Understanding the efficiency of social media based crisis communication during hurricane Sandy

Rapid communication during extreme events is one of the critical aspects of successful disaster management strategies. Due to their ubiquitous nature, social media platforms are expected to offer a unique opportunity for crisis communication. In this study, about 52.5 million tweets related to hurricane Sandy posted by 13.75 million users are analyzed to assess the effectiveness of social media communication during disasters and identify the contributing factors leading to effective crisis communication strategies. Efficiency of a social media user is defined as the ratio of attention gained over the number of tweets posted. A model is developed to identify more efficient users based on several relevant features. Results indicate that during a disaster event, only few social media users become highly efficient in gaining attention. In addition, efficiency does not depend on the frequency of tweeting activity only; instead it depends on the number of followers and friends, user category, bot score (controlled by a human or a machine), and activity patterns (predictability of activity frequency). Since the proposed efficiency metric is easy to evaluate, it can potentially detect effective social media users in real time to communicate information and awareness to vulnerable communities during a disaster.     

[2015年第1期] 社会事件网络舆情的政府干预机制

社会事件的前卫指标来源于网络, 随着网络技术平台的发展, 使得信息 网络传播更为迅速和广泛,给社会事件的舆情应对带来诸多挑战。 文章以社会事 件网络舆情干预机制为研究对象, 从舆情传播过程的视角 , 提出社会事件网络舆 情干预机制的 4 个阶段, 即舆情干预判断、干预时机、干预措施和干预评价。 此 外, 文章还具体分析了各关键干预环节的技术解决方案, 并给出 舆情干预的政策 建议,为政府及相关部门对舆情的应对提供借鉴。     

Modeling and simulation of microblog-based public health emergency-associated public opinion communication

With the advent of the era of “we media,” many people's opinions have become easily accessible. Public health emergencies have always been an important aspect of public opinion exchange and emotional communication. In view of this sudden group panic, public opinion cannot be effectively monitored, controlled or guided. This makes it easy to amplify the beliefs and irrationality of social emotions, that threaten social security and stability. Considering the important role of opinion leaders in micro-blogs and users’ interest in micro-blog information, a SIR model of public opinion propagation is constructed based on the novel coronavirus pneumonia model and micro-blog's public health emergencies information. The parameters of the model are calculated by combining the actual crawl data from the novel coronavirus pneumonia epidemic period, and the trends in the evolution of public opinion are simulated by MATLAB. The simulation results are consistent with the actual development of public opinion dissemination, which shows the effectiveness of the model. These research findings can help the government understand the principles that guide the propagation of public opinion and advise an appropriate time to control and correctly guide public opinion.     

期刊公众账号对学术文献社会传播的推动作用分析

【目的】 通过期刊公众账号对学术文献转载的计量分析,探究期刊公众账号在学术文献社会传播中发挥的作用。【方法】 以48种进化生物学SCI收录期刊2010—2017年发表的学术文献为研究对象,抓取文献在Twitter平台的转载数据,采用数据计量分析方法探究期刊公众账号对文献转载的影响。【结果】(1)具有公众账号的期刊所发表的文献被大量转载,高被转载文献也大多来自于设有公众账号的期刊,且该类期刊发文的被引和被转载之间的相关性更为显著。(2)“OfficialSMBE”“EcoEvoJournals”和“RSocPublishing”等期刊公众账号转载了大量文献,是转载群体共线网络中的高中心性节点;其次,“OfficialSMBE”“EcoEvoJournals”和“BehavEcolPapers”等期刊公众账号为文献的高频首次转载账号,在文献的转载中发挥了引领的作用。【结论】 设有公众账号的期刊发文有较为显著的转载优势,期刊公众账号推动了学术文献的社会传播。     

Understanding trust influencing factors in social media communication: A qualitative study

Based on five types of trust, this research explores trust influencing factors in peer-to-peer interpersonal communication, group communication and mass communication. Previous research has mainly focused on trust and the corresponding antecedents in electronic commerce communication and online collaboration. This study extends the literature on trust influencing factors in social media communication. A trust traffic light model is used to illustrate the importance of keywords, drawn from interviews with 115 participants who use WeChat frequently. Salient trust factors were found and further elaborated through qualitative analysis. Furthermore, we developed a trust cognitive onion model to illustrate the interactions of trust factors.     

