Developing a rule-based method for identifying researchers on Twitter: The case of vaccine discussions

Creator/Principal investigator(s):

Björn Ekström

Description:

This study seeks to develop a method for identifying the occurrences and proportions of researchers, media and other professionals active in Twitter discussions. As a case example, an anonymised dataset from Twitter vaccine discussions is used. The study proposes a method of using keywords as strings within lists to identify classes from user biographies. This provides a way to apply multiple classification principles to a set of Twitter biographies using semantic rules through the Python programming language. The script used for the study is here deposited.

Subject area:

higher and further education, information society, language and linguistics (CESSDA Topic Classification)
Language Technology (Computational Linguistics), Social Sciences, Information Studies (The Swedish standard of fields of research 2011)

Keywords:

classification, social media

Principal organisation:

University of Borås

Accessibility status:

Access to data through SND
Data are freely accessible

Download data:

class.py (7.62 KB)

Responsible department/unit:

University of Borås

Creator/Principal investigator(s):

Björn Ekström

Identifiers:

SND-ID: SND 1117

Description:

This study seeks to develop a method for identifying the occurrences and proportions of researchers, media and other professionals active in Twitter discussions. As a case example, an anonymised dataset from Twitter vaccine discussions is used. The study proposes a method of using keywords as strings within lists to identify classes from user biographies. This provides a way to apply multiple classification principles to a set of Twitter biographies using semantic rules through the Python programming language. The script used for the study is here deposited.

Language:

English

Time period(s) investigated:

2018-06-01 — 2019-10-31

Unit of analysis:

Population:

Twitter users

Time Method:

Sampling procedure:

Other

Funding:

Horizon 2020 — Ref. 770531

Subject area:

higher and further education, information society, language and linguistics (CESSDA Topic Classification)
Language Technology (Computational Linguistics), Social Sciences, Information Studies (The Swedish standard of fields of research 2011)

Keywords:

classification, social media

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Publications

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Ekström, B. (2019). Developing a rule-based method for identifying researchers on Twitter: The case of vaccine discussions. Poster abstract accepted to ISSI, 17th International Society of Scientometrics and Informetrics Conference, Rome, 2-5 September.

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Version 1.0:

2019-08-23 doi:10.5878/akmc-va16

Download data:

class.py (7.62 KB)

License:

Creative Commons License

Rule-based method for identifying researchers on Twitter

Suggested citation:

Björn Ekström. University of Borås (2019). <em>Rule-based method for identifying researchers on Twitter</em>. Swedish National Data Service. Version 1.0. <a href="https://doi.org/10.5878/akmc-va16">https://doi.org/10.5878/akmc-va16</a>

Creator/Principal investigator(s):

Björn Ekström

Description:

Method development for Twitter biography classification concerning occurrences of academics, academically related groups and individuals, media, other groups and members of the general public. Written in the Python programming language.

Data format / data structure:

Software

Published: 2019-09-20