in case you are searching for a venue to submit your work just estimate your progress and then google the web for the specific submission dates /deadlines.

• January
• ICWSM- International AAAI Conference on Weblogs and Social Media
• February
• WebScience- ACM Web Science
• KDD- ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• March
• WI-  IEEE/ACM Web Intelligence
• April
• ASONAM- IEEE/ACM International Conference on Advances in Social Network Analysis and Mining
• May
• CSCW- ACM Conference on Computer Supported Cooperative Work and Social Computing
• June
• CIKM- ACM International Conference on Information and Knowledge Management
• Juli
• ICDE- IEEE International Conference on Data Engineering
• August
• WSDM - ACM International Conference on Web Search and Data Mining
• ADMA- International Conference on Advanced Data Mining and Applications
• September
• CHI- ACM Conference on Human Factors in Computing Systems
• October
• WWW- International World Wide Web Conference
• SDM- SIAM International Conference on Data Mining
• November ...
• December ...

On searching the web for conference dates ratings and locations, I also found a nice Computer Science Conferences website. Check it out !

"News Information Flow Tracking, Yay!" - Suen et al.

Nifty is an evolution of the Memetracker project.
This time they implemented an incremental approach to their meme clustering.
What I specially like about their paper is the massive test data set of over 20 terabyte.
This data set consists of 6.1 billion blog post that they collected over 4 years.
One dry run of their clustering takes hereby less than 5 days.
For detail information please refer to the paper.

"Influence and Correlation in Social Networks" - Anagnostopoulos et al.

The authors describe a statistical test for actions in social networks.
This test helps to distinguish correlation from causality for information propagation in social networks.
Hereby, the author's test first calculates the probability for one user influencing the other user.
Afterwards, they shuffle action times of the user and test again.
To get a detailed insight into the statistical concept please take a look at the paper.

Social Networks and Academic Research

The world is getting faster and faster, but still the most reputation in research is in printed journals.
Now it seams that the times change. There is an upcoming development of social networks for researchers.
These are not like Facebook with sharing pics and useless stuff to procrastinate. Instead the research networks focus on publications and the answer of small research questions in collaborative manner. (see also German Article of Welt-Online)
So check it out, it might become an advantage soon.

researchgate - Social Researcher Network (German startup)
academia - Social Researcher Network (US version)
mendeley - Collaborative Paper Plattform