Event Mentions Research Articles

Hypergraph convolutional networks with multi-ordering relations for cross-document event coreference resolution

Recognizing the coreference relationship between different event mentions in the text (i.e., event coreference resolution) is an important task in natural language processing. It helps to understand the association between various events in the text, and plays an important role in information extraction, question answering systems, and reading comprehension. Existing research has made progress in improving the performance of event coreference resolution, but there are also some shortcomings. For example, most of the existing methods analyze the event data in the document in a serial processing mode, without considering the complex relationship between events, and it is difficult to mine the deep semantics of events. To solve these problems, this paper proposes a cross-document event co-reference resolution method (HGCN-ECR) based on hypergraph convolutional neural networks. Firstly, the BiLSTM-CRF model was used to label the semantic role of the events extracted from a number of documents. According to the labeling results, the trigger words and non-trigger words of the event were determined, and the multi-document event hypergraph was constructed around the event trigger words. Then hypergraph convolutional neural networks are used to learn higher-order semantic information in multi-document event hypergraphs, and multi-head attention mechanisms are introduced to understand the hidden features of different event relationship types by treating each event relationship as a set of separate attention mechanisms. Finally, the feed-forward neural network and the average link clustering method are used to calculate the coreference score of events and complete the coreference event clustering, and the cross-document event coreference resolution is realized. The experimental results show that the cross-document event co-reference resolution method is superior to the baseline model.

Effective type label-based synergistic representation learning for biomedical event trigger detection

BackgroundDetecting event triggers in biomedical texts, which contain domain knowledge and context-dependent terms, is more challenging than in general-domain texts. Most state-of-the-art models rely mainly on external resources such as linguistic tools and knowledge bases to improve system performance. However, they lack effective mechanisms to obtain semantic clues from label specification and sentence context. Given its success in image classification, label representation learning is a promising approach to enhancing biomedical event trigger detection models by leveraging the rich semantics of pre-defined event type labels.ResultsIn this paper, we propose the Biomedical Label-based Synergistic representation Learning (BioLSL) model, which effectively utilizes event type labels by learning their correlation with trigger words and enriches the representation contextually. The BioLSL model consists of three modules. Firstly, the Domain-specific Joint Encoding module employs a transformer-based, domain-specific pre-trained architecture to jointly encode input sentences and pre-defined event type labels. Secondly, the Label-based Synergistic Representation Learning module learns the semantic relationships between input texts and event type labels, and generates a Label-Trigger Aware Representation (LTAR) and a Label-Context Aware Representation (LCAR) for enhanced semantic representations. Finally, the Trigger Classification module makes structured predictions, where each label is predicted with respect to its neighbours. We conduct experiments on three benchmark BioNLP datasets, namely MLEE, GE09, and GE11, to evaluate our proposed BioLSL model. Results show that BioLSL has achieved state-of-the-art performance, outperforming the baseline models.ConclusionsThe proposed BioLSL model demonstrates good performance for biomedical event trigger detection without using any external resources. This suggests that label representation learning and context-aware enhancement are promising directions for improving the task. The key enhancement is that BioLSL effectively learns to construct semantic linkages between the event mentions and type labels, which provide the latent information of label-trigger and label-context relationships in biomedical texts. Moreover, additional experiments on BioLSL show that it performs exceptionally well with limited training data under the data-scarce scenarios.

Assessing the financial impacts of significant wildfires on US capital markets: sectoral analysis

This study investigates the impact of significant wildfires from 2019 to 2022 on nine sectors within the US capital markets, utilizing a dataset encompassing 161 wildfires. Employing a combination of parametric and nonparametric tests, alongside regression analysis, the research scrutinizes how capital markets in distinct sectors respond to wildfire events, revealing nuanced effects. In sectors directly impacted, the insurance industry displays sensitivity to fire costs, with explicit country or event mentions correlating with sustained returns. Conversely, the real estate sector experiences diminished returns during prolonged wildfires, while the forestry and timber industry exhibits heightened sensitivity to fire costs, especially when ignited by lightning. Within indirect impact sectors, the health industry shows vulnerability to fire-related fatalities, with subsequent negative correlations with country mentions. In the food industry, fire costs contribute positively to returns, while duration and size yield negative effects. The transportation industry witnesses a gradual decline in returns, escalating with the number of fire days or associated costs. In resilience and mitigation sectors, utilities demonstrate recovery post-wildfires, contrasting with consistent declines in the energy sector. Among interconnected sectors, the travel and tourism industry sees increased returns tied to the number of victims, with events caused by human actions having a more pronounced impact. This research underscores the significance of tailored risk assessment and mitigation strategies, offering valuable insights for investors and policymakers navigating the intricate relationship between environmental events and financial markets.

