A tale of two epidemics: Contextual Word2Vec for classifying twitter streams during outbreaks

Unstructured tweet feeds are becoming the source of real-time information for various events. However, extracting actionable information in real-time from this unstructured text data is a challenging task. Hence, researchers are employing word embedding approach to classify unstructured text data. We set our study in the contexts of the 2014 Ebola and 2016 Zika outbreaks and probed the accuracy of domain-specific word vectors for identifying crisis-related actionable tweets. Our findings suggest that relatively smaller domain-specific input corpora from the Twitter corpus are better in extracting meaningful semantic relationship than generic pre-trained Word2Vec (contrived from Google News) or GloVe (of Stanford NLP group). However, domain-specific quality tweet corpora during the early stages of outbreaks are normally scant, and identifying actionable tweets during early stages is crucial to stemming the proliferation of an outbreak. To overcome this challenge, we consider scholarly abstracts, related to Ebola and Zika virus, from PubMed and probe the efficiency of cross-domain resource utilization for word vector generation. Our findings demonstrate that the relevance of PubMed abstracts for the training purpose when Twitter data (as input corpus) would be scant during the early stages of the outbreak. Thus, this approach can be implemented to handle future outbreaks in real time. We also explore the accuracy of our word vectors for various model architectures and hyper-parameter settings. We observe that Skip-gram accuracies are better than CBOW, and higher dimensions yield better accuracy.     

Blockchain technology awareness on social media: Insights from twitter analytics

PurposeThis study investigates the affective technology acceptance model applied to the case of blockchain through Twitter text mining.Design/methodology/approachThe analysis focuses on mapping the acceptance drivers of the blockchain technology by visualizing the users perception constructs through Blockchain hashtags. More than 5000 relevant tweets per day were collected between December 15, 2020, and January 15, 2021. The Kruskal-Wallis and the Mann-Whitney tests were applied over the frequency of the characteristics and the emotions' measurements to validate the research hypotheses.FindingsThe results prove that users show more interest in security, shareability, and decentralization characteristics. Therefore, the blockchain technology usefulness is rather perceived in the informational domain, and the blockchain ease of use is further expressed in smart contracts as a use case. Blockchain benefits are more discussed than the drawbacks among Twitter users. Besides, positive feelings with strong emotions of trust and joy dominate among users. In summary, the results show significant awareness of users towards blockchain technology.OriginalityTo the best of the authors' knowledge, this paper is the first study that explores the affective technology acceptance model with user-generated content analysis.     

Automatic new topic identification using multiple linear regression

The purpose of this study is to provide automatic new topic identification of search engine query logs, and estimate the effect of statistical characteristics of search engine queries on new topic identification. By applying multiple linear regression and multi-factor ANOVA on a sample data log from the Excite search engine, we demonstrated that the statistical characteristics of Web search queries, such as time interval, search pattern and position of a query in a user session, are effective on shifting to a new topic. Multiple linear regression is also a successful tool for estimating topic shifts and continuations. The findings of this study provide statistical proof for the relationship between the non-semantic characteristics of Web search queries and the occurrence of topic shifts and continuations.     

Thoughts on major natural disasters

Amajor fact revealed by the Sichuan earthquake is that natural disasters （NDs） have become a formidable challenge that human beings across the world have to face today. The new millennium has witnessed the frequent occurrence of devastating catastrophes, which led to massive death tolls and property damages,and caused tremendous anguish to society.     

The effect of twitter dissemination on cost of equity: A big data approach

Reducing information asymmetry between investors and a firm can have an impact on the cost of equity, especially in an environment or times of uncertainty. New technologies can potentially help disseminate corporate financial information, reducing such asymmetries. In this paper we analyse firms’ dissemination decisions using Twitter, developing a comprehensive measure of the amount of financial information that a company makes available to investors (iDisc) from a big data of firms’ tweets (1,197,208 tweets). Using a sample of 4131 firm-year observations for 791 non-financial firms listed on the US NASDAQ stock exchange over the period 2009–2015, we find evidence that iDisc significantly reduces the cost of equity. These results are pronounced for less visible firms which are relatively small in size, have a low analyst following and a small number of investors. Highly visible firms are less likely to benefit from iDisc in influencing their cost of equity as other communication channels may have widely disseminated their financial information. Our investigations encourage managers to consider the benefits of directly spreading a firm’s financial information to stakeholders and potential investors using social media in order to reduce firm equity premium (COE).     

Optimism and pessimism analysis using deep learning on COVID-19 related twitter conversations

This paper proposes a new deep learning approach to better understand how optimistic and pessimistic feelings are conveyed in Twitter conversations about COVID-19. A pre-trained transformer embedding is used to extract the semantic features and several network architectures are compared. Model performance is evaluated on two new, publicly available Twitter corpora of crisis-related posts. The best performing pessimism and optimism detection models are based on bidirectional long- and short-term memory networks.Experimental results on four periods of the COVID-19 pandemic show how the proposed approach can model optimism and pessimism in the context of a health crisis. There is a total of 150,503 tweets and 51,319 unique users. Conversations are characterised in terms of emotional signals and shifts to unravel empathy and support mechanisms. Conversations with stronger pessimistic signals denoted little emotional shift (i.e. 62.21% of these conversations experienced almost no change in emotion). In turn, only 10.42% of the conversations laying more on the optimistic side maintained the mood. User emotional volatility is further linked with social influence.     

