Leveraging unscheduled event prediction through mining scheduled event tweets

Contains fulltext : 101833.pdf (author's version ) (Open Access)24th Benelux Conference on Artficial Intelligence, 25 oktober 201

Modelling patterns of time and emotion in Twitter #anticipointment

Contains fulltext : 168711.pdf (publisher's version ) (Open Access)Radboud University, 21 maart 2017Promotores : Bosch, A.P.J. van den, Mulken, M.J.P. van217 p

Event detection in Twitter: A machine-learning approach based on term pivoting

Contains fulltext : 132203.pdf (author's version ) (Open Access)The large number of messages on Twitter posted each day provide rich insights into real-world events and public opinion. However, it is difficult to automatically distinguish tweets referring to such events from everyday chatter, and subsequently to distinguish significant events affecting many people from insignificant events. We apply a term-pivot approach to event detection from the Twitter stream. In order to filter out noisy and mundane events, we train a machine learning classifier on several rich features, and rank the events based on classifier confidence. After training and re-training the classifier using manually annotated data, we obtain an F score of 0.79. However, a baseline that only takes into account the frequency of the tweets that refer to an event yields a better F score of 0.86. We argue that performance is highly related to the definition of what makes a significant event, and that human understanding of this concept is not uniform

Sarcastic Soulmates: Intimacy and irony markers in social media messaging

We research the use of sarcasm on Twitter, and show that a computer has more difficulty to detect sarcasm shared among peers than sarcasm shared with any interested audience. This data set features the data used for training machine learning classifiers, and annotations of the output

Estimating the time between Twitter messages and future events

Contains fulltext : 112942.pdf (publisher's version ) (Open Access)We describe and test three methods to estimate the remaining time between a series of microtexts (tweets) and the future event they refer to via a hashtag. Our system generates hourly forecasts. A linear and a local regression-based approach are applied to map hourly clusters of tweets directly onto time-to-event. To take changes over time into account, we develop a novel time series analysis approach that fi rst derives word frequency time series from sets of tweets and then performs local regression to predict time-to-event from nearest-neighbor time series. We train and test on a single type of event, Dutch premier league football matches. Our results indicate that in an 'early' stage, four days or more before the event, the time series analysis produces time-to-event predictions that are about one day off ; closer to the event, local regression attains a similar accuracy. Local regression also outperforms both mean and median-based baselines, but on average none of the tested system has a consistently strong performance through time.Dutch-Belgian Information Retrieval Workshop, DIR-201

Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics

Contains fulltext : 180459pub.pdf (publisher's version ) (Open Access)133 p

The perfect solution for detecting sarcasm in tweets #not

Contains fulltext : 112949.pdf (publisher's version ) (Open Access)To avoid a sarcastic message being understood in its unintended literal meaning, in microtexts such as messages on Twitter.com sarcasm is often explicitly marked with the hashtag ‘#sarcasm’. We collected a training corpus of about 78 thousand Dutch tweets with this hashtag. Assuming that the human labeling is correct (annotation of a sample indicates that about 85% of these tweets are indeed sarcastic), we train a machine learning classifier on the harvested examples, and apply it to a test set of a day’s stream of 3.3 million Dutch tweets. Of the 135 explicitly marked tweets on this day, we detect 101 (75%) when we remove the hashtag. We annotate the top of the ranked list of tweets most likely to be sarcastic that do not have the explicit hashtag. 30% of the top-250 ranked tweets are indeed sarcastic. Analysis shows that sarcasm is often signalled by hyperbole, using intensifiers and exclamations; in contrast, non-hyperbolic sarcastic messages often receive an explicit marker. We hypothesize that explicit markers such as hashtags are the digital extralinguistic equivalent of nonverbal expressions that people employ in live interaction when conveying sarcasm.4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA-2013), 14 juni 201

Predicting time-to-event from Twitter messages

Contains fulltext : 119451.pdf (author's version ) (Open Access)We describe a system that estimates when an event is going to happen from a stream of microtexts on Twitter referring to that event. Using a Twitter archive and 60 known football events, we train machine learning classifiers to map unseen tweets onto discrete time segments. The time period before the event is automatically segmented; the accuracy with which tweets can be classified into these segments determines the error (RMSE) of the time-to-event prediction. In a cross-validation experiment we observe that support vector machines with chi-squared feature selection attain the lowest prediction error of 52.3 hours off. In a comparison with human subjects, humans produce a larger error, but recognize more tweets as posted before the event; the machine-learning approach more often misclassifies a ‘before’ tweet as posted during or after the event.25th Belgium-Netherlands Artificial Intelligence Conference (BNAIC-2013), 6 november 201

Timely identification of event start dates from Twitter

Contains fulltext : 135169.pdf (publisher's version ) (Open Access

Automatically identifying periodic social events from Twitter

Many events referred to on Twitter are of a periodic nature, characterized by roughly constant time intervals in between occurrences. Examples are annual music festivals, weekly television programs, and the full moon cycle. We propose a system that can automatically identify periodic events from Twitter in an unsupervised and open-domain fashion. We first extract events from the Twitter stream by associating terms that have a high probability of denoting an event to the exact date of the event. We compare a timeline-based and a calendar-based approach to detecting periodic patterns from the event dates that are connected to these terms. After applying event extraction on over four years of Dutch tweets and scanning the resulting events for periodic patterns, the calendar-based approach yields a precision of 0.76 on the 500 top-ranked periodic events, while the timeline-based approach scores 0.63