Topic time series analysis of microblogs

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Modeling Microblogs using Topic Models

As the popularity of micro-blogging increases, managing friends and followers and their tweets is becoming increasingly complex. In this project, we explore the usage of topic models in understanding both text and links in micro-blogs. On a data set of 21306 users, we find that LDA can find good topics that seem to capture meaningful topics of discussion in twitter. We also find that knowing wh...

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Characterizing Microblogs with Topic Models

As microblogging grows in popularity, services like Twitter are coming to support information gathering needs above and beyond their traditional roles as social networks. But most users’ interaction with Twitter is still primarily focused on their social graphs, forcing the often inappropriate conflation of “people I follow” with “stuff I want to read.” We characterize some information needs th...

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Wikipedia-based Topic Clustering for Microblogs

Microblogging has become a primary channel by which people not only share information, but also search for information. However, microblog search results are most often displayed by simple criteria such as creation time or author. A review of the literature suggests that clustering by topic may be useful, but short posts offer limited scope for clustering using lexical evidence alone. This pape...

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Although tidal observations which are extracted from coastal tide gages, have higher accuracy due to their higher sampling rate, installing these types of gages can impose some spatial limitation since we cannot use every part of sea to install them. To solve this limitation, we can employ satellite altimetry observations. However, satellite altimetry observations have lower sampling rate. Acco...

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ژورنال

عنوان ژورنال: IMA Journal of Applied Mathematics

سال: 2016

ISSN: 0272-4960,1464-3634

DOI: 10.1093/imamat/hxw025