TwitterVizDemo

h1. Wiki

h2. Description

Micro-blogging platforms and social networks have gained a lot of attention both by the media as well as by researchers. The spatio temporal information provided explicitly or implicitly by the users of such platforms, as well as additional information that can be mined from the social network's structure, render these types of platforms a rich source of information that can be used to make more timely and effective decisions based on the users' behavior. A lot of work has been done towards the creation of frameworks for Twitter analytics, but these solutions usually focus on specific tasks. The lack of complete and integrated solutions as recognized by current research leads us to the proposal of TwitterViz a complete solution for the visualization and analysis of spatio-temporal twitter data in combination with the analysis of the twitter graph, by leveraging the use of a popular graph database and using state of the art visualization tools that aim in providing insights to the non-expert.

h2. Video Demonstration

A video demonstration is available to showcase the various functionalities of TwitterViz.

"TwitterViz Video Demonstration":https://www.dropbox.com/s/0vsrsod25m26ikm/demo.mp4?dl=0

h2. TwitterViz

Access the current version of TwitterViz here:

"TwitterViz v0.1":https://www.dropbox.com/s/0vsrsod25m26ikm/demo.mp4?dl=0

h2. TwitterViz System Description

*Spatio-Temporal Analysis*

* Range queries on the map to visualize tweets from specific areas.

* Visualization of sentiment on tweets on the map in specific geographic areas using a defined visual syntax. The user can investigate how the sentiments change in specific areas as well as how they change in time, by also applying other restrictions based on the social network's structure.

* Visualization of a user's followers' tweets on the map, combining information from the graph.

* Visualization and study of the temporal evolution of tweets in user-defined time windows.

* Analysis of the spatio-temporal distribution of tweets.

* Presentation of a variety of real-time statistics on the streaming data.

*Graph Mining*

* Defined Cypher queries for the non-expert for graph exploration.

* Support for custom queries on the graph for more complex analysis, such as pattern matching queries on the graph. Custom queries can be formulated easily and can combine the tweets' geo-social characteristics.

* Visualization of the results for all of the queries on the graph.

* Presentation of a variety of statistics for real-time graph analysis.

h2. Examples of Use

*Example Queries*

1.MATCH (n:HashTag)-[r:MATCHES]->(h:HashTag) RETURN n,h order by r.Frequency desc limit 10

Βρίσκει τα Hashtags που εμφανίζονται πιο συχνά μεταξύ τους στο ίδιο tweet

2.START n=node:tweetWKT('withinDistance:[37.976772,23.726039, 0.5]') MATCH (n)-[]-(h:HashTag) WHERE h.Hashtag='athens' RETURN n,h

Βρίσκει τα tweets σε ακτίνα μισού χιλιομέτρου από το κέντρο της Αθήνας με το Hashtag 'athens'

3.MATCH (n:User)<-[:FOLLOWS]-(m:User), (n)-[]->(t:Tweet) WHERE t.Region='NewYork' WITH n,count(m) AS total  RETURN n ORDER BY total DESC limit 10

Βρίσκει του χρήστες της Νέας Υόρκης με τους περισσότερους followers

4.start n=node:tweetWKT('bbox:[-1.0,24.0, 37.5,52.0]') MATCH (n)-[]->(h:HashTag) WHERE h.Hashtag='fun' return n,h

Βρίσκει τα tweets που έχουν το hashtag 'fun' και γίνονται στην Ευρώπη (Λονδίνο, Άμστερνταμ, Αθήνα)

5.MATCH (u:User)-[:TWEETS]->(t:Tweet)-[:HAS]->(h:HashTag) WHERE h.Hashtag = 'london' WITH u,h,count(t) as total RETURN u,h order by total DESC limit 5

Βρίσκει τους χρήστες που χρησιμοποιούν συχνότερα το hashtag 'london'

h2. Publications