<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Background: Information can now be rapidly exchanged due to social media. Due to its openness, Twitter has generated massive amounts of data. In this paper, we apply data mining and analytics to extract the usage patterns of social media by small businesses. Objectives: The aim of this paper is to describe with an example how data mining can be applied to social media. This paper further examines the impact of social media on small businesses. The Twitter posts related to small businesses are analyzed in detail. Methods/Approach: The patterns of social media usage by small businesses are observed using IBM Watson Analytics. In this paper, we particularly analyze tweets on Twitter for the hashtag #smallbusiness. Results: It is found that the number of females posting topics related to small business on Twitter is greater than the number of males. It is also found that the number of negative posts in Twitter is relatively low. Conclusions: Small firms are beginning to understand the importance of social media to realize their business goals. For future research, further analysis can be performed on the date and time the tweets were posted.</p></abstract>

Background: Information can now be rapidly exchanged due to social media. Due to its openness, Twitter has generated massive amounts of data. In this paper, we apply data mining and analytics to extract the usage patterns of social media by small businesses. Objectives: The aim of this paper is to describe with an example how data mining can be applied to social media. This paper further examines the impact of social media on small businesses. The Twitter posts related to small businesses are analyzed in detail. Methods/Approach: The patterns of social media usage by small businesses are observed using IBM Watson Analytics. In this paper, we particularly analyze tweets on Twitter for the hashtag #smallbusiness. Results: It is found that the number of females posting topics related to small business on Twitter is greater than the number of males. It is also found that the number of negative posts in Twitter is relatively low. Conclusions: Small firms are beginning to understand the importance of social media to realize their business goals. For future research, further analysis can be performed on the date and time the tweets were posted.

Mining for Social Media: Usage Patterns of Small Businesses

Business Systems Research Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Mining for Social Media: Usage Patterns of Small Businesses"}

Background: Information can now be rapidly exchanged due to social media. Due to its openness, Twitter has generated massive amounts of data. In this paper, we...

Mining for Social Media: Usage Patterns of Small Businesses

Online veröffentlicht: 18. Mai 2017

Seitenbereich: 43 - 50

Eingereicht: 15. Jan. 2017

Akzeptiert: 31. März 2017

DOI: https://doi.org/10.1515/bsrj-2017-0004

Schlüsselwörter
social media, analysis, data mining, small business

© by Shilpa Balan

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mining for Social Media: Usage Patterns of Small Businesses

Online veröffentlicht: 18. Mai 2017

Seitenbereich: 43 - 50

Eingereicht: 15. Jan. 2017

Akzeptiert: 31. März 2017

DOI: https://doi.org/10.1515/bsrj-2017-0004

Schlüsselwörtersocial media, analysis, data mining, small business

© by Shilpa Balan

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
social media, analysis, data mining, small business