Twitter related problems:
Relation Extraction (Wang 2011)
Classification (Jain 2014)
Event Tracking (Pohl 2013)
Geo search and visualization (Gao 2013)
Trend Mining (Desmier 2013)
Rezidual Analysis of Attributed Graphs (Miller 2013)
Detection Theory (Miller 2013)
Spectral analysis of graphs
Tensors (Miller 2013)
Problems addresed in the framework of DSP on graphs:
Mathematical model of Twitter as a dynamic attributed graph with streams attached to vertexes.
Twitter diffusion of topics or hash terms.
Quering a corpus of dependencies parses of sentences viewed as graphs. (Miller 2012)
Integrated search of documents, multimedia archives and geographic data.
Detection Theory on twitter graphs.
Event Detection. (Dong 2014)
Early Warning System
Big Data Implementation.
Big Data Application Layer for Graphs:
Remove high frequency words as in Lucene
Adapteva or GPU/matlab. http://www.mathworks.com/discovery/gpu-signal-processing.html Use data from SEMEVAL 2014 for sentence semantic relatedness. Dependency parsing based links as Walsh codes, capture relation between words expressed by a vector (word2vec). Unified search from RDF Graphs and unstructured text. Use iconic environment (m3data or Simulink). Study deeplearning4J twitter application. To draw dependency graphs: DependenSee A Dependency Parse Visualisation Tool that makes pictures of Stanford Dependency output. By Awais Athar. (http://nlp.stanford.edu/software/lex-parser.shtml#Sample). Form a document signal by concatenate sentences associated signals. A dependancy graph link source is encoded by a Walsh code and the destination by the code obtained by a rotation with -90 degree. Question-Answering as a decoding-encoding problem or filter(docs)/Fourier(search). Apply at multimedia annotation or unified searching, encryption and watermarking.
Jive search with relations represented as database tabels in M3Data (Campbell 2013), community detection, leadership... implemented as M3Data big data (AROM) by Apache Crunch on top of Spark with collaborative interface by NoFlo