Exploring users' motivations to participate in viral communication on social media

Borges‐Tiago, Teresa; Tiago, Flávio; Cosme, Carla

doi:10.1016/j.jbusres.2018.11.011

Cited by 66 publications

(47 citation statements)

References 35 publications

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“…The current research thus presents a motivational framework and classifies tourists into four distinct segments. In line with previous literature [17,21,23,29,32], this study affirms that enjoyment, self-esteem, social ties, recognition, interests, social norms, goals, self-efficacy, outcome expectations and prestige, are related with users' motivations to post on Instagram while travelling. Notably, in addition to the fact that users would like to elevate their status and gain prestige by sharing on social media sites [25,35], this study uncovers other factors that are more internal and self-centred such as recognition, enjoyment and self-esteem.…”

Section: Resultssupporting

confidence: 92%

“…The second section focused on the relationship between tourist behaviour in the context of sharing pictures on Instagram while traveling and the intrinsic and extrinsic factors. According to the identified motivations in step 2, this study summarised a list of 47 statements to measure 11 motives based on previous research [16,17,32,33] (i.e., enjoyment, self-esteem, recognition, interests, social norms, goals, social ties, social status and prestige, selfefficiency, outcome expectations, and memorabilia). A 6-point Likert scale was adopted (1 = strongly disagree; 6 = strongly agree).…”

Section: Step 3: Data Collection and Survey Developmentmentioning

confidence: 99%

See 1 more Smart Citation

The Implicit and Explicit Motivations of Tourist Behaviour in Sharing Travel Photographs on Instagram: A Path and Cluster Analysis

Daxböck

Dulbecco

Kursite

et al. 2021

Information and Communication Technologies in Tourism 2021

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Instagram has been an emerging platform for tourists to share their experiences and connect with other users in the multiphasic travel stages. Despite the huge number of photographs shared on Instagram on a daily basis, it remains ambiguous regarding the underlying motives of tourists’ posting behaviour. Thus, this study aims to conceptualise a framework based on the internal and external triggers of sharing travel photographs through a mix methods design involving diary studies and questionnaires. By conducting a path analysis, this study presents and validates a theoretical model including various motivational factors; namely enjoyment, self-esteem, recognition, interests, social norms, goals, social ties, social status and prestige, self-efficiency, outcome expectations and memorabilia. Meanwhile, this research clusters young techsavvy tourists into four distinct segments based on their behaviour of using Instagram while traveling. By bridging motivational theories, social psychology, and social media in the context of tourism, this research extends literature related to user-generated content and Instagram. Practically, this research allows marketers to optimise the effectiveness of marketing strategies based on the characteristics of tourists and their behaviour on social media platforms.

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Section: Resultssupporting

confidence: 92%

Section: Step 3: Data Collection and Survey Developmentmentioning

confidence: 99%

The Implicit and Explicit Motivations of Tourist Behaviour in Sharing Travel Photographs on Instagram: A Path and Cluster Analysis

Daxböck

Dulbecco

Kursite

et al. 2021

Information and Communication Technologies in Tourism 2021

View full text Add to dashboard Cite

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“…At the same time, other research projects analyzed the impact of network topologies and influence characteristics of certain nodes of the social media communities in the information diffusion mechanism [20][21][22][23]. In [20], Zhang et al developed a new metric called social influence locality to compute the probability that an information is spread by a node in a social media graph based on the behavior of surrounding nodes.…”

Section: Related Workmentioning

confidence: 99%

“…This result suggests that the analysis of users' features contributes to a better precision in prediction of users' sharing action on social media platforms. In [23], Borges et al investigated what motivates users to share viral communications on the web communities. They observed that users involved in the sharing action of viral contents prefer not to participate in the discussion of the content itself.…”

Section: Related Workmentioning

confidence: 99%

Automated Classification of Fake News Spreaders to Break the Misinformation Chain

2021

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In social media, users are spreading misinformation easily and without fact checking. In principle, they do not have a malicious intent, but their sharing leads to a socially dangerous diffusion mechanism. The motivations behind this behavior have been linked to a wide variety of social and personal outcomes, but these users are not easily identified. The existing solutions show how the analysis of linguistic signals in social media posts combined with the exploration of network topologies are effective in this field. These applications have some limitations such as focusing solely on the fake news shared and not understanding the typology of the user spreading them. In this paper, we propose a computational approach to extract features from the social media posts of these users to recognize who is a fake news spreader for a given topic. Thanks to the CoAID dataset, we start the analysis with 300 K users engaged on an online micro-blogging platform; then, we enriched the dataset by extending it to a collection of more than 1 M share actions and their associated posts on the platform. The proposed approach processes a batch of Twitter posts authored by users of the CoAID dataset and turns them into a high-dimensional matrix of features, which are then exploited by a deep neural network architecture based on transformers to perform user classification. We prove the effectiveness of our work by comparing the precision, recall, and f1 score of our model with different configurations and with a baseline classifier. We obtained an f1 score of 0.8076, obtaining an improvement from the state-of-the-art by 4%.

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“…These social media influencers have the ability to rapidly spread information to numerous social media users [26]. The level of influence these influencers have, can amplify the impact of the information [27].…”

Section: Social Factorsmentioning

confidence: 99%

A Systematic Review on Fake News Themes Reported in Literature

Celliers

Hattingh

2020

Lecture Notes in Computer Science

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In this systematic literature review, a study of the factors involved in the spreading of fake news, have been provided. In this review, the root causes of the spreading of fake news are identified to reduce the encouraging of such false information. To combat the spreading of fake news on social media, the reasons behind the spreading of fake news must first be identified. Therefore, this literature review takes an early initiative to identify the possible reasons behind the spreading of fake news. The purpose of this literature review is to identify why individuals tend to share false information and to possibly help in detecting fake news before it spreads.

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Exploring users' motivations to participate in viral communication on social media

Cited by 66 publications

References 35 publications

The Implicit and Explicit Motivations of Tourist Behaviour in Sharing Travel Photographs on Instagram: A Path and Cluster Analysis

The Implicit and Explicit Motivations of Tourist Behaviour in Sharing Travel Photographs on Instagram: A Path and Cluster Analysis

Automated Classification of Fake News Spreaders to Break the Misinformation Chain

A Systematic Review on Fake News Themes Reported in Literature

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