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1.
Journalists, emergency responders, and the general public use Twitter during disasters as an effective means to disseminate emergency information. However, there is a growing concern about the credibility of disaster tweets. This concern negatively influences Twitter users’ decisions about whether to retweet information, which can delay the dissemination of accurate—and sometimes essential—communications during a crisis. Although verifying information credibility is often a time-consuming task requiring considerable cognitive effort, researchers have yet to explore how people manage this task while using Twitter during disaster situations.To address this, we adopt the Heuristic-Systematic Model of information processing to understand how Twitter users make retweet decisions by categorizing tweet content as systematically processed information and a Twitter user’s profile as heuristically processed information. We then empirically examine tweet content and Twitter user profiles, as well as how they interact to verify the credibility of tweets collected during two disaster events: the 2011 Queensland floods, and the 2013 Colorado floods. Our empirical results suggest that using a Twitter profile as source-credibility information makes it easier for Twitter users to assess the credibility of disaster tweets. Our study also reveals that the Twitter user profile is a reliable source of credibility information and enhances our understanding of timely communication on Twitter during disasters. 相似文献
2.
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. 相似文献
3.
Big data analytics and international negotiations: Sentiment analysis of Brexit negotiating outcomes
We introduce Big Data Analytics (BDA) and Sentiment Analysis (SA) to the study of international negotiations, through an application to the case of the UK-EU Brexit negotiations and the use of Twitter user sentiment. We show that SA of tweets has potential as a real-time barometer of public sentiment towards negotiating outcomes to inform government decision-making. Despite the increasing need for information on collective preferences regarding possible negotiating outcomes, negotiators have been slow to capitalise on BDA. Through SA on a corpus of 13,018,367 tweets on defined Brexit hashtags, we illustrate how SA can provide a platform for decision-makers engaged in international negotiations to grasp collective preferences. We show that BDA and SA can enhance decision-making and strategy in public policy and negotiation contexts of the magnitude of Brexit. Our findings indicate that the preferred or least preferred Brexit outcomes could have been inferred by the emotions expressed by Twitter users. We argue that BDA can be a mechanism to map the different options available to decision-makers and bring insights to and inform their decision-making. Our work, thereby, proposes SA as part of the international negotiation toolbox to remedy for the existing informational gap between decision makers and citizens’ preferred outcomes. 相似文献
4.
The increased availability of social media big data has created a unique challenge for marketing decision-makers; turning this data into useful information. One of the significant areas of opportunity in digital marketing is influencer marketing, but identifying these influencers from big data sets is a continual challenge. This research illustrates how one type of influencer, the market maven, can be identified using big data. Using a mixed-method combination of both self-report survey data and publicly accessible big data, we gathered 556,150 tweets from 370 active Twitter users. We then proposed and tested a range of social-media-based metrics to identify market mavens. Findings show that market mavens (when compared to non-mavens) have more followers, post more often, have less readable posts, use more uppercase letters, use less distinct words, and use hashtags more often. These metrics are openly available from public Twitter accounts and could integrate into a broad-scale decision support system for marketing and information systems managers. These findings have the potential to improve influencer identification effectiveness and efficiency, and thus improve influencer marketing. 相似文献
5.
《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
6.
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
7.
《Information processing & management》2022,59(3):102918
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. 相似文献
8.
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. 相似文献
9.
《Information processing & management》2020,57(1):102107
Social media platforms such as Twitter provide convenient ways to share and consume important information during disasters and emergencies. Information from bystanders and eyewitnesses can be useful for law enforcement agencies and humanitarian organizations to get firsthand and credible information about an ongoing situation to gain situational awareness among other potential uses. However, the identification of eyewitness reports on Twitter is a challenging task. This work investigates different types of sources on tweets related to eyewitnesses and classifies them into three types (i) direct eyewitnesses, (ii) indirect eyewitnesses, and (iii) vulnerable eyewitnesses. Moreover, we investigate various characteristics associated with each kind of eyewitness type. We observe that words related to perceptual senses (feeling, seeing, hearing) tend to be present in direct eyewitness messages, whereas emotions, thoughts, and prayers are more common in indirect witnesses. We use these characteristics and labeled data to train several machine learning classifiers. Our results performed on several real-world Twitter datasets reveal that textual features (bag-of-words) when combined with domain-expert features achieve better classification performance. Our approach contributes a successful example for combining crowdsourced and machine learning analysis, and increases our understanding and capability of identifying valuable eyewitness reports during disasters. 相似文献
10.
