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As human beings step into the age of information network, big data technology is constantly improving the intelligence level of various agents such as individuals and enterprises. The crowd decision-making of the intellectual community plays an important role in the active participation of many individuals and schools in giving their wisdom, effectively solve the problems of negative internet communication, single publicity media and unprofessional promotion team in WeChat public account.

This paper aims to optimize the content and improve the effectiveness of network ideological and political education in universities. This study analyzes five highly popular WeChat public accounts at the Central University of Finance and Economics in 2019. It obtains the popularity index of tweets using the WeChat communication index algorithm and finds that the important factors that influence tweet popularity are release time and content value.

To improve the public account tweets, this study highlights the connection between the tweets’ value and students’ emotional needs, which enhances the value of tweet content in students’ life and provides more original and distinctive content.

This study found that the content and interest of college students are tweet time, tweet value and tweet content. Therefore, the public account of college ideological and political education should be improved from the following three aspects: realizing the connection between the value of tweet content and students’ emotional needs; enhancing the value of tweet content in students’ life and learning; and insisting on the original and distinctive original intention of tweet content.

The research objective of the paper is twofold. First, it scrutinises the current state of the art concerning adopting the most popular social media by European port managing bodies (PMBs). Second, it investigates the use of social media in the corporate social responsibility (CSR) communication strategies of European PMBs.

The paper carries out online field research on the use of social media by the top-25 European ports. Then, it provides an in-depth case study of the use of Twitter by the Port of Rotterdam for CSR communication. Finally, a content analysis of the tweets published in the 2017–2019 timeframe is performed.

Empirical results demonstrate the extensive use of social media by European PMBs to reach a wider array of stakeholders. Uneven approaches emerge considering port sizes and cultural clusters. The content analysis shows that one-third of tweets published by the Twitter account of the Port of Rotterdam address CSR issues, especially green initiatives, advocating the use of social media to communicate CSR.

The study focuses on the European domain. A broader sample of ports worldwide should be examined to further investigate the drivers affecting PMBs' strategic adoption and use of social media, mainly to communicate CSR.

The paper provides port managers with insights to strengthen CSR communication. Given the increasing pressure of the public opinion on environmental and social issues, the ability of European PMBs to communicate their CSR commitment through social media represents a key driver when searching for consensus of stakeholders and “licence to operate”.

The paper adds to the existing maritime logistics literature by introducing a promising field of research.

This chapter examines how established media – that is, print, TV and radio sources which pre-existed the popularisation of social media – use social media to disseminate content. Specifically it examines the manner in which three UK media sources – BBC News, The Guardian and the Daily Mail – used Twitter during the 2014–2015 Ebola crisis. It asks five key questions concerning: the balance between factual reporting and opinion or comment; the degree to which it shifted attention to specific events within the context of the outbreak; whether the dialogical potential of social media was exploited; the degree to which social media acted as a signpost to more detailed information elsewhere, or existed as independent content; and the degree of media reflexivity. It concludes that established media used this new technology within their existing paradigms for reporting rather than exploiting some of its more innovative characteristics.

This study is motivated to provide a theoretically informed, data-driven assessment of the consequences associated with the participation of non-human bots in social accountability movements; specifically, the anti-inequality/anti-corporate #OccupyWallStreet conversation stream on Twitter.

A latent Dirichlet allocation (LDA) topic modeling approach as well as XGBoost machine learning algorithms are applied to a dataset of 9.2 million #OccupyWallStreet tweets in order to analyze not only how the speech patterns of bots differ from other participants but also how bot participation impacts the trajectory of the aggregate social accountability conversation stream. The authors consider two research questions: (1) do bots speak differently than non-bots and (2) does bot participation influence the conversation stream.

The results indicate that bots do speak differently than non-bots and that bots exert both weak form and strong form influence. Bots also steadily become more prevalent. At the same time, the results show that bots also learn from and adapt their speaking patterns to emphasize the topics that are important to non-bots and that non-bots continue to speak about their initial topics.

These findings help improve understanding of the consequences of bot participation within social media-based democratic dialogic processes. The analyses also raise important questions about the increasing importance of apparently nonhuman actors within different spheres of social life.

The current study is the first, to the authors’ knowledge, that uses a theoretically informed Big Data approach to simultaneously consider the micro details and aggregate consequences of bot participation within social media-based dialogic social accountability processes.

Corporate social responsibility (CSR) communication is becoming increasingly important for brands and companies. Social media such as Twitter may be platforms particularly suited to this topic, given their ability to foster dialogue and content diffusion. The purpose of this paper is to investigate factors driving the effectiveness of CSR communication on Twitter, with a focus on the communication strategies and elements of storytelling.

