7,948 research outputs found
Extracting News Events from Microblogs
Twitter stream has become a large source of information for many people, but
the magnitude of tweets and the noisy nature of its content have made
harvesting the knowledge from Twitter a challenging task for researchers for a
long time. Aiming at overcoming some of the main challenges of extracting the
hidden information from tweet streams, this work proposes a new approach for
real-time detection of news events from the Twitter stream. We divide our
approach into three steps. The first step is to use a neural network or deep
learning to detect news-relevant tweets from the stream. The second step is to
apply a novel streaming data clustering algorithm to the detected news tweets
to form news events. The third and final step is to rank the detected events
based on the size of the event clusters and growth speed of the tweet
frequencies. We evaluate the proposed system on a large, publicly available
corpus of annotated news events from Twitter. As part of the evaluation, we
compare our approach with a related state-of-the-art solution. Overall, our
experiments and user-based evaluation show that our approach on detecting
current (real) news events delivers a state-of-the-art performance
Extracting semantic entities and events from sports tweets
Large volumes of user-generated content on practically every major issue and event are being created on the microblogging site Twitter. This content can be combined and processed to detect events, entities and popular moods to feed various knowledge-intensive practical applications. On the downside, these content items are very noisy and highly informal, making it difficult to extract sense out of the stream. In this paper, we exploit various approaches to detect the named entities and significant micro-events from users’ tweets during a live sports event. Here we describe how combining linguistic features with background knowledge and the use of Twitter-specific features can achieve high, precise detection results (f-measure = 87%) in different datasets. A study was conducted on tweets from cricket matches in the ICC World Cup in order to augment the event-related non-textual media with collective intelligence
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