F.A. Kunneman

The (Un)Predictability of Emotional Hashtags in Twitter

Hashtags in Twitter posts may carry different semantic payloads. Their dual form (word and label) may serve to categorize the tweet, but may also add content to the message, or strengthen it. Some hashtags are related to emotions. In a study on emotional hashtags in Dutch Twitter posts we employ machine learning classifiers to test to what extent tweets that are stripped from their hashtag could be reassigned to this hashtag. About half of the 24 tested hashtags can be predicted with AUC scores of .80 or higher. However, when we apply the three best-performing classifiers to unseen tweets that do not carry the hashtag but might have carried it according to human annotators, the classifiers manage to attain a precision-at-250 of .7 for only two of the hashtags. We observe that some hashtags are predictable from their tweets, and strengthen the emotion already expressed in the tweets. Other hashtags are added to messages that do not predict them, presumably to provide emotional information that was not yet in the tweet.

Open-domain extraction of future events from Twitter

Explicit references on Twitter to future events can be leveraged to feed a fully automatic monitoring system of real-world events. We describe a system that extracts open-domain future events from the Twitter stream. It detects future time expressions and entity mentions in tweets, clusters tweets together that overlap in these mentions above certain thresholds, and summarizes these clusters into event descriptions that can be presented to users of the system. Terms for the event description are selected in an unsupervised fashion. 1 We evaluated the system on a month of Dutch tweets, by showing the top-250 ranked events found in this month to human annotators. Eighty per cent of the candidate events were indeed assessed as being an event by at least three out of four human annotators, while all four annotators regarded sixty-three per cent as a real event. An added component to complement event descriptions with additional terms was not assessed better than the original system, due to the occasional addition of redundant terms. Comparing the found events to gold-standard events from maintained calendars on the Web mentioned in at least five tweets, the system yields a recall-at-250 of 0.20 and a recall based on all retrieved events of 0.40.

Predicting civil unrest by categorizing Dutch Twitter Events

We propose a system that assigns topical labels to automatically detected events in the Twitter stream. The automatic detection and labeling of events in social media streams is challenging due to the large number and variety of messages that are posted. The early detection of future social events, specifically those associated with civil unrest, has a wide applicability in areas such as security, e-governance, and journalism. We used machine learning algorithms and encoded the social media data using a wide range of features. Experiments show a high-precision (but low-recall) performance in the first step. We designed a second step that exploits classification probabilities, boosting the recall of our category of interest, social action events.