Identifying emotional causes of mental disorders from social media for effective intervention

Identifying the emotional causes of mental illnesses is key to effective intervention. Existing emotion-cause analysis approaches can effectively detect simple emotion-cause expressions where only one cause and one emotion exist. However, emotions may often result from multiple causes, implicitly or explicitly, with complex interactions among these causes. Moreover, the same causes may result in multiple emotions. How to model the complex interactions between multiple emotion spans and cause spans remains under-explored. To tackle this problem, a contrastive learning-based framework is presented to detect the complex emotion-cause pairs with the introduction of negative samples and positive samples. Additionally, we developed a large-scale emotion-cause dataset with complex emotion-cause instances based on subreddits associated with mental health. Our proposed approach was compared to prevailing CNN-based, LSTM-based, Transformer-based and GNN-based methods. Extensive experiments have been conducted and the quantifiable outcomes indicate that our proposed solution achieves competitive performance on simple emotion-cause pairs and significantly outperformed baseline methods in extracting complex emotion-cause pairs. Empirical studies further demonstrated that our proposed approach can be used to reveal the emotional causes of mental disorders for effective intervention.     

TASR: Adversarial learning of topic-agnostic stylometric representations for informed crisis response through social media

The impact of crisis events can be devastating in a multitude of ways, many of which are unpredictable due to the suddenness in which they occur. The evolution of social media (for example Twitter) has given directly affected individuals or those with valuable information a platform to effectively share their stories to the masses. As a result, these platforms have become vast repositories of helpful information for emergency organizations. However, different crisis events often contain event-specific keywords, which results in the difficult extraction of useful information with a single model. In this paper, we put forward TASR, which stands for Topic-Agnostic Stylometric Representations, a novice deep learning architecture that uses stylometric and adversarial learning to remove topical bias to better manage the unknown surrounding unseen events. As an alternative to domain adaptive approaches requiring data from the unseen event, it reduces the work for those responding to the onset of a crisis. Overall, we conduct a comprehensive study of the situational properties of TASR, the benefits of its architecture including its topic-agnostic and explainable properties, and how it improves upon comparable models in past research. From two experiments, on average, TASR is able to outperform state-of-the-art methods such as transfer learning and domain adoption by 11% in AUC. The ablation study illustrates how different architecture choices of TASR impact the results and that TASR has been optimized for this task. Finally, we conduct a case study to show that explainable results from our model can be used to help guide human analysts through crisis information extraction.     

危机管理模式在突发公共卫生事件中的作用

突发公共卫生事件在人类历史上由来已久。在我国,随着近年来突发公共卫生事件的增多,建立突发事件应急体制和相关法律,研究应对突发公共卫生事件的预警机制以及理论和技术支撑,也越来越受到重视。本文分析在应对突发公共卫生事件中危机管理模式的意义和作用,笔者就此浅谈危机管理模式作用。     

Sharing knowledge in the public sector: two case studies

Knowledge Management Research & Practice - In important areas of the public sector, client service depends on cooperation and collaboration among workers in different organizations or agencies....     

Understanding public opinions on social media for financial sentiment analysis using AI-based techniques

The digital currency has taken the financial markets by storm ever since its inception. Academia and industry are focussing on Artificial intelligence (AI) tools and techniques to study and gain an understanding of how businesses can draw insights from the large-scale data available online. As the market is driven by public opinions, and social media today provides an encouraging platform to share ideas and views; organizations and policy-makers could use the natural language processing (NLP) technology of AI to analyze public sentiments. Recently, a new and moderately unconventional instrument known as non-fungible tokens (NFTs) is emerging as an upcoming business market. Unlike the stock market, no precise quantitative parameters exist for the price determination of NFTs. Instead, NFT markets are driven more by public opinion, expectations, the perception of buyers, and the goodwill of creators. This study evaluates human emotions on the social media platforms Twitter posted by the public relating to NFTs. Additionally, this study conducts secondary market analysis to determine the reasons for the growing acceptance of NFTs through sentiment and emotion analysis. We segregate tweets using Pearson Product-Moment Correlation Coefficient (PPMCC) and study 8-scale emotions (Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, and Trust) along with Positive and Negative sentiments. Tweets majorly contained positive sentiment (～ 72%), and positive emotions like anticipation and trust were found to be predominant all over the world. This is the first of its kind financial and emotional analysis of tweets pertaining to NFTs to the best of our understanding.     

A symbolic interactionism perspective of using social media for personal and business communication

Achieving the anticipated business benefits of a social medium is important as organizations diligently invest in different social media platforms. While much previous research assumes that social media helps organizations to communicate with customers, less is known about whether customers embrace using social media to interact with organizations. It is important to understand the role of social media for business communication from the customers’ perspective, as this may significantly deviate from the organizations’ own communicative intentions. In this exploratory case study of the Moon Struck hotel in China, we investigate both how customers interpret the hotel’s use of WeChat official account for business communication and how customers respond to messages received from Moon Struck’s WeChat account. Adopting a symbolic interactionism perspective, we surprisingly find that WeChat personal accounts and Moon Struck’s official account offer radically different meanings to followers. Specifically, WeChat personal account symbolizes a sociality-oriented meaning (e.g., relationship and image building), while Moon Struck’s WeChat official account symbolizes information broadcasting-related meaning (e.g., selling, advertising, and branding). Both technological features and the distance of relationships among users contribute to the constructed symbolic meaning of technology, subsequently affecting users’ WeChat use patterns. The theoretical implications of this study are discussed and recommendations are made for future research and practice.     

