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1.
《Information processing & management》2023,60(4):103325
Climate change has become one of the most significant crises of our time. Public opinion on climate change is influenced by social media platforms such as Twitter, often divided into believers and deniers. In this paper, we propose a framework to classify a tweet’s stance on climate change (denier/believer). Existing approaches to stance detection and classification of climate change tweets either have paid little attention to the characteristics of deniers’ tweets or often lack an appropriate architecture. However, the relevant literature reveals that the sentimental aspects and time perspective of climate change conversations on Twitter have a major impact on public attitudes and environmental orientation. Therefore, in our study, we focus on exploring the role of temporal orientation and sentiment analysis (auxiliary tasks) in detecting the attitude of tweets on climate change (main task). Our proposed framework STASY integrates word- and sentence-based feature encoders with the intra-task and shared-private attention frameworks to better encode the interactions between task-specific and shared features. We conducted our experiments on our novel curated climate change CLiCS dataset (2465 denier and 7235 believer tweets), two publicly available climate change datasets (ClimateICWSM-2022 and ClimateStance-2022), and two benchmark stance detection datasets (SemEval-2016 and COVID-19-Stance). Experiments show that our proposed approach improves stance detection performance (with an average improvement of 12.14% on our climate change dataset, 15.18% on ClimateICWSM-2022, 12.94% on ClimateStance-2022, 19.38% on SemEval-2016, and 35.01% on COVID-19-Stance in terms of average F1 scores) by benefiting from the auxiliary tasks compared to the baseline methods. 相似文献
2.
《Information processing & management》2023,60(1):103116
Research on automated social media rumour verification, the task of identifying the veracity of questionable information circulating on social media, has yielded neural models achieving high performance, with accuracy scores that often exceed 90%. However, none of these studies focus on the real-world generalisability of the proposed approaches, that is whether the models perform well on datasets other than those on which they were initially trained and tested. In this work we aim to fill this gap by assessing the generalisability of top performing neural rumour verification models covering a range of different architectures from the perspectives of both topic and temporal robustness. For a more complete evaluation of generalisability, we collect and release COVID-RV, a novel dataset of Twitter conversations revolving around COVID-19 rumours. Unlike other existing COVID-19 datasets, our COVID-RV contains conversations around rumours that follow the format of prominent rumour verification benchmarks, while being different from them in terms of topic and time scale, thus allowing better assessment of the temporal robustness of the models. We evaluate model performance on COVID-RV and three popular rumour verification datasets to understand limitations and advantages of different model architectures, training datasets and evaluation scenarios. We find a dramatic drop in performance when testing models on a different dataset from that used for training. Further, we evaluate the ability of models to generalise in a few-shot learning setup, as well as when word embeddings are updated with the vocabulary of a new, unseen rumour. Drawing upon our experiments we discuss challenges and make recommendations for future research directions in addressing this important problem. 相似文献
3.
《Information processing & management》2022,59(2):102820
As COVID-19 swept over the world, people discussed facts, expressed opinions, and shared sentiments about the pandemic on social media. Since policies such as travel restriction and lockdown in reaction to COVID-19 were made at different levels of the society (e.g., schools and employers) and the government, we build a large geo-tagged Twitter dataset titled UsaGeoCov19 and perform an exploratory analysis by geographic location. Specifically, we collect 650,563 unique geo-tagged tweets across the United States covering the date range from January 25 to May 10, 2020. Tweet locations enable us to conduct region-specific studies such as tweeting volumes and sentiment, sometimes in response to local regulations and reported COVID-19 cases. During this period, many people started working from home. The gap between workdays and weekends in hourly tweet volumes inspire us to propose algorithms to estimate work engagement during the COVID-19 crisis. This paper also summarizes themes and topics of tweets in our dataset using both social media exclusive tools (i.e., #hashtags, @mentions) and the latent Dirichlet allocation model. We welcome requests for data sharing and conversations for more insights.UsaGeoCov19 link: http://yunhefeng.me/geo-tagged_twitter_datasets/. 相似文献
4.
