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This research analyses how dialogical communication on social media enhances the effectiveness of sustainability messages in terms of brand engagement and purchase intention. Dialogical messages generate social media engagement, which helps improve consumer responses.

Two experimental studies (2 × 2) were conducted, in which two factors were manipulated: the dialogical potential of the message (dialogical versus monological) and message orientation (sustainability versus commercial).

The dialogical potential of the message moderates the effect of sustainability messages on brand engagement and purchase intention. Results indicate that sustainability messages featuring dialogical elements generate greater brand engagement and purchase intention than commercial messages. Furthermore, social media engagement mediates these effects.

This study offers valuable insights into applying dialogical principles to sustainability communication on social media. Marketers should design dialogical messages to foster dialogue with customers and enhance engagement.

Few studies have focused on analysing the effects of applying dialogical strategies on social media to communicate sustainability. Thus, this study highlights the importance of dialogical communication beyond the inclusion of interactive elements when communicating sustainability on social media. The inclusion of dialogical features specifically benefits sustainability messages, given the transparency and honesty they demand.

The pandemic has enhanced the global phenomenon of disinformation. This paper aims to study the false news concerning COVID-19, spread through social media in Spain, by using the LatamChequea database for a duration from 01/22/2020, when the first false information has been detected, up to 03/09/2021.

A quantitative analysis has been conducted with regard to the correlation between fake news stories and the pandemic state, the motive to share them, their dissemination in other countries and the effectiveness of fact checking. This study is complemented by a qualitative method: a focus group conducted with representatives of different groups within the society.

Fake news has been primarily disseminated through several social networks at the same time, with two peaks taking place in over a half of the said false stories. The first took place from March to April of 2020 during complete lockdown, and we were informed of prevention measures, the country’s situation and the origin of the virus, whereas the second was related to news revolving around the coming vaccines, which occurred between October and November. The audience tends to neither cross-check the information received nor report fake news to competent authorities, and fact-checking methods fail to stop their spread. Further awareness and digital literacy campaigns are thus required in addition to more involvement from governments and technological platforms.

The main limitation of the research is the fact that it was only possible to conduct a focus group of five individuals who do not belong to generation Z due to the restrictions imposed by the pandemic, although a clear contribution to the analysis of the impact of fake news on social networks during the COVID-19 pandemic in Spain can be seen from the privileged experiences in each of the fields of work that were identified. In this sense, the results of the study are not generalizable to a larger population. On the other hand, and with a view to future research, it would be advisable to carry out a more specific study of how fake news affects generation Z.

This research is original in nature, and the findings of this study are valuable for business practitioners and scholars, brand marketers, social media platform owners, opinion leaders and policymakers.

This study aims to examine the tourists' visit intention by watching deepfake destination videos, using Information Manipulation and Media Richness Theory.

This study conducted a primary survey utilizing a structured questionnaire. In total, 1,360 tourists were surveyed, and quantitative data analysis was done using PLS-SEM.

The results indicate that the factors that affect the tourists' visit intention after watching deepfake videos include information manipulation tactics, trust and media richness. This study also found that perceived deception and cognitive load do not influence the tourists' visit intention.

The originality/salience of this study lies in the fact that this is possibly among the first to combine the Media Richness Theory and Information Manipulation for understanding tourists' visit intention and post-viewing deepfake destination videos.

The study investigates factors like media, tribal, religious and party politics' influence on Nigerias’ 2023 presidential election choice. It confirms dominant social media platforms and examines their influence on election polls, e-participation and political candidate choice. The main objectives of this study are to: investigate if tribal, religious and party politics affect the respondent’s choice of a presidential candidate, ascertain the respondent's most used social media platform for political engagement and determine how social media platforms influenced the election polls during the 2023 Nigerian presidential election.

A sample size of 384 registered voters was used to survey three states in Southeast Nigeria hinged on the technological acceptance model, the instrumentalist theory of ethnicity and the theory of reasoned action.

The study found that tribal politics did not influence political candidates during the 2023 Nigerian presidential election. However, religious and party politics influenced their choices as well as X (Twitter), found as the most used and most influential social media platform vital for enhancing participatory democracy and informing people at real-time.

The researchers experienced challenges such as ensuring that the respondents filled the questions appropriately to reduce the number of void questionnaires and a funding problem since they had yet to receive any grant to enhance the study.

The study commends improved Internet connectivity and accessibility among the citizens for increased political engagement on social media. It also recommends that the Nigerian government enforce the rule of law in politics to enable diverse tribes and religions to experience democratic e-participation and development without marginalisation or subjugation by incumbent power. The findings affirm that social media is apt in political communication during the 2023 presidential elections in Nigeria. The study is a contribution to knowledge, timely and original.

