國立虎尾科技大學 |

A Qualitative Study on How Micro-Influencers Detect Bots on LinkedIn.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	A Qualitative Study on How Micro-Influencers Detect Bots on LinkedIn./
Author:	Hein, Stacy.
Description:	1 online resource (120 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-03, Section: A.
Contained By:	Dissertations Abstracts International85-03A.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798380345101

A Qualitative Study on How Micro-Influencers Detect Bots on LinkedIn.
Hein, Stacy.

A Qualitative Study on How Micro-Influencers Detect Bots on LinkedIn. - 1 online resource (120 pages)

Source: Dissertations Abstracts International, Volume: 85-03, Section: A.

Thesis (D.C.S.)--Colorado Technical University, 2023.

Includes bibliographical references

The world of social media is both a boon and a bane for society. It allows users to get their voice out to a larger audience but also allows malicious actors to prey on those in the space using artificial intelligence and bot technology. Misinformation and online scams are a stark reality of the social media landscape. This study captured the methods of micro-influencers on LinkedIn to detect bots without using costly bot detection services. Many users do not possess the technical or financial resources to engage in bot detection services. LinkedIn micro-influencers have between 1,000 and 50,000 online followers and are usually highly regarded in their specific areas of expertise. The 14 participants involved in the study possessed a wide range of technical knowledge on bots and bot technology. The users engaged in interviews with the researcher and were asked specific questions about their bot detection methods. From the research, the participants engaged in several types of bot detection methods, including posting content only from known connections, evaluating the source of a connection request or post for irregularities, evaluating the content for irregularities, no validation, or "gut feel." Most participants used more than one of these strategies or methods to detect fake accounts on LinkedIn effectively.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380345101Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Bot detectionIndex Terms--Genre/Form:

554714
Electronic books.

A Qualitative Study on How Micro-Influencers Detect Bots on LinkedIn.
LDR:02679ntm a22003977 4500 001 1145303
005 20240618081813.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798380345101
035 $a (MiAaPQ)AAI30638807
035 $a AAI30638807
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Hein, Stacy. $3 1470573
245 1 2 $a A Qualitative Study on How Micro-Influencers Detect Bots on LinkedIn.
264 0 $c 2023
300 $a 1 online resource (120 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-03, Section: A.
500 $a Advisor: Marker, David C.
502 $a Thesis (D.C.S.)--Colorado Technical University, 2023.
504 $a Includes bibliographical references
520 $a The world of social media is both a boon and a bane for society. It allows users to get their voice out to a larger audience but also allows malicious actors to prey on those in the space using artificial intelligence and bot technology. Misinformation and online scams are a stark reality of the social media landscape. This study captured the methods of micro-influencers on LinkedIn to detect bots without using costly bot detection services. Many users do not possess the technical or financial resources to engage in bot detection services. LinkedIn micro-influencers have between 1,000 and 50,000 online followers and are usually highly regarded in their specific areas of expertise. The 14 participants involved in the study possessed a wide range of technical knowledge on bots and bot technology. The users engaged in interviews with the researcher and were asked specific questions about their bot detection methods. From the research, the participants engaged in several types of bot detection methods, including posting content only from known connections, evaluating the source of a connection request or post for irregularities, evaluating the content for irregularities, no validation, or "gut feel." Most participants used more than one of these strategies or methods to detect fake accounts on LinkedIn effectively.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Information technology. $3 559429
650 4 $a Web studies. $3 1148502
653 $a Bot detection
653 $a Bot mitigation
653 $a Bots
653 $a LinkedIn
653 $a Social media
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0489
690 $a 0646
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Colorado Technical University. $b Computer Science. $3 1179604
773 0 $t Dissertations Abstracts International $g 85-03A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30638807 $z click for full text (PQDT)