NUI Galway – UL Alliance First Annual ENGINEERING AND - ARAN ...

A Real-Time Tweet Diffusion Advisor for #Twitter 1
Peyman Nasirifard and Conor Hayes
Digital Enterprise Research Institute
National University of Ireland, Galway
firstname.lastname@deri.org
1 This abstract is based on [Peyman Nasirifard, Conor Hayes, A Real-Time Tweet Diffusion Advisor for #Twitter. In Proceedings of the ACM DL
on the 2011 ACM Conference on Computer Supported Cooperative Work (CSCW), ACM, 2011]
Abstract
In this paper we describe our novel Twitter assistant
called Tadvise. Tadvise helps users to know their
Twitter communities better and also assists them to
identify community hubs for propagating their
community-related tweets.
1. Introduction
Twitter is the current big thing with hundred millions
of users so called twitterers, who tweet more than 140
million times per day [2]. In such environments, where
there exist massive information flow, users require
personalized assistance to help them to get the most
relevant information and at the same time limit nonrelevant
information. To this end, we developed a novel
Twitter assistant that helps users to know their followers
better and also to find potential topic-sensitive hubs,
who can efficiently propagate a tweet further in the
network. In the following, we briefly describe Tadvise
and its functionalities.
2. Tadvise: Twitter Assistant
To register for Tadvise, a twitterer u simply chooses
to follow the Tadvise Twitter account (i.e., @Tadvise).
Once notified, Tadvise crawls the social network of u
and builds appropriate user profiles. After completing
these steps, which are performed offline, Tadvise sends
a direct message to u, indicating that it is ready to
provide advice. By visiting the Tadvise homepage, u
can benefit from advice and/or tweet a message directly
to Twitter. Tadvise uses a traffic light metaphor to
indicate its advice. A green light advises users to
(re)tweet a message. The red light advises users not to
(re)tweet a message. The amber light indicates that we
cannot decide either way and the decision is left to the
user. The user can over-ride the recommendation at any
time.
Tadvise has three main components: The Crawling
component of Tadvise gets a seed as input and uses
Twitter API and white-listed Twitter accounts for
crawling twitterers. The User Profile Builder
component builds appropriate user profiles based on
crawled information. Finally the Advice Engine
component gets the user profiles and a tweet as inputs
and provide real-time advice based on the traffic light
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metaphor. It also adds potential hubs to the tweet
automatically. Such hubs, if retweet an original tweet,
help to propagate a tweet more efficiently in
Twittersphere.
Schafer et al. [1] argue that it is useful to persuade
users that the provided recommendations are useful. In
order to convince end users that our recommendations
are relevant, we provide simple text-based explanations.
Our explanations originate from the processes that we
use for giving advice. In other words, we show the list
of potentially interested Twitter users at distances of 1
and 2 of a seed (i.e., followers plus followers of the
followers of the seed) and also justify how our
recommended hubs can propagate a tweet further in the
network. We also present a ranked list of potentially
interested Twitter users at distance of 2 of the seed, who
can not receive the tweet via the recommended hubs.
The seed can freely add such (top-ranked) Twitter users
to the tweet (i.e., direct message), in order to attract
their attention.
Our evaluation shows that Tadvise helps users to
know their followers better and also to find better hubs
for propagating their tweets.
A video that describes functionalities of Tadvise can
be accessed at [3].
Acknowledgements
This work is partially supported by Science Foundation
Ireland (SFI) under Grant No. SFI/08/CE/I1380 (Lion-2
project).
References
[1] J.B. Schafer, D. Frankowski, J. Herlocker, and S.
Sen. “Collaborative filtering recommender systems”. In
the Adaptive Web, volume 4321 of Lecture Notes in
Computer Science, pages 291-324. Springer
Berlin/Heidelberg, 2007.
[2] Twitter Official Blog:
http://blog.twitter.com/2011/03/numbers.html.
[3] Tadvise – Twitter Assistant:
http://vimeo.com/13907852