“三思而行”、“谋定后动”的文化氛围下，偏好悲观认知的国内创业者如何化解资源压力另辟创新蹊径？本文基于资源保存理论，探究防御性悲观经由因果逻辑、效果逻辑影响节俭式创新的作用机制，借鉴“奇正相生”的哲学思想，分析两种逻辑的双元性。通过415份创业者样本的实证分析发现：防御性悲观对节俭式创新有显著的直接影响；因果逻辑、效果逻辑在其间承担部分中介，且与节俭式创新均存在倒U型关系；两种逻辑呈此消彼长的替代关系，且使用趋于均衡时，更有利于节俭式创新。研究结果有助于客观认识悲观对创业的积极效用，并为创业者化解悲观情绪、应对资源困境和加速创新效能提供了理论启示和决策指南。 相似文献
5.
文章首先分析了2020年1—11月中国进出口的发展态势;其次基于中国经济增长、国际需求、中美经贸摩擦和国际疫情状况4个方面构建了3种预测情景;最后在3种情景下,基于计量经济模型、人工智能方法和系统分析方法,提出了分解集成预测模型体系,预测了中国2021年进出口增长趋势。在全球疫情得到一定的控制、世界经济缓慢复苏、中国经济稳定增长的基准情景下,预计2021年中国进出口总额约为4.9万亿美元,同比增长约5.7%;其中,出口总额约为2.7万亿美元,同比增长约6.2%,进口总额约为2.2万亿美元,同比增长约4.9%;贸易顺差约为5 766亿美元。在乐观情景下,2021年中国出口和进口增速较基准情景分别上升3.0和3.3个百分点;在悲观情景下,2021年中国出口和进口增速较基准情景分别下降2.9和3.2个百分点。 相似文献
6.
There has been an increased usage and popularity of digital platforms during the COVID-19 crisis. This has resulted in many new types of digital platforms emerging that are tied to specific localities and based on emergent needs. This article presents the results of a study on the ClickforVic digital platform that was started during the first 2020 lockdown in Melbourne, Australia as a way for country farmers to connect with urban consumers. The study is premised on transformational entrepreneurship theory that enables a focus on the societal changes that have resulted from the COVID-19 pandemic. A semi-structured in-depth interview approach was utilised to understand how farm entrepreneurs perceived the digital platform and how this contributed to transformational entrepreneurship outcomes. The study is amongst the first to incorporate a digital platform, farm entrepreneurship, transformational entrepreneurship and COVID-19 perspective. The findings suggest that farm entrepreneurs are driven by financial, social and community goals during a crisis that influences their usage of digital platforms. As a consequence, the findings contribute to managerial practice and policy debate by highlighting the way digital platforms can be used in times of crisis to produce transformational entrepreneurship outcomes. 相似文献
7.
《Information processing & management》2022,59(6):103095
Modeling discussions on social networks is a challenging task, especially if we consider sensitive topics, such as politics or healthcare. However, the knowledge hidden in these debates helps to investigate trends and opinions and to identify the cohesion of users when they deal with a specific topic. To this end, we propose a general multilayer network approach to investigate discussions on a social network. In order to prove the validity of our model, we apply it on a Twitter dataset containing tweets concerning opinions on COVID-19 vaccines. We extract a set of relevant hashtags (i.e., gold-standard hashtags) for each line of thought (i.e., pro-vaxxer, neutral, and anti-vaxxer). Then, thanks to our multilayer network model, we figure out that the anti-vaxxers tend to have ego networks denser (+14.39%) and more cohesive (+64.2%) than the ones of pro-vaxxer, which leads to a higher number of interactions among anti-vaxxers than pro-vaxxers (+393.89%). Finally, we report a comparison between our approach and one based on single networks analysis. We prove the effectiveness of our model to extract influencers having ego networks with more nodes (+40.46%), edges (+39.36%), and interactions with their neighbors (+28.56%) with respect to the other approach. As a result, these influential users are much more important to analyze and can provide more valuable information. 相似文献
8.
《Information processing & management》2020,57(1):102126
We analyze a data-set including more than 4.5 million tweets related to four highly emotional riot events. In particular, we examine statistically significant structural patterns that emerge as humans directly engage in an exchange of emotional messages with other humans on Twitter. Furthermore, we compare typical human-to-human communication patterns with those that emerge as bots engage in an emotional message-exchange with human users. To this end, we apply the novel concept of emotion-exchange motifs. We found that a) human-to-human conversations results in a variety of motifs that contain reciprocal edges and self-loops, b) bots predominantly contribute to the emergence of message broadcasting, single-way message sending behavior, c) in contrast to previous findings we found that in certain events bots frequently engage in direct message exchanges with humans, d) during riot events bots tend to direct fear-conveying messages to human users. 相似文献
9.
