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Online social media platforms are turning into the prime source of news andnarratives about worldwide events. However,a systematic summarization-basednarrative extraction that can facilitate communicating the main underlyingevents is lacking. To address this issue, we propose a novel event-basednarrative summary extraction framework. Our proposed framework is designed as aprobabilistic topic model, with categorical time distribution, followed byextractive text summarization. Our topic model identifies topics' recurrenceover time with a varying time resolution. This framework not only captures thetopic distributions from the data, but also approximates the user activityfluctuations over time. Furthermore, we define significance-dispersitytrade-off (SDT) as a comparison measure to identify the topic with the highestlifetime attractiveness in a timestamped corpus. We evaluate our model on alarge corpus of Twitter data, including more than one million tweets in thedomain of the disinformation campaigns conducted against the White Helmets ofSyria. Our results indicate that the proposed framework is effective inidentifying topical trends, as well as extracting narrative summaries from textcorpus with timestamped data.

Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model