Using a sample of 281,291 tweets from top global companies in the food sector, automated content analysis (including supervised machine learning) was used to investigate the influence of CSR communication, emotion, and aspirational talk on the likelihood that Twitter users will retweet and like tweets from the companies.

The findings highlight the importance of aspirational talk and engaging users in CSR messages. Furthermore, the study revealed that the companies and brands on Twitter that tweeted more frequently about CSR were associated with higher overall levels of content diffusion and endorsement.

This study provides important insights into key aspects of communicating about CSR issues on social networking sites such as Twitter and makes several practical recommendations for companies.

The authors examine the contemporaneous and causal association between tweet features (bullishness, message volume and investor agreement) and market features (stock returns, trading volume and volatility) using 140 South African companies and a dataset of firm-level Twitter messages extracted from Bloomberg for the period 1 January 2015 to 31 March 2020.

Panel regressions with ticker fixed-effects are used to examine the contemporaneous link between tweet features and market features. To examine the link between the magnitude of tweet features and stock market features, the study uses quantile regression.

No monotonic relationship is found between the magnitude of tweet features and the magnitude of market features. The authors find no evidence that past values of tweet features can predict forthcoming stock returns using daily data while weekly and monthly data shows that past values of tweet features contain useful information that can predict the future values of stock returns.

The study is among the earlier to examine the association between textual sentiment from social media and market features in a South African context. The exploration of the relationship across the distribution of the stock market features gives new insights away from the traditional approaches which investigate the relationship at the mean.

The purpose of this paper is to explore how the health ministries of Turkey and Slovenia use Twitter as government agencies obliged to communicate with the public.

This study employed a content analysis methodology to examine how Turkish (TR) and Slovenian (SLO) health ministries use Twitter for informing and encouraging behavior change in the public. A total of 662 “tweets” were analyzed. Drawing on prior studies, a coding scheme was developed and employed, and χ² and t-tests were conducted for data analysis. Additionally, this study aimed at effecting a content analysis according to the “four models” method of Grunig and Hunt regarding efforts made to build communication with the public.

This study uncovered that the TR and SLO health ministries did not utilize two-way communication principles for Twitter communication, and their frequency of Twitter use is inadequate.

The sampled tweets were selected by using a scientific sampling method. However, this might not have been substantial enough to represent the entirety of tweets in the study timeframe. Analyzing tweets across a longer timeframe would be helpful in confirming this study's findings. This study was also limited to two countries and to publicly available tweets; the messages of health ministries' followers to the ministries themselves were not examined. The findings of this study may not be generalizable to other countries. Other potential studies, with a particular focus on this topic, may be able to measure individual perceptions of the credibility and usefulness of messages from health ministries and their willingness to engage in two-way communication.

This study is one of the first to evaluate how the health ministries of Turkey and Slovenia communicate on Twitter and to apply the four models of Grunig and Hunt with regard to Twitter. This study also identified that noncompeting government agencies were not minded to communicate with their publics.

This study aims to analyze a backchannel account on news of the coronavirus at the beginning of the pandemic, with information not disseminated in official media due to the social alarm it might cause and the negative image of government management. Specifically, it examines acceptance and dissemination of this type of content in a period of lack of information, while reflecting on what would constitute proper management of this type of channel.

First, based on a literature review, this study classifies possible explanatory variables of online content dissemination into content richness and psychological content. Second, this study performs sentiment analysis of the Twitter backchannel account @COVID_19NEWS and use Qualitative Comparative Analysis to find causal configurations of variables that obtained a high rate of retweets.

The results reveal predominance of one combination of three factors in backchannel information diffusion: emotional, identifying and video content. Other interesting combinations of factors were shown to be attractive enough to contribute to success of the tweets.

Knowledge of the main configurations that attract information dissemination in backchannel accounts is useful for public management of a health crisis such as the Covid-19 outbreak. Rather than suppressing these channels, the authors discuss different solutions.

This study advances scholarship on backchannel communications in emergency situations, providing insights to understand and manage such channels.

Este estudio analiza una cuenta extraoficial sobre noticias del coronavirus al inicio de la pandemia, con información no difundida en los medios oficiales por su posible repercusión en la alarma social y la imagen negativa de la gestión gubernamental. Concretamente examina la aceptación y difusión de este contenido en un periodo de desinformación, así como reflexiona sobre la gestión de este tipo de canales.

En primer lugar, en base a la revisión de la literatura, clasificamos las variables explicativas según la riqueza de contenido y el contenido psicológico. En segundo lugar, sobre la cuenta extraoficial de @COVID_19NEWS en Twitter, realizamos análisis de sentimiento y utilizamos Análisis Comparativo Cualitativo (QCA) para encontrar configuraciones causales de variables que obtuvieron una alta tasa de retweets.