Coronavirus related discussions have spiraled at an exponential rate since its initial outbreak. By the end of May, more than 6 million people were diagnosed with this infection. Twitter witnessed an outpouring of anxious tweets through messages associated with the spread of the virus. Government and health officials replied to the troubling tweets, reassuring the public with regular alerts on the virus's progress and information to defend against the virus. We observe that social media users are worried about Covid 19-related crisis and we identify three separate conversations on virus contagion, prevention, and the economy. We analyze the tone of officials’ tweet text as alarming and reassuring and capture the response of Twitter users to official communications. Such studies can provide insights to health officials and government agencies for crisis management, specifically regarding communicating emergency information to the public via social media for establishing reassurance. 相似文献
10.
本文使用2007-2017年沪深A股上市公司的数据,研究了分析师乐观偏差对企业研发投入的影响,并进一步考察分析师面临的“利益冲突”和企业的信息透明度在其中发挥的作用。研究发现：(1)分析师乐观偏差与企业研发投入之间显著负相关,表明分析师的乐观偏差会抑制企业的研发投入;(2)机构投资者持股比例越高,企业存在再融资行为,分析师乐观偏差与企业研发投入之间的负向关系就越为显著,说明“利益冲突”会加剧两者的关系;(3)企业信息透明度的提高能够缓解分析师乐观偏差对企业研发投入的负面影响。本文的研究对于全面认识分析师在企业创新活动中的影响,以及如何充分发挥分析师作为资本市场信息中介角色的作用具有重要的理论和现实意义。 相似文献
11.
[目的/意义]微博作为一种重要的信息传播载体,在疫情信息发布与传播中发挥着重要作用。深入分析疫情信息中蕴含的疫情事件及其对网民情绪的影响,有助于各级政府准确掌握网络舆论情况,科学高效地做好防控宣传和舆情引导工作。[方法/过程]以新冠肺炎疫情相关的微博新闻及其评论作为研究对象,利用条件随机场(Conditional Random Field,CRF)模型从微博新闻中抽取疫情事件并建立疫情事件画像;在情感词典的基础上,引入双向长短期记忆网络(Bidirectional Long Short-Term Memory,Bi-LSTM)模型建立网民情绪画像;利用基于自注意力机制的Bi-LSTM模型对疫情事件与网民情绪进行关联分析。[结果/结论]真实语料集上的实验结果表明,围绕捐资、防控、临床和英雄等主题,CRF模型疫情事件抽取的F值均达到73%以上,Bi-LSTM模型网民情绪识别的F值均在70%以上,基于注意力机制的Bi-LSTM模型给出的网民情绪分布基本符合疫情发展态势。 相似文献
12.
《Research Policy》2023,52(1):104623
In this paper we develop a novel multi-stage integrative framework for technology foresight-planning. This framework integrates econometric analysis and a technology foresight procedure to predict: (i) the most COVID-19 resilient industries at the national level; and (ii) the most adversely affected industries (due to prior investment in innovation) that requires public support. Our econometric results show that the pandemic has induced a ‘double-edge sword’ effect of innovation on firm’s COVID-19 adaptable capacity (CAC). Costly innovation undertaken before the pandemic can be bad for firms if it compounds the problem of declining post-pandemic outbreak profit and optimism. Contrarily, firm level innovation improves firm’s CAC as the prior innovators’ profitability are found to have above-even odds of rebounding quickly post-pandemic outbreak. Our empirical analysis focuses mostly on developing economies, where firms are most likely implementing only incremental (non-frontier) innovation and thus our results should be generalized with caution. 相似文献
13.