Mastering Context-to-Label Representation Transformation for Event Causality Identification with Diffusion Models

To understand event structures of documents, event causality identification (ECI) emerges as a crucial task, aiming to discern causal relationships among event mentions. The latest approach for ECI has introduced advanced deep learning models where transformer-based encoding models, complemented by enriching components, are typically leveraged to learn effective event context representations for causality prediction. As such, an important step for ECI models is to transform the event context representations into causal label representations to perform logits score computation for training and inference purposes. Within this framework, event context representations might encapsulate numerous complicated and noisy structures due to the potential long context between the input events while causal label representations are intended to capture pure information about the causal relations to facilitate score estimation. Nonetheless, a notable drawback of existing ECI models stems from their reliance on simple feed-forward networks to handle the complex context-to-label representation transformation process, which might require drastic changes in the representations to hinder the learning process. To overcome this issue, our work introduces a novel method for ECI where, instead abrupt transformations, event context representations are gradually updated to achieve effective label representations. This process will be done incrementally to allow filtering of irrelevant structures at varying levels of granularity for causal relations. To realize this, we present a diffusion model to learn gradual representation transition processes between context and causal labels. It operates through a forward pass for causal label representation noising and a reverse pass for reconstructing label representations from random noise. Our experiments on different datasets across multiple languages demonstrate the advantages of the diffusion model with state-of-the-art performance for ECI.

Prompt for extraction: Multiple templates choice model for event extraction

Event Extraction (EE) is an essential task in natural language processing that aims to mine events occurring in event mentions represent events using event records, which usually consist of event types, trigger words, argument elements corresponding to roles in the event types. Recently, prompt-based generative models have been developed to extract events. However, these prompt-based generative studies have ignored the fact that the strong language comprehension capability of the pre-trained language model (PLM) can analyze extract the potential role relationships in multiple templates for more information that can help extract argument elements. To determine the extended templates that can help the model for event extraction, we propose the multiple template choice model (MTCM), which designs an extended event type mining module to automatically mine the extended event types in the event mention uses the templates corresponding to the extended event types to interact with the template, corresponding to the currently to-be-extracted event type of event mention, in a multi-template information interaction, which gives the model more information guides the PLM for event extraction. To validate our model, we used two widely used datasets in the event extraction domain, ACE2005-EN ERE-EN. The experimental results show that our model achieves state-of-the-art performance on the ACE2005-EN dataset significantly improves the ERE dataset. In addition, according to the results, our model can be effectively adapted to low-resource environments.

Open‐domain event schema induction via weighted attentive hypergraph neural network

SummaryEvent schema refers to the use of a template to depict similar events, and it is a necessary prerequisite for event causality extractions. The induction of event schemas is a difficult task, especially for texts in the open domain, due to the complex and diverse manifestations of events. Previous models considered participants in event mentions are independent or compositional, ignoring the high‐order correlations among participants, which limit their capability of induce event schema. To remedy this, we propose constructing an Event Structure Hypergraph (ESH) to better utilizes the event structural information for event schema induction. In particular, we first extract event mentions from the open‐domain corpus. and then construct an ESH by representing event mentions as a hyperedges. ESH contains high‐order information between participants in event mention. To, learn event mentions representation based on ESH, we propose a weighted attentive hypergraph neural network (WHGNN) to model event high‐order correlations and then integrate node‐category weight matrix into the training of network by improving event representation. By applying jointly cluster algorithm on the event mentions representation, we can induce reliable event schemas. Experimental results on three datasets demonstrate that our approach can induce salient and high‐quality event schemas on open‐domain corpus.

QURANIC INFLUENCES ON THE FABLES OF PREMCHAND AND KHADIJA MASTOOR

Premchand is a reliable fiction writer of Urdu. In his fictions, every subject of life is presented with all its atmosphere. There is a deep influence of Quranic influences, teachings and verses on his fictions. Quran is also presented in the fictions of Khadija Mastoor. Majeed's verses and teachings have a deep influence. His stories portray the modern era too. There are mentions of Quranic verses, teachings, hints, events and messengers of Allah in their stories. The fusion of Hindu Muslim culture in the fictions of Khadija Mastoor and Premchand also keeps the reader engrossed.

