No Cover Image

Journal article 1460 views 4234 downloads

Social Bots and the Spread of Disinformation in Social Media: The Challenges of Artificial Intelligence

Nick Hajli Orcid Logo, Usman Saeed, Mina Tajvidi Orcid Logo, Farid Shirazi Orcid Logo

British Journal of Management, Volume: 33, Issue: 3, Pages: 1238 - 1253

Swansea University Author: Nick Hajli Orcid Logo

Check full text

DOI (Published version): 10.1111/1467-8551.12554

Abstract

Artificial intelligence (AI) is creating a revolution in business and society at large as well as challenges for organisations. AI-powered social bots can sense, think, and act on social media platforms in ways similar to humans. The challenge is that social bots can perform many harmful actions, su...

Full description

Published in: British Journal of Management
ISSN: 1045-3172 1467-8551
Published: Wiley 2022
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa58271
first_indexed 2021-10-08T12:18:58Z
last_indexed 2023-01-11T14:38:45Z
id cronfa58271
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2022-07-25T16:00:41.9035255</datestamp><bib-version>v2</bib-version><id>58271</id><entry>2021-10-08</entry><title>Social Bots and the Spread of Disinformation in Social Media: The Challenges of Artificial Intelligence</title><swanseaauthors><author><sid>7608daaad16c0921edd18f5ac2643553</sid><ORCID>0000-0002-9818-181X</ORCID><firstname>Nick</firstname><surname>Hajli</surname><name>Nick Hajli</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2021-10-08</date><deptcode>CBAE</deptcode><abstract>Artificial intelligence (AI) is creating a revolution in business and society at large as well as challenges for organisations. AI-powered social bots can sense, think, and act on social media platforms in ways similar to humans. The challenge is that social bots can perform many harmful actions, such as providing wrong information to people, escalating arguments, perpetrating scams, and exploiting the stock market. As such, an understanding of different kinds of social bots and their authors' intentions is vital from the management perspectives. Drawing from the actor-network theory (ANT), this study investigates human and non-human actors' role in social media, particularly Twitter. We use text mining and machine learning techniques, and after applying different pre-processing techniques, we applied the bag of words model to a dataset of 30,000 English-language tweets. The present research is among the few studies to use a theory-based focus to look, through experimental research, at the role of social bots and the spread of disinformation in social media. Firms can use our tool for the early detection of harmful social bots before they can spread misinformation on social media about their organisations.</abstract><type>Journal Article</type><journal>British Journal of Management</journal><volume>33</volume><journalNumber>3</journalNumber><paginationStart>1238</paginationStart><paginationEnd>1253</paginationEnd><publisher>Wiley</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>1045-3172</issnPrint><issnElectronic>1467-8551</issnElectronic><keywords/><publishedDay>1</publishedDay><publishedMonth>7</publishedMonth><publishedYear>2022</publishedYear><publishedDate>2022-07-01</publishedDate><doi>10.1111/1467-8551.12554</doi><url/><notes/><college>COLLEGE NANME</college><department>Management School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>CBAE</DepartmentCode><institution>Swansea University</institution><apcterm/><funders/><projectreference/><lastEdited>2022-07-25T16:00:41.9035255</lastEdited><Created>2021-10-08T13:14:55.9150337</Created><path><level id="1">Faculty of Humanities and Social Sciences</level><level id="2">School of Management - Business Management</level></path><authors><author><firstname>Nick</firstname><surname>Hajli</surname><orcid>0000-0002-9818-181X</orcid><order>1</order></author><author><firstname>Usman</firstname><surname>Saeed</surname><order>2</order></author><author><firstname>Mina</firstname><surname>Tajvidi</surname><orcid>0000-0002-6259-505x</orcid><order>3</order></author><author><firstname>Farid</firstname><surname>Shirazi</surname><orcid>0000-0001-5641-7268</orcid><order>4</order></author></authors><documents><document><filename>58271__21479__e1c3c3909c3347f7b4c69a09ae6cc392.pdf</filename><originalFilename>58271.pdf</originalFilename><uploaded>2021-11-10T15:14:35.9555031</uploaded><type>Output</type><contentLength>525688</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