《Information processing & management》2022,59(6):103073
With the onset of COVID-19, the pandemic has aroused huge discussions on social media like Twitter, followed by many social media analyses concerning it. Despite such an abundance of studies, however, little work has been done on reactions from the public and officials on social networks and their associations, especially during the early outbreak stage. In this paper, a total of 9,259,861 COVID-19-related English tweets published from 31 December 2019 to 11 March 2020 are accumulated for exploring the participatory dynamics of public attention and news coverage during the early stage of the pandemic. An easy numeric data augmentation (ENDA) technique is proposed for generating new samples while preserving label validity. It attains superior performance on text classification tasks with deep models (BERT) than an easier data augmentation method. To demonstrate the efficacy of ENDA further, experiments and ablation studies have also been implemented on other benchmark datasets. The classification results of COVID-19 tweets show tweets peaks trigged by momentous events and a strong positive correlation between the daily number of personal narratives and news reports. We argue that there were three periods divided by the turning points on January 20 and February 23 and the low level of news coverage suggests the missed windows for government response in early January and February. Our study not only contributes to a deeper understanding of the dynamic patterns and relationships of public attention and news coverage on social media during the pandemic but also sheds light on early emergency management and government response on social media during global health crises. 相似文献
14.
《Information processing & management》2023,60(3):103276
The COVID-19 pandemic has spurred a large amount of experimental and observational studies reporting clear correlation between the risk of developing severe COVID-19 (or dying from it) and whether the individual is male or female. This paper is an attempt to explain the supposed male vulnerability to COVID-19 using a causal approach. We proceed by identifying a set of confounding and mediating factors, based on the review of epidemiological literature and analysis of sex-dis-aggregated data. Those factors are then taken into consideration to produce explainable and fair prediction and decision models from observational data. The paper outlines how non-causal models can motivate discriminatory policies such as biased allocation of the limited resources in intensive care units (ICUs). The objective is to anticipate and avoid disparate impact and discrimination, by considering causal knowledge and causal-based techniques to compliment the collection and analysis of observational big-data. The hope is to contribute to more careful use of health related information access systems for developing fair and robust predictive models. 相似文献
15.
This study employs digital methods in conjunction with traditional content and discourse analyses to explore how the US President Donald Trump conducts diplomacy on Twitter and how, if at all, diplomatic entities around the world engage in diplomatic exchanges with him. The results confirm speculations that Trump’s diplomatic communication on Twitter disrupts traditional codes of diplomatic language but show little evidence that new codes of diplomatic interactions on social media are being constructed, given that other diplomatic entities around the world mostly remain within the confines of traditional notions of diplomacy in (not) communicating with Trump on Twitter. 相似文献
16.
《Information processing & management》2023,60(3):103299
Understanding the effects of gender-specific emotional responses on information sharing behaviors are of great importance for swift, clear, and accurate public health crisis communication, but remains underexplored. This study fills this gap by investigating gender-specific anxiety- and anger-related emotional responses and their effects on the virality of crisis information by creatively drawing on social role theory, integrated crisis communication modeling, and text mining. The theoretical model is tested using two datasets (Changsheng vaccine crisis with 2,423,074 textual data and COVID-19 pandemic with 893,930 textual data) collected from Weibo, a leading social media platform in China. Females express significantly high anxiety and anger levels (p value<0.001) during the Changsheng fake vaccine crisis, while express significantly higher levels of anxiety during COVID-19 than males (p value<0.001), but not anger (p value=0.13). Regression analysis suggests that the virality of crisis information is significantly strengthened when the level of anger in posts of males is high or the level of anxiety in posts of females is high for both crises. However, such gender-specific virality differences of anger/anxiety expressions are violated once females have large numbers of followers (influencers). Furthermore, the gender-specific emotional effects on crisis information are more significantly enhanced for male influencers than female influencers. This study contributes to the literature on gender-specific emotional characteristics of crisis communication on social media and provides implications for practice. 相似文献
17.
