Maryam Khodabakhsh
Hossein Fani
ABSTRACT
Researchers have shown that it is possible to identify reported instances of personal life events from users' social content, e.g., tweets. This is known as personal life event detection. In this paper, we take a step forward and explore the possibility of predicting users' next personal life event based solely on the their historically reported personal life events, a task which we refer to as personal life event prediction. We present a framework for modeling streaming social content for the purpose of personal life event prediction and describe how various instantiations of the framework can be developed to build a life event prediction model. In our extensive experiments, we find that (i) historical personal life events of a user have strong predictive power for determining the user's future life event; (ii) the consideration of sequence in historically reported personal life events shows inferior performance compared to models that do not consider sequence, and (iii) the number of historical life events and the length of the past time intervals that are taken into account for making life event predictions can impact prediction performance whereby more recent life events show more relevance for the prediction of future life events.
- John G. Cleary and Ian H. Witten. 1984. Data Compression Using Adaptive Coding and Partial String Matching. IEEE Trans Comm 32, 4 (1984), 396--402.Google Scholar
Cross Ref
- Mukund Deshpande and George Karypis. 2004. Selective Markov models for predicting Web page accesses. ACM Trans. Internet Techn. 4, 2 (2004), 163--184. Google ScholarDigital Library
- Thomas Dickinson, Miriam Fernández, Lisa A. Thomas, Paul Mulholland, Pam Briggs, and Harith Alani. 2015. Identifying Prominent Life Events on Twitter. In K-CAP. 4:1--4:8. Google ScholarDigital Library
- Ted Gueniche, Philippe Fournier-Viger, Rajeev Raman, and Vincent S. Tseng. 2015. CPT+: Decreasing the Time/Space Complexity of the Compact Prediction Tree. In PAKDD 2015. 625--636.Google Scholar
- Ted Gueniche, Philippe Fournier-Viger, and Vincent S. Tseng. 2013. Compact Prediction Tree: A Lossless Model for Accurate Sequence Prediction. In ADMA 2013. 177--188. Google ScholarDigital Library
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural Computation 9, 8 (1997), 1735--1780. Google ScholarDigital Library
- Maryam Khodabakhsh, Mohsen Kahani, Ebrahim Bagheri, and Zeinab Noorian. 2018. Detecting life events from twitter based on temporal semantic features. Knowl.-Based Syst. 148 (2018), 1--16.Google ScholarCross Ref
- Jiwei Li, Alan Ritter, Claire Cardie, and Eduard H. Hovy. 2014. Major Life Event Extraction from Twitter based on Congratulations/Condolences Speech Acts. In EMNLP 2014. 1997--2007.Google Scholar
- Vasileios Mezaris, Ansgar Scherp, Ramesh Jain, and Mohan S. Kankanhalli. 2014. Real-life events in multimedia: detection, representation, retrieval, and applica- tions. Multimedia Tools and Applications 70, 1 (2014), 1--6. Google ScholarDigital Library
- James E. Pitkow and Peter Pirolli. 1999. Mining Longest Repeating Subsequences to Predict World Wide Web Surfing. In USITS'99. Google ScholarDigital Library
Index Terms
- Predicting Personal Life Events from Streaming Social Content
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