Event causality identification via structure optimization and reinforcement learning

Event causality identification (ECI) aims to identify possible causal relationships between event-mention pairs in a text. In the past, ECI models mainly used classification frameworks and rarely used generative models to solve this task. Although some progress has been made, the existing approaches suffer from the following two problems: (1) In the generative approach of inter-event mention dependency paths, noise and unnecessary sentence components cannot be effectively reduced, thus limiting the ability of the model to capture the critical correlation knowledge between event mentions; and (2) Existing multi-task generative model training which uses the REINFORCE algorithm suffers from a high-variance problem that imposes limitations on capturing critical causal knowledge. Therefore, we propose a novel Structural Optimization strategy Reinforcement Learning algorithm Generation model, GenSORL. The model aims to generate causal relationships from input sentences and includes dependency path generation as a complementary task to improve the causal label prediction performance. Specifically, this approach utilizes a new dependency syntax strategy to optimize dependency-path generation and extract important ECI contextual words between event mentions. Regarding the high-variance problem, a policy gradient with baseline is proposed for training the generative model, further adopting an innovative reward function to measure the accuracy of causal prediction and generation quality. In experiments using two frequently used benchmark datasets, the proposed method outperformed state-of-the-art models.

The Paradox of Information Control Under Authoritarianism: Explaining Trust in Competing Messages in China

To steer public opinion, autocracies prioritize state media reports of political news while marginalizing commercial and foreign media. Can this dominance guarantee people’s trust in state media news? We contend that rumors, circulated via informal channels and resistant to state information control, present a formidable challenge to public trust in state media news. Our two survey experiments in China pitted news of varying information quality (e.g. informative/detailed reports vs cursory mentions of events) from state media sources against rumors, showing that state media news can retain high levels of trustworthiness only if its information quality is high; however, low-quality state media news resulting from information control diminishes its trustworthiness and prompts people to believe rumors. Low-quality rumors have more negative effects than high-quality rumors on news trustworthiness and citizens’ satisfaction with government policies. Thus, information control can paradoxically erode trust in state media, which often represent the government in autocracies.

A contrastive learning framework for Event Detection via semantic type prototype representation modelling

The diversity of natural language expressions for describing events poses a challenge for the task of Event Detection (ED) with machine learning methods. To detect and classify event mentions, ED models essentially need to construct a semantic linkage between representations of the mentions and a set of target types. Unfortunately, most existing models use meaningless homogeneous one-hot vectors to represent the event type classes in ED, ignoring the fact that the event type labels also consist of meaningful words and can provide important clues for type representation learning. In this paper, we propose a Contrastive Semantic Prototype Representation Learning Framework for Event Detection (SemPRE), which exploits the pre-defined event type label words to inject the semantic information of the types and guide event detection. Specifically, we utilize pre-trained BERT to fuse text and event type into a joint representation space, and employ a contrastive-regularized module to enhance cross-type interaction. We conduct extensive experiments on the ACE 2005 and MAVEN benchmark datasets. The performance results show that our proposed SemPRE model achieves state-of-the-art performance on the datasets and outperforms existing baselines on limited annotated data and without using any external resources. Further analysis shows that our model is also effective in detecting multiple events and ambiguous trigger words.

Guizhou Ethnic Groups in the Qing Dynasty Miao Albums: Miao, Yizu, Gelao

This paper aims to evaluate textual descriptions of Guizhou ethnic groups found in the Qing dynasty Miao albums as a source for the study of history of large ethnic groups such as Miao, Yizu and Gelao. Two Miao albums about ethnic groups of Guizhou are housed at the library of Saint Petersburg State University, one of them has 72 descriptions with hand-drawn plates, only 28 out of 40 descriptions with plates remain in the other one. Among them are dozens of descriptions of Miao groups, several groups of Yizu, Buyi and Gelao, mainly with information on their costume and customs. Scarce information on history is found only in the descriptions of Yizu, since Yizu had been holding positions of local tusi chieftains and had established their own polities which the Chinese empire was forced to interact with. Rare descriptions of Miao have mentions of historical events. This study traces the historical sources, starting from the Han dynasty, where information was borrowed from, for the Miao albums. The paper restores these pieces of information and provides a commentary. Apparently, the value of Miao albums is presented by their visual images of Guizhou people together with accompanying ethnographic notes rather than the historical accounts.