Los resultados revelan la importancia de una combinación de tres factores en la difusión de información del canal secundario: contenido emocional, identificativo y video. Otras combinaciones de factores también contribuyeron al éxito del tweet.

Estas configuraciones podrían ser útiles para la gestión pública ante una crisis sanitaria como la Covid-19, prestando atención a los factores cuya configuración atrae la difusión de información en las RRSS. En lugar de suprimir estos canales, se presentan soluciones para garantizar una colaboración eficaz.

Este estudio realiza una contribución académica a las comunicaciones extraoficiales en situaciones de emergencia, proporcionando información para comprender y gestionar este tipo de canales.

Covid-19, Coronavirus, Canal extraoficial, Twitter, Análisis cualitativo comparado

Trabajo de investigación

在新冠疫情初期, 由于可能引起社会恐慌和政府管理部门的负面形象, 官方媒体缺少相关的新闻报道。本文研究了在这种官方信息匮乏的危机时期, 非正式渠道（backchannel）对于新冠病毒内容的接受和传播情况, 本文同时反思了如何对这类非正式渠道进行正确的管理。

基于文献综述, 我们先将在线内容传播的可能解释变量分为内容丰富度和心理内容这两个方面。其次, 我们对推特上的非正式渠道账户@COVID_19NEWS发布的内容进行情感分析, 并使用定性比较分析法来寻找内容获得高转发率的原因。

结果显示, 对于非正式渠道信息的成功传播, 情绪化、具有辩认度和包含视频内容这三个要素的组合占主导地位。此外, 其他要素的组合也有来助于推文的成功传播和扩散。

了解非正式渠道吸引信息传播的主要原因, 将有利于应对健康危机（例如Covid-19爆发）和进行公共管理。文本讨论了不同的解决方案, 而不是简单地压制这些非正式渠道。

这项研究推进了危机背景下非正式渠道传播的学术研究, 为理解和管理这类非正式渠道提供了见解。

关键词 - Covid-19, 新冠病毒, 非正式渠道, 推特, 定性比较分析

Social networks (SNs) have recently evolved from a means of connecting people to becoming a tool for social engineering, radicalization, dissemination of propaganda and recruitment of terrorists. It is no secret that the majority of the Islamic State in Iraq and Syria (ISIS) members are Arabic speakers, and even the non-Arabs adopt Arabic nicknames. However, the majority of the literature researching the subject deals with non-Arabic languages. Moreover, the features involved in identifying radical Islamic content are shallow and the search or classification terms are common in daily chatter among people of the region. The authors aim at distinguishing normal conversation, influenced by the role religion plays in daily life, from terror-related content.

This article presents the authors' experience and the results of collecting, analyzing and classifying Twitter data from affiliated members of ISIS, as well as sympathizers. The authors used artificial intelligence (AI) and machine learning classification algorithms to categorize the tweets, as terror-related, generic religious, and unrelated.

The authors report the classification accuracy of the K-nearest neighbor (KNN), Bernoulli Naive Bayes (BNN) and support vector machine (SVM) [one-against-all (OAA) and all-against-all (AAA)] algorithms. The authors achieved a high classification F1 score of 83\%. The work in this paper will hopefully aid more accurate classification of radical content.

In this paper, the authors have collected and analyzed thousands of tweets advocating and promoting ISIS. The authors have identified many common markers and keywords characteristic of ISIS rhetoric. Moreover, the authors have applied text processing and AI machine learning techniques to classify the tweets into one of three categories: terror-related, non-terror political chatter and news and unrelated data-polluting tweets.

This paper aims to illustrate the scope and challenges of using computer-aided content analysis in international marketing with the aim to capture consumer sentiments about COVID-19 from multi-lingual tweets.

The study is based on some 35 million original COVID-19-related tweets. The study methodology illustrates the use of supervised machine learning and artificial neural network techniques to conduct extensive information extraction.

The authors identified more than two million tweets from six countries and categorized them into PESTEL (i.e. Political, Economic, Social, Technological, Environmental and Legal) dimensions. The extracted consumer sentiments and associated emotions show substantial differences across countries. Our analyses highlight opportunities and challenges inherent in using multi-lingual online sentiment analysis in international marketing. Based on these insights, several future research directions are proposed.

First, the authors contribute to methodology development in international marketing by providing a “use-case” for computer-aided text mining in a multi-lingual context. Second, the authors add to the knowledge on differences in COVID-19-related consumer sentiments in different countries. Third, the authors provide avenues for future research on the analysis of unstructured multi-media posts.