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A Feasibility Study on Extracting Twitter Users' Interests Using NLP Tools for Serendipitous Connections

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A Feasibility Study on Extracting Twitter Users' Interests Using NLP Tools for Serendipitous Connections. / Piao, S.; Whittle, J.
Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom). IEEE, 2011. p. 910-915.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Piao, S & Whittle, J 2011, A Feasibility Study on Extracting Twitter Users' Interests Using NLP Tools for Serendipitous Connections. in Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom). IEEE, pp. 910-915. https://doi.org/10.1109/PASSAT/SocialCom.2011.164

APA

Piao, S., & Whittle, J. (2011). A Feasibility Study on Extracting Twitter Users' Interests Using NLP Tools for Serendipitous Connections. In Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom) (pp. 910-915). IEEE. https://doi.org/10.1109/PASSAT/SocialCom.2011.164

Vancouver

Piao S, Whittle J. A Feasibility Study on Extracting Twitter Users' Interests Using NLP Tools for Serendipitous Connections. In Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom). IEEE. 2011. p. 910-915 doi: 10.1109/PASSAT/SocialCom.2011.164

Author

Piao, S. ; Whittle, J. / A Feasibility Study on Extracting Twitter Users' Interests Using NLP Tools for Serendipitous Connections. Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom). IEEE, 2011. pp. 910-915

Bibtex

@inproceedings{edabfcaa7e374f9db881b5ef86f35fc9,
title = "A Feasibility Study on Extracting Twitter Users' Interests Using NLP Tools for Serendipitous Connections",
abstract = "This paper presents our research on the feasibility of extracting Twitter users' interests for suggesting serendipitous connections using natural language processing (NLP) technology. Defined by Andel [1] as the art of making an unsought finding, serendipity has a positive role in scientific research and people's daily lives. Applications that facilitate serendipity would bring various benefits to us. In this work, we focus on the mining of users' interests from Twitter messages (tweets hereafter) to support the detection of serendipitous connections. To address the challenge, we explore a set of NLP tools to develop a real-time system for automatically extracting the users' interests in the form of named entities and core terms. We also examine the different contributions of three different information sources with regard to the user's interests. Furthermore, we examine the issue of determining the additional attribute of surprisingness/ unexpectedness of the terms and entities of interest which we deem critical for detecting serendipitous connections. Our prototype system was tested with a group of Twitter users involving approximately 2,300 tweets. Our algorithm achieved varying degrees of success on each of the users, demonstrating feasibility of identifying serendipitous interest terms and entities. For example, 27.5% of terms extracted for one of the users were judged to be serendipitous.",
keywords = "interest extraction, named entity , natural langauge processing, serendipity , social computing , twitter analysis",
author = "S. Piao and J. Whittle",
year = "2011",
doi = "10.1109/PASSAT/SocialCom.2011.164",
language = "English",
isbn = "978-1-4577-1931-8",
pages = "910--915",
booktitle = "Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - A Feasibility Study on Extracting Twitter Users' Interests Using NLP Tools for Serendipitous Connections

AU - Piao, S.

AU - Whittle, J.

PY - 2011

Y1 - 2011

N2 - This paper presents our research on the feasibility of extracting Twitter users' interests for suggesting serendipitous connections using natural language processing (NLP) technology. Defined by Andel [1] as the art of making an unsought finding, serendipity has a positive role in scientific research and people's daily lives. Applications that facilitate serendipity would bring various benefits to us. In this work, we focus on the mining of users' interests from Twitter messages (tweets hereafter) to support the detection of serendipitous connections. To address the challenge, we explore a set of NLP tools to develop a real-time system for automatically extracting the users' interests in the form of named entities and core terms. We also examine the different contributions of three different information sources with regard to the user's interests. Furthermore, we examine the issue of determining the additional attribute of surprisingness/ unexpectedness of the terms and entities of interest which we deem critical for detecting serendipitous connections. Our prototype system was tested with a group of Twitter users involving approximately 2,300 tweets. Our algorithm achieved varying degrees of success on each of the users, demonstrating feasibility of identifying serendipitous interest terms and entities. For example, 27.5% of terms extracted for one of the users were judged to be serendipitous.

AB - This paper presents our research on the feasibility of extracting Twitter users' interests for suggesting serendipitous connections using natural language processing (NLP) technology. Defined by Andel [1] as the art of making an unsought finding, serendipity has a positive role in scientific research and people's daily lives. Applications that facilitate serendipity would bring various benefits to us. In this work, we focus on the mining of users' interests from Twitter messages (tweets hereafter) to support the detection of serendipitous connections. To address the challenge, we explore a set of NLP tools to develop a real-time system for automatically extracting the users' interests in the form of named entities and core terms. We also examine the different contributions of three different information sources with regard to the user's interests. Furthermore, we examine the issue of determining the additional attribute of surprisingness/ unexpectedness of the terms and entities of interest which we deem critical for detecting serendipitous connections. Our prototype system was tested with a group of Twitter users involving approximately 2,300 tweets. Our algorithm achieved varying degrees of success on each of the users, demonstrating feasibility of identifying serendipitous interest terms and entities. For example, 27.5% of terms extracted for one of the users were judged to be serendipitous.

KW - interest extraction

KW - named entity

KW - natural langauge processing

KW - serendipity

KW - social computing

KW - twitter analysis

U2 - 10.1109/PASSAT/SocialCom.2011.164

DO - 10.1109/PASSAT/SocialCom.2011.164

M3 - Conference contribution/Paper

SN - 978-1-4577-1931-8

SP - 910

EP - 915

BT - Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom)

PB - IEEE

ER -