Predicting positive user responses to social media advertising: The roles of emotional appeal,informativeness, and creativity

As social network services become more pervasive, social media advertising emerges as an attractive vehicle for augmenting advertising effectiveness. To leverage this new means of marketing, one must understand what engages SNS users in a favorable online behavior (i.e., overtly indicating personal interest in, or support for, the exposed message by clicking the Like or Share button in Facebook), thereby resulting in an effective advertising campaign. This research conceptualizes SNS ad effectiveness as a concept encompassing emotional appeal, informativeness and creativity that all have a potential to contribute to a positive online behavior. It empirically investigates the antecedents of positive user behavior for a SNS ad based on the theory of reasoned action, the social influence theory, and a persuasion theory. It proposes and tests a conceptual model of the formation of online user’s behavioral responses with regards to SNS advertising. The results of our empirical tests of the model reveal that informativeness and advertising creativity were key drivers of favorable behavioral responses to an SNS ad and that intention to engage in favorable user responses was positively associated with purchase intention. Based on these findings, the paper suggests further research directions and offers implications for harnessing the full potential of the new SNS advertising platform.     

Characterizing the dissemination of misinformation on social media in health emergencies: An empirical study based on COVID-19

The dissemination of misinformation in health emergencies poses serious threats to public health and increases health anxiety. To understand the underlying mechanism of the dissemination of misinformation regarding health emergencies, this study creatively draws on social support theory and text mining. It also explores the roles of different types of misinformation, including health advice and caution misinformation and health help-seeking misinformation, and emotional support in affecting individuals’ misinformation dissemination behavior on social media and whether such relationships are contingent on misinformation ambiguity and richness. The theoretical model is tested using 12,101 textual data about COVID-19 collected from Sina Weibo, a leading social media platform in China. The empirical results show that health caution and advice, help seeking misinformation, and emotional support significantly increase the dissemination of misinformation. Furthermore, when the level of ambiguity and richness regarding misinformation is high, the effect of health caution and advice misinformation is strengthened, whereas the effect of health help-seeking misinformation and emotional support is weakened, indicating both dark and bright misinformation ambiguity and richness. This study contributes to the literature on misinformation dissemination behavior on social media during health emergencies and social support theory and provides implications for practice.     

新媒体传播对转发意愿及品牌态度的影响研究

传统传播环境下企业营销传播活动对用户品牌态度形成具有显著影响,但社会化媒体的发展极大地改变了企业营销传播的生态环境,现阶段企业的社会化媒体传播并未获得预期影响力,需要从理论上对企业社会化媒体传播的策略及其影响因素进行创新性研究。本文采用实验研究方法,基于企业传播信息内容主题、信息源、传播策略与用户再传播意愿和品牌态度间关系的理论假设,实证研究发现:企业社会化媒体传播对用户品牌态度有正向显著影响;信息内容主题类型、信息源、传播组合策略对用户再传播意愿有显著影响;用户再传播意愿对用户品牌态度的影响不显著等。研究结论丰富了企业社会化媒体传播的理论研究,对企业社会化媒体传播实践具有指导意义。     

COVID-19, digital privacy,and the social limits on data-focused public health responses

The implementation of digital contact tracing applications around the world to help reduce the spread of the COVID-19 pandemic represents one of the most ambitious uses of massive-scale citizen data ever attempted. There is major divergence among nations, however, between a “privacy-first” approach which protects citizens’ data at the cost of extremely limited access for public health authorities and researchers, and a “data-first” approach which stores large amounts of data which, while of immeasurable value to epidemiologists and other researchers, may significantly intrude upon citizens’ privacy. The lack of a consensus on privacy protection in the contact tracing process creates risks of non-compliance or deliberate obfuscation from citizens who fear revealing private aspects of their lives – a factor greatly exacerbated by recent major scandals over online privacy and the illicit use of citizens’ digital information, which have heightened public consciousness of these issues and created significant new challenges for any collection of large-scale public data. While digital contact tracing for COVID-19 remains in its infancy, the lack of consensus around best practices for its implementation and for reassuring citizens of the protection of their privacy may already have impeded its capacity to contribute to the pandemic response.     

