During the COVID-19 pandemic, social media served as an important channel for the public to obtain health information and disseminate opinions when offline communication was severely hindered. Yet the emergence of social bots influencing social media conversations about public health threats will require researchers and practitioners to develop new communication strategies considering their influence. So far, little is known as to what extent social bots have been involved in COVID-19 vaccine-related discussions and debates on social media. This work selected a period of nearly 9 months after the approval of the first COVID-19 vaccines to detect social bots and performed high-frequency word analysis for both social bot-generated and human-generated tweets, thus working out the extent to which social bots participated in the discussion on the COVID-19 vaccine on Twitter and their participation features. Then, a textual analysis was performed on the content of tweets. The findings revealed that 8.87% of the users were social bots, with 11% of tweets in the corpus. Besides, social bots remained active over three periods. High-frequency words in the discussions of social bots and human users on vaccine topics were similar within the three peaks of discourse.

This research explored how perceived homophily and reverence of consumers bridge the gap between endorser characteristics and consumer-celebrity para-social interaction (PSI). Online surveys were utilized to collect data from consumers. The results based on structural equation modeling showed that the perceived attractiveness and expertise of a celebrity were separately antecedent to the perceived homophily and reverence of consumers for the celebrity. This in turn allowed consumers to build PSI with the celebrity and led to a positive attitude toward the celebrity-endorsed brand. No differences were found between non-fans and fans of the selected celebrity regarding the confirmed path from celebrity characteristics to consumer brand attitudeviaPSI and its influencing factors. This work highlighted the significance of consumer-celebrity relations for endorsement effectiveness through proving consumer-celebrity PSI and its drivers as indispensable steps in the endorsement process.

The advancement in technology is taking place with an accelerating pace across the globe. With the increasing expansion and technological advancement, a vast volume of text data are generated everyday, in the form of social media platform, websites, company data, healthcare data, and news. Indeed, it is a difficult task to extract intriguing patterns from the text data, such as opinions, summaries, and facts, having varying length. Because of the problems of the length of text data and the difficulty of feature value extraction in news, this paper proposes a news text classification method based on the combination of deep learning (DL) algorithms. In order to classify the text data, the earlier approaches use a single word vector to express text information and only the information of the relationship between words were considered, but the relationship between words and categories was ignored which indeed is an important factor for the classification of news text. This paper follows the idea of a customized algorithm which is the combination of DL algorithms such as CNN, LSTM, and MLP and proposes a customized DCLSTM-MLP model for the classification of news text data. The proposed model is expressed in parallel with word vector and word dispersion. The relationship among words is represented by the word vector as an input of the CNN module, and the relationship between words and categories is represented by a discrete vector as an input of the MLP module in order to realize comprehensive learning of spatial feature information, time-series feature information, and relationship between words and categories of news text. To check the stability and performance of the proposed method, multiple experiments were performed. The experimental results showed that the proposed method solves the problems of text length, difficulty of feature extraction in the news text, and classification of news text in an effective way and attained better accuracy, recall rate, and comprehensive value as compared to the other models.

During the COVID-19 pandemic, social media has become an emerging platform for the public to find information, share opinions, and seek coping strategies. Vaccination, one of the most effective public health interventions to control the COVID-19 pandemic, has become the focus of public online discussions. Several studies have demonstrated that social bots actively involved in topic discussions on social media and expressed their sentiments and emotions, which affected human users. However, it is unclear whether social bots’ sentiments affect human users’ sentiments of COVID-19 vaccines. This study seeks to scrutinize whether the sentiments of social bots affect human users’ sentiments of COVID-19 vaccines. The work identified social bots and built an innovative computational framework, i.e., the BERT-CNN sentiment analysis framework, to classify tweet sentiments at the three most discussed stages of COVID-19 vaccines on Twitter from December 2020 to August 2021, thus exploring the impacts of social bots on online vaccine sentiments of humans. Then, the Granger causality test was used to analyze whether there was a time-series causality between the sentiments of social bots and humans. The findings revealed that social bots can influence human sentiments about COVID-19 vaccines. Their ability to transmit the sentiments on social media, whether in the spread of positive or negative tweets, will have a corresponding impact on human sentiments.

Despite growing recognition of social bots’ role in swaying public opinion, evidence of their specific impact on public cognition remains limited. Applying the network agenda-setting theory and multiple regression quadratic assignment procedure (MRQAP) analysis of X (formerly Twitter) posts, we examine the relative impact of social bots and legacy media on public agenda networks over time, focusing on China’s dual-carbon policy. Findings reveal that social bots’ agenda networks exert a significant influence on public agenda networks, with their impact being more pronounced than that of legacy media during the initial period (T1). However, this influence diminishes in the subsequent period (T2), suggesting that social bots have a potent yet short-lived effect on shaping public cognitive networks around specific issues. Furthermore, social bots exhibited more negative attitudes towards dual-carbon topics, contrasting with the neutral stance of legacy media. A comparative semantic network analysis highlights the distinct narrative structures promoted by social bots and legacy media. This research provides crucial insights into automated actors’ roles in molding environmental discourse and public perception.