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
11.
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). 相似文献
12.
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
13.
14.
Five hundred million tweets are posted daily, making Twitter a major social media platform from which topical information on events can be extracted. These events are represented by three main dimensions: time, location and entity-related information. The focus of this paper is location, which is an essential dimension for geo-spatial applications, either when helping rescue operations during a disaster or when used for contextual recommendations. While the first type of application needs high recall, the second is more precision-oriented. This paper studies the recall/precision trade-off, combining different methods to extract locations. In the context of short posts, applying tools that have been developed for natural language is not sufficient given the nature of tweets which are generally too short to be linguistically correct. Also bearing in mind the high number of posts that need to be handled, we hypothesize that predicting whether a post contains a location or not could make the location extractors more focused and thus more effective. We introduce a model to predict whether a tweet contains a location or not and show that location prediction is a useful pre-processing step for location extraction. We define a number of new tweet features and we conduct an intensive evaluation. Our findings are that (1) combining existing location extraction tools is effective for precision-oriented or recall-oriented results, (2) enriching tweet representation is effective for predicting whether a tweet contains a location or not, (3) words appearing in a geography gazetteer and the occurrence of a preposition just before a proper noun are the two most important features for predicting the occurrence of a location in tweets, and (4) the accuracy of location extraction improves when it is possible to predict that there is a location in a tweet. 相似文献
15.
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
16.
《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
17.
《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
18.
Emna Mnif Khaireddine Mouakhar Anis Jarboui 《The Journal of High Technology Management Research》2021,32(2):100416
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. 相似文献
19.
Recent years have been characterized by the ubiquitous use of social networks as a mean of self and social identity, which offers new opportunities for qualitative and quantitative research in social sciences. The dynamics of interactions on social platforms such as Twitter promote the development of social movements around hashtags, such as #MeToo. According to previous research, this movement has set the beginning of an era. The present study aims to determine the key indicators of social identity in the #MeToo movement in Twitter using textual analysis and sentiment analysis of user-generated content. To this end, we use a cognitive pragmatics point of view to study a corpus of 31.305 tweets. Using the methodological approaches of corpus linguistics (CL) and discourse analysis (DA), we identify keywords, topics, frequency, and n-grams or collocations to understand the social identity of the #MeToo movement. The key indicators of the social identity in the #MeToo Era are validated using association statistical measures of Log-Likelihood and Mutual Information (MI). Our results reveal the polarization of sentiments where UGC is associated with both negative and positive topics. The social identity is particularly strongly correlated with women and the workplace. Finally, regardless the industry or area, these results present a holistic approach to the social identity of #MeToo. 相似文献
20.
《Information processing & management》2019,56(6):102045
The widespread popularity and worldwide application of social networks have raised interest in the analysis of content created on the networks. One such analytical application and aspect of social networks, including Twitter, is identifying the location of various political and social events, natural disasters and so on. The present study focuses on the localization of traffic accidents. Outdated and inaccurate information in user profiles, the absence of location data in tweet texts, and the limited number of geotagged posts are among the challenges tackled by location estimation. Adopting the Dempster–Shafer Evidence Theory, the present study estimates the location of accidents using a combination of user profiles, tweet texts, and the place attachments in tweets. The results indicate improved performance regarding error distance and average error distance compared to previously developed methods. The proposed method in this study resulted in a reduced error distance of 26%. 相似文献