Discovering
Emerging Topics in Social Streams via Link-Anomaly Detection
ABSTRACT
Detection
of emerging topics is now receiving renewed interest motivated by the rapid
growth of social networks. Conventional-term-frequency-based approaches may not
be appropriate in this context, because the information exchanged in social network
posts
include not only text but also images, URLs, and videos. We focus on emergence
of topics signaled by social aspects of theses networks. Specifically, we focus
on mentions of users—links between users that are generated dynamically
(intentionally or unintentionally)
through replies, mentions, and retweets. We propose a probability model of the mentioning
behavior of a social network user, and propose to detect the emergence of a new
topic from the anomalies measured through the model. Aggregating anomaly scores
from hundreds of users, we show that we can detect emerging topics only based
on the reply/mention relationships in social-network posts. We demonstrate our
technique in several real data sets we gathered from Twitter. The experiments
show that the proposed mention-anomaly-based approaches can detect new topics
at least as early as text-anomaly-based approaches, and in some cases much
earlier when the topic is poorly identified by the textual contents in posts.
1.
Goal
of Project:
The
main aim of this paper is
Our
goal is to detect emerging topics as early as the keyword-based methods
Our
goal is to evaluate whether the proposed approach can detect the emergence of
the topics recognized and collected by people.
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