Flourishing on facebook: virtue friendship & new social media

The widespread and growing use of new social media, especially social networking sites such as Facebook and Twitter, invites sustained ethical reflection on emerging forms of online friendship. Social scientists and psychologists are gathering a wealth of empirical data on these trends, yet philosophical analysis of their ethical implications remains comparatively impoverished. In particular, there have been few attempts to explore how traditional ethical theories might be brought to bear upon these developments, or what insights they might offer, if any. In attempting to address this lacuna in applied ethical research, this paper investigates the ethical significance of online friendship by means of an Aristotelian theory of the good life, which holds that human flourishing is chiefly realized through ??complete?? friendships of virtue. Here, four key dimensions of ??virtue friendship?? are examined in relation to online social media: reciprocity, empathy, self-knowledge and the shared life. Online social media support and strengthen friendship in ways that mirror these four dimensions, particularly when used to supplement rather than substitute for face-to-face interactions. However, deeper reflection on the meaning of the shared life (suzên) for Aristotle raises important and troubling questions about the capacity of online social media to support complete friendships of virtue in the contemporary world, along with significant concerns about the enduring relevance of this Aristotelian ideal for the good life in the 21st century.     

Recognizing fake information through a developed feature scheme: A user study of health misinformation on social media in China

This study aims at helping people recognize health misinformation on social media in China. A scheme was first developed to identify the features of health misinformation on social media based on content analysis of 482 pieces of health information from WeChat, a social media platform widely used in China. This scheme was able to identify salient features of health misinformation, including exaggeration/absolutes, induced text, claims of being unique and secret, intemperate tone or language, and statements of excessive significance and likewise. The scheme was then evaluated in a user-centred experiment to test if it is useful in identifying features of health misinformation. Forty-four participants for the experimental group and 38 participants for the control group participated and finished the experiment, which compared the effectiveness of these participants in using the scheme to identify health misinformation. The results indicate that the scheme is effective in terms of improving users’ capability in health misinformation identification. The results also indicate that the participants’ capability of recognizing misinformation in the experimental group has been significantly improved compared to those of the control group. The study provides insights into health misinformation and has implications in enhancing people's online health information literacy. It informs the development of a system that can automatically limit the spread of health misinformation. Moreover, it potentially improves users’ online health information literacy, in particular, under the circumstances of the COVID-19 pandemic.     

Implicit media frames: automated analysis of public debate on artificial sweeteners

The framing of issues in the mass media plays a crucial role in the public understanding of science and technology. This article contributes to research concerned with the analysis of media frames over time by making an analytical distinction between implicit and explicit media frames, and by introducing an automated method for the analysis of implicit frames. In particular, we apply a semantic maps method to a case study on the newspaper debate about artificial sweeteners, published in the New York Times between 1980 and 2006. Our results show that the analysis of semantic changes enables us to filter out the dynamics of implicit frames, and to detect emerging metaphors in public debates. Theoretically, we discuss the relation between implicit frames in public debates and the codification of meaning and information in scientific discourses, and suggest further avenues for research interested in the automated analysis of frame changes and trends in public debates.     

News media,social media,and hyperlink networks: An examination of integrated media effects

Research typically focuses on one medium. But in today's digital media environment, people use and are influenced by their experience with multiple systems. Building on media ecology research, we introduce the notion of integrated media effects. We draw on resource dependence and homophily theories to analyze the mechanisms that connect media systems. To test the integrated media effects, we examine the relationships between news media visibility and social media visibility and hyperlinking patterns among 410 nongovernmental organization (NGO) websites in China. NGOs with greater news media visibility and more social media followers receive significantly more hyperlinks. Further, NGOs with a similar number of social media followers prefer to hyperlink to each other. The results suggest that both news media and social media systems are related to the configuration of hyperlink networks, providing support for the integrated media effects described. Implications for the study of hyperlink networks, online behaviors of organizations, and public relations are drawn from the results.     

Real-time processing of social media with SENTINEL: A syndromic surveillance system incorporating deep learning for health classification

Interest in real-time syndromic surveillance based on social media data has greatly increased in recent years. The ability to detect disease outbreaks earlier than traditional methods would be highly useful for public health officials. This paper describes a software system which is built upon recent developments in machine learning and data processing to achieve this goal. The system is built from reusable modules integrated into data processing pipelines that are easily deployable and configurable. It applies deep learning to the problem of classifying health-related tweets and is able to do so with high accuracy. It has the capability to detect illness outbreaks from Twitter data and then to build up and display information about these outbreaks, including relevant news articles, to provide situational awareness. It also provides nowcasting functionality of current disease levels from previous clinical data combined with Twitter data.The preliminary results are promising, with the system being able to detect outbreaks of influenza-like illness symptoms which could then be confirmed by existing official sources. The Nowcasting module shows that using social media data can improve prediction for multiple diseases over simply using traditional data sources.