Linked Open Social Signals
From Knoesis wiki
We explore the symbiosis of real time analysis and linked data. Representing social signals as structured data will enable flexibility in handling the information overload of those interested in collectively analyzing social signals for sensemaking.
This is work in progress by Pablo N. Mendes (Kno.e.sis), Alex Passant (DERI), Pavan Kapanipathi (Kno.e.sis) and Dr. Amit P. Sheth (Kno.e.sis). It builds upon Twitris and SMOB.
Contents
[hide]Quick Info
- Real Time: the load estimate for the health care topic drinking from the firehose is
- 1 post per second
- 35K triples per hour (tph) or 10 triples per second, steady over HTTP SPARQL Update. Feasible?
- Writeup: Get it from here.
Pitching
Some motivation: decentralized semantic microblogging.
- Information Overload
- As reported by ReadWriteWeb recently, during an emergency it’s practically impossible to get status updates on things like roads, hospitals, airports, and people using Twitter [1]
- Twitter as a poor vehicle for marketing [2]. Many people make up hashtags as they tweet, exploding the semantic graph, creating more semantic dispersion. Some promising new tools that can help you quickly put a hashtag in context — or let people easily look up the meaning of the hashtags you launch or use [3]
- Wouldn’t it be cool if Twitter had a topic backbone and you could snap your tweets to it as you write them? [4]
- Competition
- These companies call it real-time web intelligence,
- http://www.evri.com/
- http://www.ellerdale.com/
- Push vs Pull
- Siegel’s rule for information life span: The half-life relevance of a piece of pushed information is about the same as the frequency of the medium. [5]
- Twitter developed a new set of frameworks @anywhere for adding this Twitter experience anywhere on the web. Imagine being able to follow a New York Times journalist directly from her byline, tweet about a video without leaving YouTube, and discover new Twitter accounts while visiting the Yahoo! home page—and that’s just the beginning. [6]
- Persistent Search: http://billburnham.blogs.com/burnhamsbeat/2006/04/persistent_sear.html
- Siegel’s rule for information life span: The half-life relevance of a piece of pushed information is about the same as the frequency of the medium. [5]
Related
- Bibliography of Research on Twitter & Microblogging [7]
At Kno.e.sis
- Social Signals @kno.e.sis
- A. Sheth, Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A comprehensive path towards event monitoring and situational awareness, February 17, 2009.
- A. Sheth, Citizen Sensing, Social Signals, and Enriching Human Experience- IEEE Internet Computing, July/August 2009.
- Meenakshi Nagarajan, Karthik Gomadam, Amit P. Sheth, Ajith Ranabahu, Raghava Mutharaju, Ashutosh Jadhav: Spatio-Temporal-Thematic Analysis of Citizen Sensor Data: Challenges and Experiences. WISE 2009: 539-553 [8]
At DERI
- Microblogging: A Semantic Web and Distributed Approach (Paper at SFSW2008)
- A Distributed Semantic Microblogging Platform (Challenge submission SFSW2008)
Semantic Microblogging
- http://smob.me
- http://semantictweet.com
- http://smesher.org/
- http://openmicroblogging.org/
- http://status.net/
- Short and Tweet: Experiments on Recommending Content from Information Streams [9]
- Cheng. Fall'09 class project at iSchool (Berkeley). Classifying Metatweets pdf
- Klout on health care: [10]
- Topsy on health care: [11]
- Krishnamurty, Gill, Arlitt. SIGCOMM'08. A few chirps about twitter. pdf
- classifies 100,000 users in broadcasters, acquaintances, miscreants or evangelists.