Automatic keyword identification by artificial neural networks compared to manual identification by users of filtering systems

Information filtering (IF) systems usually filter data items by correlating a vector of terms that represent the user profile with similar vectors of terms that represent data items. Terms that represent data items can be determined by experts or automatic indexing methods. In this study we employ an artificial neural network (ANN) as an alternative method for both IF and term selection and compare its effectiveness to that of “traditional” methods. In an earlier study we developed and examined the performance of an IF system that employed content-based and stereotypic rule-based filtering methods in the domain of e-mail messages. In this study, we train a large-scale ANN-based filter, which uses meaningful terms in the same database as input, and use it to predict the relevance of those messages. Our results reveal that the ANN relevance prediction out-performs the prediction of the IF system. Moreover, we found very low correlation between the terms in the user profile (explicitly selected by the users) and the positive causal-index (CI) terms of the ANN, which indicate the relative importance of terms in messages. This implies that the users underestimate the importance of some terms, failing to include them in their profiles. This may explain the rather low prediction accuracy of the IF system.     

Content features of tweets for effective communication during disasters: A media synchronicity theory perspective

Users’ ability to retweet information has made Twitter one of the most prominent social media platforms for disseminating emergency information during disasters. However, few studies have examined how Twitter’s features can support the different communication patterns that occur during different phases of disaster events. Based on the literature of disaster communication and Media Synchronicity Theory, we identify distinct disaster phases and the two communication types—crisis communication and risk communication—that occur during those phases. We investigate how Twitter’s representational features, including words, URLs, hashtags, and hashtag importance, influence the average retweet time—that is, the average time it takes for retweet to occur—as well as how such effects differ depending on the type of disaster communication. Our analysis of tweets from the 2013 Colorado floods found that adding more URLs to tweets increases the average retweet time more in risk-related tweets than it does in crisis-related tweets. Further, including key disaster-related hashtags in tweets contributed to faster retweets in crisis-related tweets than in risk-related tweets. Our findings suggest that the influence of Twitter’s media capabilities on rapid tweet propagation during disasters may differ based on the communication processes.     

Pre- and post-launch emotions in new product development: Insights from twitter analytics of three products

The paper showcases the possible application of social media analytics in new product development (NPD). It compares users’ emotions before and after the launch of three new products in the market—a pizza, a car and a smart phone—for possible inputs for NPD. The user-generated content offers an alternative to conventional survey data and is cross-cultural in nature, relatively inexpensive and provides real-time information about user behaviour. A total of 302,632 tweets that mentioned the three new products before and after the launch were collected and analysed. Sentiment analysis of the tweets from two time periods was conducted and compared. The users’ responses to the pre- and post-launch of three products vary. The dissatisfaction with the new products represented by negative emotions aligns with the market performance. In the pre-launch period, trust and joy were more common for pizza, joy was more common for the car, and trust was more common for the phone. In the post-launch period, anger and disgust were more common for pizza, joy and trust were more common for the car, and joy was more common for only one aspect of the phone. Further analysis showed that for the car and the phone, firms need to focus on user attitudes towards product attributes, whereas for pizza, firms should concentrate on physiological changes, i.e., changes in product attributes, service and promotional sides. By using the proposed alternative approach, businesses can obtain real-time feedback about the expectations and experiences of the new products. The NPD process can be adjusted accordingly.     

基于技术系数矩阵的灾害影响评估及政策启示

近年来,自然灾害频发,促使人们更加关注灾害造成的社会经济损失。以往的研究多采用传统的技术经济方法评估灾害对某一产业或某一地区带来的直接损失,对评估灾害综合损失(包含间接损失)的研究较少。在列昂捷夫技术系数矩阵的基础上,借鉴了Haimes、Santos等人的方法,充分考虑产业经济系统各子系统之间存在的技术经济关联性,提出了灾害影响的综合评估模型。以我国2008年度的气象灾害为例,分别计算了静、动态情形下的灾害综合影响值,筛选了对灾害较为敏感的产业,提出了相应的政策建议,如根据灾害的关联影响构建新的灾后捐赠机制,灾后应加快受损方的设备、工艺和技术等的更新速度,借助技术进步减少灾害的综合损失等。     

基于分层模型的苹果种植农户气象灾害适应性行为研究

气象灾害适应性行为已经成为稳定苹果种植户收入,促进苹果产业可持续发展的重要举措。本文利用陕西、山东、甘肃和河南4个苹果主产省的45个村庄与931个农户调查数据,采用分层模型对苹果种植户气象灾害适应性行为进行研究。结果表明：96.67%的样本农户苹果生产受到气象灾害影响,其中85.92%的农户采用了适应性行为,但对包括覆膜、防冻剂等新型适应性措施的采用比例较低;村庄和农户层次的因素共同影响苹果种植户适应气象灾害的行为;村庄层次的苹果种植面积占比、基础设施供给情况,农户层次的户主风险类型、家庭规模、生产特征等因素正向影响农户采取适应性行为,而县年平均温度负向影响农户采取适应性行为;农户采取事前预防性行为和补救性行为的村庄与农户层次的影响因素存在较大差异。     