Personal and situational determinants of perception of important life events

IntroductionThe present time is characterized by the instability of society, the authoritarian state, social insecurity, and the events of the global pandemic. One of the main properties of modernity is the continuous nature of the social changes taking place in it. Social uncertainty, instability and social insecurity complicate the usual strategy for constructing ideas about the world around us. In connection with certain factors, events in a person’s life can be defined as significant.ObjectivesThe purpose of this study was to ckeck the fact that important life events are something associated with stress and have a negative emotional connotation.MethodsThe methodological basis of the research was the qualitative study: semi-structured interviews, including projective techniques (case and content analysis) were carried out. N = 50 residents of Russia and Austria, age 16-65.ResultsThe hypothesis about important life events was completely confirmed. It turned out that important life events were described by the respondents mainly as negative, that is, in most cases (more than 70% of the total number of answers), important events were associated as something associated with stress, having a negative emotional connotation. It is interesting that mentions of joyful and happy events were much less frequent than negative ones, while there were practically no descriptions of the experience of vivid emotions accompanying any long-awaited events. At the same time, important events are quite extensively associated with stress and critical emotional experiences, often with mental problems: “shock”, “crisis”, “debts”, “severe depression”, “complete collapse”, “everything goes to hell”.ConclusionsImportant life events for a person are rather negatively assessed events, as well as events associated with stress, rather than positively assessed events.Disclosure of InterestNone Declared

Australasian Institute of Digital Health Summit 2022-Automated Social Media Surveillance for Detection of Vaccine Safety Signals: A Validation Study.

Social media platforms have emerged as a valuable data source for public health research and surveillance. Monitoring of social media and user-generated data on the Web enables timely and inexpensive collection of information, overcoming time lag and cost of traditional health reporting systems. This article identifies personally experienced coronavirus disease 2019 (COVID-19) vaccine reactions expressed on Twitter and validate the findings against an established vaccine reactions reporting system. We collected around 3 million tweets from 1.4 million users between February 1, 2021, to January 31, 2022, using COVID-19 vaccines and vaccine reactions keyword lists. We performed topic modeling on a sample of the data and applied a modified F1 scoring technique to identify a topic that best differentiated vaccine-related personal health mentions. We then manually annotated 4,000 of the records from this topic, which were used to train a transformer-based classifier to identify likely personally experienced vaccine reactions. Applying the trained classifier to the entire data set allowed us to select records we could use to quantify potential vaccine side effects. Adverse events following immunization (AEFI) referred to in these records were compared with those reported to the state of Victoria's spontaneous vaccine safety surveillance system, SAEFVIC (Surveillance of Adverse Events Following Vaccination In the Community). The most frequently mentioned potential vaccine reactions generally aligned with SAEFVIC data. Notable exceptions were increased Twitter reporting of bleeding-related AEFI and allergic reactions, and more frequent SAEFVIC reporting of cardiac AEFI. Social media conversations are a potentially valuable supplementary data source for detecting vaccine adverse event mentions. Monitoring of online observations about new vaccine-related personal health experiences has the capacity to provide early warnings about emerging vaccine safety issues.

KEPT: Knowledge Enhanced Prompt Tuning for event causality identification

Event causality identification (ECI) aims to identify causal relations of event mention pairs in text. Despite achieving certain accomplishments, existing methods are still not effective due to the following two issues: (1) the lack of causal reasoning ability, imposing restrictions on recognizing implicit causal relations; (2) the significant gap between fine-tuning and pre-training, which hinders the utilization of pre-trained language models (PLMs). In this paper, we propose a novel Knowledge Enhanced Prompt Tuning (KEPT) framework for ECI to address the issues mentioned above. Specifically, this method leverages prompt tuning to incorporate two kinds of knowledge obtained from external knowledge bases (KBs), including background information and relational information, for causal reasoning. To introduce external knowledge into our model, we first convert it to textual descriptions, then design an interactive attention mechanism and a selective attention mechanism to fuse background information and relational information, respectively. In addition, to further capture implicit relations between events, we adopt the objective from knowledge representation learning to jointly optimize the representations of causal relations and events. Experiment results on two widely-used benchmarks demonstrate that the proposed method outperforms the state-of-the-art models.

