Identifying Prominent Life Events on Twitter

Abstract

Social media is a common place for people to post and share digital reflections of their life events, including major events such as getting married, having children, graduating, etc. Although the creation of such posts is straightforward, the identification of events on online media remains a challenge. Much research in recent years focused on extracting major events from Twitter, such as earthquakes, storms, and floods. This paper however, targets the automatic detection of personal life events, focusing on five events that psychologists found to be the most prominent in people lives. We define a variety of features (user, content, semantic and interaction) to capture the characteristics of those life events and present the results of several classification methods to automatically identify these events in Twitter. Our proposed classification methods obtain results between 0.84 and 0.92 F1-measure for the different types of life events. A novel contribution of this work also lies in a new corpus of tweets, which has been annotated by using crowdsourcing and that constitutes, to the best of our knowledge, the first publicly available dataset for the automatic identification of personal life events from Twitter.

Where Did the President Visit Last Week? Detecting Celebrity Trips from News Articles

“…• LR (TFIDF) (Dickinson et al 2015): The researchers use TF-IDF vectors as the representations of news articles and employs Logistic Regression as the classifier for a PRED task. We directly adopt it in our experiments.…”

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“…Event detection from social media has received considerable attention, in particular, pinpointing important life events (Li et al, 2014;Dickinson et al, 2015). Previous research shows that people often tweet about events they do not participate in (Sanagavarapu et al, 2017), targets recurring events (Kunneman and Van den Bosch, 2015), and summarizes tweet streams about TV shows (Andy et al, 2019).…”