自然灾害的经济影响研究——以汶川大地震为例

我国传统的经济影响评估通常关注灾害所造成的直接经济损失和人员伤亡,而忽略其间接经济影响和时间维度上的变化,本文从理论和实证两个方面分析了汶川地震对四川省GDP所造成的影响。首先,论文根据索罗增长模型分析了地震发生后灾区的可能增长趋势;然后,通过柯布-道格拉斯生产函数及时间序列方法预测出若不发生地震时四川省的GDP,通过"有灾"时的实际值与"无灾"时的预测值进行对比。研究发现震后的恢复重建政策对四川经济发挥了积极有效作用,特别是长期来看拉动了四川省GDP的较快增长。     

技术灾害管理中的政策创新

不同于自然灾害,技术灾害往往多是人为的、只要多加投入就可部分或全部避免的,因而对技术灾害管理的关键点和着力点,就应该采取完善技术管理体系、鼓励技术社团组织发展、普及科技知识、进行技术监控体系建设等,并要积极遵守公众参与克服有限理性原则、程序公正原则、公民社会协同等政策创新原则,以期减少技术灾害损失和发生可能性、取得较好的技术灾害管理绩效。     

计算机自动程序设计研究

自动程序设计是计算机科学的中心目标之一,围绕中心目标进行研究是计算机科学工作者的责任。遗传程序设计作为演化计算的分支,具有概率搜索的本质和结构优化的特征,已成为研究计算机自动程序设计的重要工具。文中研究了自动程序设计的概念和遗传程序设计的方法,利用并行计算技术来实现自动程序设计。     

元数据自动生成技术研究

本文首先分析了元数据自动生成技术的可行性，接着对自动生成方法进行了阐述，包括元数据提取技术、元数据收割技术、元数据分面技术和其他技术；然后介绍了MGR、MWP和WWLib三个相关的国际研究项目，并比较研究了较有影响力的元数据自动生成工具Klarity和DC.dot，以及功能更为强大的CORC系统；最后对该技术作了总结和展望。     

我国重大气候灾害的形成机理和预测理论研究

1首席科学家 黄荣辉 中国科学院院士,中国科学院大气物理研究所研究员,博士生导师.1965年毕业于北京大学地球物理系,1968年获大气物理研究所硕士,1983年获东京大学理学博士.中国科学院地学部常委、全国政协委员、世界气候研究计划(WCRP)中国委员会副主任、中国科学院研究生院学位评定委员会副主任.1986年获国家"五一"劳动奖章和国家级有突出贡献中青年科学家称号.曾主持过多项国家重大研究项目.发表论文100余篇,著编多部著作.曾获国家自然科学奖三等奖3项,中国科学院科技进步奖一等奖2项、二等奖1项,中国科学院自然科学奖二等奖1项,1999年何梁何利基金科学与技术进步奖.     

我国地震灾害治理中的知识共享制度研究

 地震灾害的治理是一项科学性、技术性和社会性很强的系统工程,随着地震灾害治理步入人本化、科学化、法治化和制度化的轨道,在我国地震灾害治理的制度建设中初步确立了知识共享制度。会商制度有机地实现了多种监测成果和科技人员群体智慧的结合;资源共享制度有机地实现了地震灾害治理中物质技术资源与人力资源的有机结合;信息公开制度有机地实现了地震灾害治理信息的共享和满足公众知情权的结合;地震灾害治理知识的普及与传播制度有机地实现了地震灾害治理中知识共享与社会公众防震减灾素质提高的结合。     

文本自动聚类技术研究

自动聚类作为一种自动化程度较高的无监督机器学习技术,在信息检索和数据挖掘领域得到了广泛的应用.探讨了文本聚类的定义和步骤,依据文本自动聚类的步骤分别对文本的处理、自动聚类算法以及文本聚类结果的评价进行了阐述.     

基于Gabor小波特征确认多媒体消息数字水印版权标识

为解决基于数字水印的无线多媒体消息版权管理系统对提取后水印标识的自动识别问题，本文在充分考虑多媒体消息在传播中可能遭受攻击的基础上，提出一种基于Gabor小波特征的标识确认方案。该方案利用这类小波函数确定的滤波器适合局部分析和多方向多尺度分析的特点，提取与水印版权标识结构信息相关的统计量，形成特征集向量，通过特征集的距离比较，在小尺寸水印质量退化情况下，实现了对水印标识的识别。分析和实验表明，该方案能够满足无线多媒体消息版权管理的需求，并且在文中分析的情况下，设备的自动识别精度可以达到95%以上，较好地支持了对无线多媒体消息的版权管理。