2014 DynamicsofNewsEventsandSocialMe

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The analysis of social sentiment expressed on the Web is becoming increasingly relevant to a variety of applications, and it is important to understand the underlying mechanisms which drive the evolution of sentiments in one way or another, in order to be able to predict these changes in the future. In this paper, we study the dynamics of news events and their relation to changes of sentiment expressed on relevant topics. We propose a novel framework, which models the behavior of news and social media in response to events as a convolution between event's importance and media response function, specific to media and event type. This framework is suitable for detecting time and duration of events, as well as their impact and dynamics, from time series of publication volume. These data can greatly enhance events analysis; for instance, they can help distinguish important events from unimportant, or predict sentiment and stock market shifts. As an example of such application, we extracted news events for a variety of topics and then correlated this data with the corresponding sentiment time series, revealing the connection between sentiment shifts and event dynamics.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 DynamicsofNewsEventsandSocialMe	Malu Castellanos Mikalai Tsytsarau Themis Palpanas			Dynamics of News Events and Social Media Reaction				10.1145/2623330.2623670		2014