490 research outputs found
Winter is here: summarizing Twitter streams related to pre-scheduled events
Pre-scheduled events, such as TV shows and sports games, usually garner considerable attention from the public. Twitter captures large volumes of discussions and messages related to these events, in real-time. Twitter streams related to pre-scheduled events are characterized by the following: (1) spikes in the volume of published tweets reflect the highlights of the event and (2) some of the published tweets make reference to the characters involved in the event, in the context in which they are currently portrayed in a subevent. In this paper, we take advantage of these characteristics to identify the highlights of pre-scheduled events from tweet streams and we demonstrate a method to summarize these highlights. We evaluate our algorithm on tweets collected around 2 episodes of a popular TV show, Game of Thrones, Season 7.Published versio
Indirect Match Highlights Detection with Deep Convolutional Neural Networks
Highlights in a sport video are usually referred as actions that stimulate
excitement or attract attention of the audience. A big effort is spent in
designing techniques which find automatically highlights, in order to
automatize the otherwise manual editing process. Most of the state-of-the-art
approaches try to solve the problem by training a classifier using the
information extracted on the tv-like framing of players playing on the game
pitch, learning to detect game actions which are labeled by human observers
according to their perception of highlight. Obviously, this is a long and
expensive work. In this paper, we reverse the paradigm: instead of looking at
the gameplay, inferring what could be exciting for the audience, we directly
analyze the audience behavior, which we assume is triggered by events happening
during the game. We apply deep 3D Convolutional Neural Network (3D-CNN) to
extract visual features from cropped video recordings of the supporters that
are attending the event. Outputs of the crops belonging to the same frame are
then accumulated to produce a value indicating the Highlight Likelihood (HL)
which is then used to discriminate between positive (i.e. when a highlight
occurs) and negative samples (i.e. standard play or time-outs). Experimental
results on a public dataset of ice-hockey matches demonstrate the effectiveness
of our method and promote further research in this new exciting direction.Comment: "Social Signal Processing and Beyond" workshop, in conjunction with
ICIAP 201
Analysis and Forecasting of Trending Topics in Online Media Streams
Among the vast information available on the web, social media streams capture
what people currently pay attention to and how they feel about certain topics.
Awareness of such trending topics plays a crucial role in multimedia systems
such as trend aware recommendation and automatic vocabulary selection for video
concept detection systems.
Correctly utilizing trending topics requires a better understanding of their
various characteristics in different social media streams. To this end, we
present the first comprehensive study across three major online and social
media streams, Twitter, Google, and Wikipedia, covering thousands of trending
topics during an observation period of an entire year. Our results indicate
that depending on one's requirements one does not necessarily have to turn to
Twitter for information about current events and that some media streams
strongly emphasize content of specific categories. As our second key
contribution, we further present a novel approach for the challenging task of
forecasting the life cycle of trending topics in the very moment they emerge.
Our fully automated approach is based on a nearest neighbor forecasting
technique exploiting our assumption that semantically similar topics exhibit
similar behavior.
We demonstrate on a large-scale dataset of Wikipedia page view statistics
that forecasts by the proposed approach are about 9-48k views closer to the
actual viewing statistics compared to baseline methods and achieve a mean
average percentage error of 45-19% for time periods of up to 14 days.Comment: ACM Multimedia 201
A rule dynamics approach to event detection in Twitter with its application to sports and politics
The increasing popularity of Twitter as social network tool for opinion expression as well as informa- tion retrieval has resulted in the need to derive computational means to detect and track relevant top- ics/events in the network. The application of topic detection and tracking methods to tweets enable users to extract newsworthy content from the vast and somehow chaotic Twitter stream. In this paper, we ap- ply our technique named Transaction-based Rule Change Mining to extract newsworthy hashtag keywords present in tweets from two different domains namely; sports (The English FA Cup 2012) and politics (US Presidential Elections 2012 and Super Tuesday 2012). Noting the peculiar nature of event dynamics in these two domains, we apply different time-windows and update rates to each of the datasets in order to study their impact on performance. The performance effectiveness results reveal that our approach is able to accurately detect and track newsworthy content. In addition, the results show that the adaptation of the time-window exhibits better performance especially on the sports dataset, which can be attributed to the usually shorter duration of football events
REAL-TIME EVENT SUMMARIZATION OF ESPORTS EVENT THROUGH TWITTER STREAMS
Exploration of real-time summarization (RTS) methodologies and applications to esports events on Twitter. The goal of this study is to evaluate the effectiveness of real-time summarization techniques at esports event detection, highlight summarization, and timeline generation. A two-step system of event-prediction and summarization is proposed. First, using Twitter as the data source, events in an esports game are predicted through machine-learning and classification to determine event occurrences. Four major classification features and five standard classification models (Naive Bayes, Logistic Regression, Decision Trees, K-Nearest Neighbors, Support Vector Machines) are evaluated to identify an optimal event-detection model. Second, natural-language text processing functions such as term-frequency and TF.IDF are evaluated for effective event summarization and to confirm successful event-detection. The CART (classification and regression tree) model is selected as the most optimal model for event-detection, predicting in-game esports events with 70% accuracy. This study demonstrates the application of Twitter as a data source in detecting real-time esports events.Master of Science in Information Scienc
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