An Assessment of Mentions of Adverse Drug Events on Social Media With Natural Language Processing: Model Development and Analysis.

BackgroundAdverse reactions to drugs attract significant concern in both clinical practice and public health monitoring. Multiple measures have been put into place to increase postmarketing surveillance of the adverse effects of drugs and to improve drug safety. These measures include implementing spontaneous reporting systems and developing automated natural language processing systems based on data from electronic health records and social media to collect evidence of adverse drug events that can be further investigated as possible adverse reactions.ObjectiveWhile using social media for collecting evidence of adverse drug events has potential, it is not clear whether social media are a reliable source for this information. Our work aims to (1) develop natural language processing approaches to identify adverse drug events on social media and (2) assess the reliability of social media data to identify adverse drug events.MethodsWe propose a collocated long short-term memory network model with attentive pooling and aggregated, contextual representation generated by a pretrained model. We applied this model on large-scale Twitter data to identify adverse drug event–related tweets. We conducted a qualitative content analysis of these tweets to validate the reliability of social media data as a means to collect such information.ResultsThe model outperformed a variant without contextual representation during both the validation and evaluation phases. Through the content analysis of adverse drug event tweets, we observed that adverse drug event–related discussions had 7 themes. Mental health–related, sleep-related, and pain-related adverse drug event discussions were most frequent. We also contrast known adverse drug reactions to those mentioned in tweets.ConclusionsWe observed a distinct improvement in the model when it used contextual information. However, our results reveal weak generalizability of the current systems to unseen data. Additional research is needed to fully utilize social media data and improve the robustness and reliability of natural language processing systems. The content analysis, on the other hand, showed that Twitter covered a sufficiently wide range of adverse drug events, as well as known adverse reactions, for the drugs mentioned in tweets. Our work demonstrates that social media can be a reliable data source for collecting adverse drug event mentions.

Joint event causality extraction using dual-channel enhanced neural network

Event Causality Extraction (ECE) plays an essential role in many Natural Language Processing (NLP), such as event prediction and dialogue generation. Recent research in NLP treats ECE as a sequence labeling problem. However, these methods tend to extract the events and their relevant causality using a single collapsed model, which usually focuses on the textual contents while ignoring the intra-element transitions inside events and inter-event causality transition association across events. In general, ECE should condense the complex relationship of intra-event and the causality transition association among events. Therefore, we propose a novel dual-channel enhanced neural network to address this limitation by taking both global event mentions and causality transition association into account. To extract complete event mentions, a Textual Enhancement Channel(TEC) is constructed to learn important intra-event features from the training data with a wider perception field. Then the Knowledge Enhancement Channel(KEC) incorporates external causality transition knowledge using a Graph Convolutional Network (GCN) to provide complementary information on event causality. Finally, we design a dynamic fusion attention mechanism to measure the importance of the two channels. Thus, our proposed model can incorporate both semantic-level and knowledge-level representations of events to extract the relevant event causality. Experimental results on three public datasets show that our model outperforms the state-of-the-art methods.

Detecting ongoing events using contextual word and sentence embeddings

This paper introduces the Ongoing Event Detection (OED) task, which is a specific Event Detection task where the goal is to detect ongoing event mentions only, as opposed to historical, future, hypothetical, or other forms or events that are neither fresh nor current. Any application that needs to extract structured information about ongoing events from unstructured texts can take advantage of an OED system. The main contribution of this paper are the following: (1) it introduces the OED task along with a dataset manually labeled for the task; (2) it presents the design and implementation of an RNN model for the task that uses BERT embeddings to define contextual word and contextual sentence embeddings as attributes, which to the best of our knowledge were never used before for detecting ongoing events in news; (3) it presents an extensive empirical evaluation that includes (i) the exploration of different architectures and hyperparameters, (ii) an ablation test to study the impact of each attribute, and (iii) a comparison with a replication of a state-of-the-art model. The results offer several insights into the importance of contextual embeddings and indicate that the proposed approach is effective in the OED task, outperforming the baseline models.

