Mining Long-term Topics from a Real-time Feed

M.C. ten Thij

Mining Long-term Topics from a Real-time Feed

M.C. ten Thij

Mathematics

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

Abstract

In our current society, the availability of data has gone from scarce to abundant: huge volumes of data are generated every second. A significant part of these data are generated on social media platforms, which provide a very volatile flow of information. Leveraging the information that is buried in this fast stream of messages, poses a serious challenge. In this paper, we aim to distinguish all topics that are discussed in real-time in a social media feed by employing clustering and algorithmic techniques. We evaluate our approach by comparing the results to a post-hoc clustering approach.

Original language	English
Title of host publication	6th International Conference, IARIA Data Analytics 2017, Barcelona, Spain, November 12-16, 2017, Proceedings
Editors	S. Bhulai, D. Kardakas
Publisher	IARIA
Pages	1-5
ISBN (Print)	9781612086033
Publication status	Published - 12 Nov 2017
Event	IARIA DATA ANALYTICS 2017 - Duration: 12 Nov 2017 → 16 Nov 2017

Conference

Conference	IARIA DATA ANALYTICS 2017
Period	12/11/17 → 16/11/17

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title = "Mining Long-term Topics from a Real-time Feed",

abstract = "In our current society, the availability of data has gone from scarce to abundant: huge volumes of data are generated every second. A significant part of these data are generated on social media platforms, which provide a very volatile flow of information. Leveraging the information that is buried in this fast stream of messages, poses a serious challenge. In this paper, we aim to distinguish all topics that are discussed in real-time in a social media feed by employing clustering and algorithmic techniques. We evaluate our approach by comparing the results to a post-hoc clustering approach.",

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booktitle = "6th International Conference, IARIA Data Analytics 2017, Barcelona, Spain, November 12-16, 2017, Proceedings",

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note = "IARIA DATA ANALYTICS 2017 ; Conference date: 12-11-2017 Through 16-11-2017",

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AU - ten Thij, M.C.

PY - 2017/11/12

Y1 - 2017/11/12

N2 - In our current society, the availability of data has gone from scarce to abundant: huge volumes of data are generated every second. A significant part of these data are generated on social media platforms, which provide a very volatile flow of information. Leveraging the information that is buried in this fast stream of messages, poses a serious challenge. In this paper, we aim to distinguish all topics that are discussed in real-time in a social media feed by employing clustering and algorithmic techniques. We evaluate our approach by comparing the results to a post-hoc clustering approach.

AB - In our current society, the availability of data has gone from scarce to abundant: huge volumes of data are generated every second. A significant part of these data are generated on social media platforms, which provide a very volatile flow of information. Leveraging the information that is buried in this fast stream of messages, poses a serious challenge. In this paper, we aim to distinguish all topics that are discussed in real-time in a social media feed by employing clustering and algorithmic techniques. We evaluate our approach by comparing the results to a post-hoc clustering approach.

M3 - Conference contribution

SN - 9781612086033

SP - 1

EP - 5

BT - 6th International Conference, IARIA Data Analytics 2017, Barcelona, Spain, November 12-16, 2017, Proceedings

A2 - Bhulai, S.

A2 - Kardakas, D.

PB - IARIA

T2 - IARIA DATA ANALYTICS 2017

Y2 - 12 November 2017 through 16 November 2017

ER -

Mining Long-term Topics from a Real-time Feed

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