2021
DOI: 10.3127/ajis.v25i0.2987
|View full text |Cite
|
Sign up to set email alerts
|

The dark and light sides of engagement: an analysis of user-generated content in wildlife trade online communities.

Abstract: Recent research has focused on the role of user-generated content (UGC) in the dark side of engagement on social media. In this study, we apply this to the unique context of the online exotic wildlife trade, a critical area of research due its involvement in devastating global species loss as well as harms to human health and livelihoods. We first conduct qualitative analysis on a large data set of UGC with the automatic machine-learning lexical software Leximancer 4.5.1 to explore the discourse that occurs in… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
3
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
3
2
1

Relationship

0
6

Authors

Journals

citations
Cited by 6 publications
(3 citation statements)
references
References 77 publications
0
3
0
Order By: Relevance
“…Processing through the central channel entails a cognitive evaluation of the strength of the argument made in the post's content, but cues from the peripheral route are based on the post's vividness, popularity, and source credibility (Munaro et al, 2021). Meanwhile, the persuasiveness of the information supplied is indicated by the argument quality, which is one of the most talked‐about aspects of social media message processing (Feddema et al, 2021; Teng et al, 2014). Le et al (2020) concentrated on the message and its source by increasing the understanding and usage of the ELM in the context of social media content research.…”
Section: Theories Contexts Characteristics and Methodology (Ro1)mentioning
confidence: 99%
“…Processing through the central channel entails a cognitive evaluation of the strength of the argument made in the post's content, but cues from the peripheral route are based on the post's vividness, popularity, and source credibility (Munaro et al, 2021). Meanwhile, the persuasiveness of the information supplied is indicated by the argument quality, which is one of the most talked‐about aspects of social media message processing (Feddema et al, 2021; Teng et al, 2014). Le et al (2020) concentrated on the message and its source by increasing the understanding and usage of the ELM in the context of social media content research.…”
Section: Theories Contexts Characteristics and Methodology (Ro1)mentioning
confidence: 99%
“…Trade has conventionally been done in physical markets, although still existent there is a paradigm shift to social media and virtual commercial platforms (Davies, 2014;Feddema et al, 2021;Harrison et al, 2016). This rise in the popularity of online trading is due to easy access, limitless, borderless and unregulated nature of the crime (Kulkarni & Di Minin, 2021;Xu et al, 2016).…”
Section: Introductionmentioning
confidence: 99%
“…Effective monitoring of global online wildlife trade requires automated content identification [8]. In conservation science and practice, the use of machine learning or digital data analysis methods to monitor the online illegal wildlife trade is increasing but is still limited [9], [10], [11], [12]. Compared to other tasks where machine learning methods are being used in conservation science, using machine learning to monitor the online trade in wildlife can be more challenging.…”
Section: Introductionmentioning
confidence: 99%