spelling 2022-07-25T16:00:41.9035255 v2 58271 2021-10-08 Social Bots and the Spread of Disinformation in Social Media: The Challenges of Artificial Intelligence 7608daaad16c0921edd18f5ac2643553 0000-0002-9818-181X Nick Hajli Nick Hajli true false 2021-10-08 CBAE Artificial intelligence (AI) is creating a revolution in business and society at large as well as challenges for organisations. AI-powered social bots can sense, think, and act on social media platforms in ways similar to humans. The challenge is that social bots can perform many harmful actions, such as providing wrong information to people, escalating arguments, perpetrating scams, and exploiting the stock market. As such, an understanding of different kinds of social bots and their authors' intentions is vital from the management perspectives. Drawing from the actor-network theory (ANT), this study investigates human and non-human actors' role in social media, particularly Twitter. We use text mining and machine learning techniques, and after applying different pre-processing techniques, we applied the bag of words model to a dataset of 30,000 English-language tweets. The present research is among the few studies to use a theory-based focus to look, through experimental research, at the role of social bots and the spread of disinformation in social media. Firms can use our tool for the early detection of harmful social bots before they can spread misinformation on social media about their organisations. Journal Article British Journal of Management 33 3 1238 1253 Wiley 1045-3172 1467-8551 1 7 2022 2022-07-01 10.1111/1467-8551.12554 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University 2022-07-25T16:00:41.9035255 2021-10-08T13:14:55.9150337 Faculty of Humanities and Social Sciences School of Management - Business Management Nick Hajli 0000-0002-9818-181X 1 Usman Saeed 2 Mina Tajvidi 0000-0002-6259-505x 3 Farid Shirazi 0000-0001-5641-7268 4 58271__21479__e1c3c3909c3347f7b4c69a09ae6cc392.pdf 58271.pdf 2021-11-10T15:14:35.9555031 Output 525688 application/pdf Accepted Manuscript true true eng
title Social Bots and the Spread of Disinformation in Social Media: The Challenges of Artificial Intelligence
spellingShingle Social Bots and the Spread of Disinformation in Social Media: The Challenges of Artificial Intelligence
Nick Hajli
title_short Social Bots and the Spread of Disinformation in Social Media: The Challenges of Artificial Intelligence
title_full Social Bots and the Spread of Disinformation in Social Media: The Challenges of Artificial Intelligence
title_fullStr Social Bots and the Spread of Disinformation in Social Media: The Challenges of Artificial Intelligence
title_full_unstemmed Social Bots and the Spread of Disinformation in Social Media: The Challenges of Artificial Intelligence
title_sort Social Bots and the Spread of Disinformation in Social Media: The Challenges of Artificial Intelligence
author_id_str_mv 7608daaad16c0921edd18f5ac2643553
author_id_fullname_str_mv 7608daaad16c0921edd18f5ac2643553_***_Nick Hajli
author Nick Hajli
author2 Nick Hajli
Usman Saeed
Mina Tajvidi
Farid Shirazi
format Journal article
container_title British Journal of Management
container_volume 33
container_issue 3
container_start_page 1238
publishDate 2022
institution Swansea University
issn 1045-3172
1467-8551
doi_str_mv 10.1111/1467-8551.12554
publisher Wiley
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 1
active_str 0
description Artificial intelligence (AI) is creating a revolution in business and society at large as well as challenges for organisations. AI-powered social bots can sense, think, and act on social media platforms in ways similar to humans. The challenge is that social bots can perform many harmful actions, such as providing wrong information to people, escalating arguments, perpetrating scams, and exploiting the stock market. As such, an understanding of different kinds of social bots and their authors' intentions is vital from the management perspectives. Drawing from the actor-network theory (ANT), this study investigates human and non-human actors' role in social media, particularly Twitter. We use text mining and machine learning techniques, and after applying different pre-processing techniques, we applied the bag of words model to a dataset of 30,000 English-language tweets. The present research is among the few studies to use a theory-based focus to look, through experimental research, at the role of social bots and the spread of disinformation in social media. Firms can use our tool for the early detection of harmful social bots before they can spread misinformation on social media about their organisations.
published_date 2022-07-01T08:01:56Z
_version_ 1821301136759455744
score 11.047565

Similar Items