世界锂资源供需形势展望 总被引：4，自引：1，他引：4
锂主要用于铝冶炼、空气处理、润滑剂、锂电池以及陶瓷和玻璃这五大消费部门中。近年来新能源汽车快速发展对锂电池的需求引发了人们对锂资源的高度关注。本文运用部门预测法,对2015-2025年世界各国对锂的需求进行综合分析,分析认为：①石墨烯与燃料电池技术发展缓慢的用锂高值情景下,锂需求会逐渐上升。2015年、2020年与2025年的全球锂需求为3.2、5.1、9.6万t;石墨烯与燃料电池技术发展快速的用锂低值情景下,锂需求达到顶峰后下降。2015、2020与2025年全球锂需求分别为3.2、4.9、4.2万t;②用锂高值情景下,全球锂资源消费结构会发生明显变化,锂电池成为第一大消费部门,2025年全球锂消费结构为电池66%,陶瓷和玻璃20%,润滑脂4%,空气处理2%,电解铝0.08%;用锂低值情景下,2025年陶瓷和玻璃以41%的消费占比继续保持第一大消费部门,其余部门的消费结构为电池25%,润滑脂10%,空气处理6%,铝冶炼0.19%;③全球锂资源丰富,储产比高达371年,2015-2020年全球锂产能将至少达到9.2万t,2015-2020年仍将持续供过于求的状态。用锂高值情景下,锂资源在2020年以后有供应趋紧的可能;用锂低值情景下,锂市场将持续供过于求的趋势。 相似文献
18.
Health misinformation has become an unfortunate truism of social media platforms, where lies could spread faster than truth. Despite considerable work devoted to suppressing fake news, health misinformation, including low-quality health news, persists and even increases in recent years. One promising approach to fighting bad information is studying the temporal and sentiment effects of health news stories and how they are discussed and disseminated on social media platforms like Twitter. As part of the effort of searching for innovative ways to fight health misinformation, this study analyzes a dataset of more than 1600 objectively and independently reviewed health news stories published over a 10-year span and nearly 50,000 Twitter posts responding to them. Specifically, it examines the source credibility of health news circulated on Twitter and the temporal, sentiment features of the tweets containing or responding to the health news reports. The results show that health news stories that are rated low by experts are discussed more, persist longer, and produce stronger sentiments than highly rated ones in the tweetosphere. However, the highly rated stories retained a fresh interest in the form of new tweets for a longer period. An in-depth understanding of the characteristics of health news distribution and discussion is the first step toward mitigating the surge of health misinformation. The findings provide insights into understanding the mechanism of health information dissemination on social media and practical implications to fight and mitigate health misinformation on digital media platforms. 相似文献
19.
Communication of post-release plans in crowdfunding development initiatives: A signaling perspective
Significant research explores how developers leverage crowdfunding to attract finance for releasing digital goods. However, researchers seldom study “post-release activities” that are crucial for maintaining and advancing those goods. This article elaborates on the challenging nature of post-release activities for crowdfunding initiatives, asking how developers communicate their post-release plans to effectively prepare backers for possible changes. Using a grounded approach that connects the longitudinal history of fundraising to development to post-release, I examine initiatives that achieved impressive fundraising and development results yet varied significantly in their post-release outcomes. While they consistently signaled post-release plans, the differences are the signals’ costs, backers' reactions, and the post-release activities and outcomes. I present theoretical propositions that (1) developers benefit in the long run by combining high-cost signaling with engaging backers in follow-up conversations about post-release issues and (2) prospective backers can utilize developers' communication to identify their post-release signals. Unlike dominant research findings about signals’ impacts on mobilizing resources during fundraising, the findings emphasize signals’ post-release consequences for stakeholders. While different signaling approaches can enhance short-term performance, they also seed contrasting longer-term outcomes for developers, backers, and the industry. These findings advance knowledge on effective strategies for engaging society to build sustainable digital goods. 相似文献
20.
During the course of the Egyptian civil movement in 2011, excessive suppression of the protesters caused a great deal of humanitarian concerns across the world. Egyptian protesters were supported not only in the Arabic-speaking world, but also throughout the English speaking world. The Twittersphere1 became a valuable arena for individuals to communicate amongst each other regarding important social movement issues. This paper is a study of the communication on Twitterverse consisting of both English and Arabic tweets and the sentiments expressed therein during the Egyptian protest movement. We focus on the research questions: what sentiments of Tweeters relate to signals of protest communication?, and how do protest related tweets in two languages in the Twitter sphere, that are a proxy of two different and important cultural groups, compare with each other? In order to understand the protest communications in Twittersphere, we examine a dual pathways model that relates to emotional and goal related sentiments. We apply this model to examine the online protest in Egypt. Our findings reveal the emotions and goal related sentiments that are fundamental for intention to protest across the two languages. We find that anger, fear, pride and hope were the prime sentiments regarding intention to or support of protest, regardless of language. 相似文献