In this study, the authors sought to investigate how the implicit social ties of both project owners and potential backers are associated with crowdfunding project success.

Drawing on social ties theory and factors that affect crowdfunding success, in this research, the authors developed a model to study how project owners' and potential backers' implicit social ties are associated with crowdfunding projects' degrees of success. The proposed model was empirically tested with crowdfunding data collected from Kickstarter and social media data collected from Twitter. The authors performed the test using an ordinary least squares (OLS) regression model with fixed effects.

The authors found that project owners' implicit social ties (specifically, their social media activities, degree centrality and betweenness centrality) are significantly and positively associated with crowdfunding projects' degrees of success. Meanwhile, potential project backers' implicit social ties (their social media activities and degree centrality) are negatively associated with crowdfunding projects' degrees of success. The authors also found that project size moderates the effects of project owners' social media activities on projects' degrees of success.

This work contributes to the literature on crowdfunding by investigating how the implicit social ties of both potential backers and project owners on social media are associated with crowdfunding project success. This study extends the previous research on social ties' roles in explaining crowdfunding project success by including implicit social ties, while the literature explored only explicit social ties.

This research aims to understand the dynamics that drive consumer engagement of multinational brands' social media posts on platform X, formerly known as Twitter. Taking the emotional tone of posts into account, the effect of vivid, interactive, informative, entertaining and practical features of posts on consumer interactions are evaluated across English- and Turkish-speaking markets.

Inspired by the conceptual framework proposed in previous literature, features were extracted computationally using natural language processing from platform X posts of 33 Fortune 500 brands from various industries from June 2016 to June 2021. Following evaluation of regression models on alternative distributions of the dependent variable, which is total number of likes, shares and comments, random subspace regression using bootstrap resampling was applied to calculate an importance score and evaluate the effect of features.

Consumers in English- and Turkish-speaking markets perceive and engage with content differently. While informative and entertaining posts resonate more with English speakers, emotions play a broader role for Turkish speakers. English-speaking audience prefers happy and vivid daytime messages with questions, while Turkish-speaking audience is drawn to angry messages, lean toward nighttime posts.

This research is a pioneer to evaluate the factors that influence brands' platform X post engagements across markets of different cultural orientation. Beyond assessing the distinctions in brand post elements, the role of emotional content in brand messages were also analyzed across English- and Turkish-speaking markets.

The purpose of this study is to examine how the interplay of stressor (e.g. fear of missing out, FoMO) and strains (e.g. perceived social overload, communication overload, information overload and system feature overload) in social networking sites (SNS) use can contribute to users’ SNS fatigue from a configurational view.

Data were collected among 363 SNS users in China via an online survey, and fuzzy-set qualitative comparative analysis (fsQCA) was applied in this study to scrutinize the different combinations of FoMO and overload that contribute to the same outcome of SNS fatigue.

Six combinations of casual conditions were identified to underlie SNS fatigue. The results showed that FoMO, perceived information overload and system feature overload are the core conditions that contribute to SNS fatigue when combined with other types of overloads.

The current work supplements the research findings on SNS fatigue by identifying the configurations contributing to SNS fatigue from the joint effects of stressor (FoMO) and strain (perceived social overload, communication overload, information overload and system feature overload) and by providing explanations for SNS fatigue from the configurational perspective.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

This study investigates user preferences for motivational features aligned with self-determination theory (SDT), emphasizing autonomy, relatedness, and competency. The study seeks to identify the most appealing and effective motivational features in AI-driven mobile apps for fostering autonomy, promoting relatedness, and enhancing feelings of competency in the context of reading comprehension.

Employing a mixed-methods research design based on the self-determination theory, the study used triangulation of methods to enhance the research findings' quality, completeness, validity, and understanding.

Both qualitative and quantitative findings showed similarities in the emphasis on personalization, progress tracking, social interaction, gamification, differences in specific feature preferences, and nuanced recommendations, highlighting the importance of a comprehensive and user-centric development approach.

The study design and sample size might constrain the statistical power and generalizability of the results. The study used a convenience sample of students from one school.

The synthesis of findings suggests the need for a comprehensive development strategy that incorporates both the commonalities and nuanced recommendations from qualitative and quantitative data. Combining the strengths of both findings, a holistic user-centric approach that integrates personalization, progress tracking, social interaction, and gamification is crucial for effective app development. Recognizing that individual preferences may vary, developers should consider tailoring features to accommodate a range of user needs, as indicated by qualitative and quantitative insights.

This comprehensive approach contributes to advancing knowledge in AI-driven mobile applications and their impact on student motivation and reading comprehension in the Philippine context. Also, it provides valuable insights for designers and developers, contributing to creating engaging and user-friendly platforms that enhance reading comprehension skills.