Selecting Optimal Context Sentences for Event-Event Relation Extraction

Understanding events entails recognizing the structural and temporal orders between event mentions to build event structures/graphs for input documents. To achieve this goal, our work addresses the problems of subevent relation extraction (SRE) and temporal event relation extraction (TRE) that aim to predict subevent and temporal relations between two given event mentions/triggers in texts. Recent state-of-the-art methods for such problems have employed transformer-based language models (e.g., BERT) to induce effective contextual representations for input event mention pairs. However, a major limitation of existing transformer-based models for SRE and TRE is that they can only encode input texts of limited length (i.e., up to 512 sub-tokens in BERT), thus unable to effectively capture important context sentences that are farther away in the documents. In this work, we introduce a novel method to better model document-level context with important context sentences for event-event relation extraction. Our method seeks to identify the most important context sentences for a given entity mention pair in a document and pack them into shorter documents to be consume entirely by transformer-based language models for representation learning. The REINFORCE algorithm is employed to train models where novel reward functions are presented to capture model performance, and context-based and knowledge-based similarity between sentences for our problem. Extensive experiments demonstrate the effectiveness of the proposed method with state-of-the-art performance on benchmark datasets.

Vaccine Adverse Event Mining of Twitter Conversations: 2-Phase Classification Study.

BackgroundTraditional monitoring for adverse events following immunization (AEFI) relies on various established reporting systems, where there is inevitable lag between an AEFI occurring and its potential reporting and subsequent processing of reports. AEFI safety signal detection strives to detect AEFI as early as possible, ideally close to real time. Monitoring social media data holds promise as a resource for this.ObjectiveThe primary aim of this study is to investigate the utility of monitoring social media for gaining early insights into vaccine safety issues, by extracting vaccine adverse event mentions (VAEMs) from Twitter, using natural language processing techniques. The secondary aims are to document the natural language processing techniques used and identify the most effective of them for identifying tweets that contain VAEM, with a view to define an approach that might be applicable to other similar social media surveillance tasks.MethodsA VAEM-Mine method was developed that combines topic modeling with classification techniques to extract maximal VAEM posts from a vaccine-related Twitter stream, with high degree of confidence. The approach does not require a targeted search for specific vaccine reaction–indicative words, but instead, identifies VAEM posts according to their language structure.ResultsThe VAEM-Mine method isolated 8992 VAEMs from 811,010 vaccine-related Twitter posts and achieved an F1 score of 0.91 in the classification phase.ConclusionsSocial media can assist with the detection of vaccine safety signals as a valuable complementary source for monitoring mentions of vaccine adverse events. A social media–based VAEM data stream can be assessed for changes to detect possible emerging vaccine safety signals, helping to address the well-recognized limitations of passive reporting systems, including lack of timeliness and underreporting.

Adverse Mentions, Negative Sentiment, and Emotions in COVID-19 Vaccine Tweets and Their Association with Vaccination Uptake: Global Comparison of 192 Countries.

Background: Many countries show low COVID-19 vaccination rates despite high levels of readiness and delivery of vaccines. The public’s misperceptions, hesitancy, and negative emotions toward vaccines are psychological factors discouraging vaccination. At the individual level, studies have revealed negative perceptual/behavioral outcomes of COVID-19 information exposure via social media where misinformation and vaccine fear flood. Objective: This study extends research context to the global level and investigates social media discourse on the COVID-19 vaccine and its association with vaccination rates of 192 countries in the world. Methods: COVID-19 vaccine tweets were compared by country in terms of (1) the number per million Twitter users, (2) mentions of adverse events—death, side-effects, blood clots, (3) negative sentiment (vs. positive), and (4) fear, sadness, or anger emotions (vs. joy). Artificial intelligence (AI) was adopted to classify sentiment and emotions. Such tweets and covariates (COVID-19 morbidity and mortality rates, GDP, population size and density, literacy rate, democracy index, institutional quality, human development index) were tested as predictors of vaccination rates in countries. Results: Over 21.3 million COVID-19 vaccine tweets posted between November 2020 and August 2021 worldwide were included in our analysis. The global average of COVID-19 vaccine tweets mentioning adverse events was 2% for ‘death’, 1.15% for ‘side-effects’, and 0.80% for ‘blood clots’. Negative sentiment appeared 1.90 times more frequently than positive sentiment. Fear, anger, or sadness appeared 0.70 times less frequently than joy. The mention of ‘side-effects’ and fear/sadness/anger emotions appeared as significant predictors of vaccination rates, along with the human development index. Conclusions: Our findings indicate that global efforts to combat misinformation, address negative emotions, and promote positive languages surrounding COVID-19 vaccination on social media may help increase global vaccination uptakes.