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For the large number of news reports generated every day, in order to obtain effective information from these unstructured news text data more efficiently. In this paper, we study the real-time crawling of news data from news websites through crawling techniques and propose a BERT model-based approach to extract events from news long text. In this study, NetEase news website is selected as an example for real-time extraction to crawl the news data of this website. BERT model as a pre-trained model based on two-way encoded representation of transformer performs well on natural language understanding and natural language generation tasks. In this study, we will fine-tune the training based on BERT model on news corpus related dataset and perform sequence annotation through CRF layer to finally complete the event extraction task. In this paper, the DUEE dataset is chosen to train the model, and the experiments show that the overall performance of the BERT model is better than other network models. Finally, the model of this paper is further optimised, using the ALBERT and RoBERTa models improved on the basis of the BERT model, experiments were conducted, the results show that both models are improved compared to the BERT model, the ALBERT model algorithm performs the best, the model algorithm's F1 value is 1% higher than that of BERT. The results show that the performance is optimised.

We examine whether firms use social media to strategically disseminate financial information. Analyzing S&P 1500 firms' use of Twitter to disseminate quarterly earnings announcements, we find that firms are less likely to disseminate when the news is bad and when the magnitude of the bad news is worse, consistent with strategic behavior. Furthermore, firms tend to send fewer earnings announcement tweets and \"rehash\" tweets when the news is bad. Cross-sectional analyses suggest that incentives for strategic dissemination are higher for firms with a lower level of investor sophistication and firms with a larger social media audience. We also find that strategic dissemination behavior is detectable in high litigation risk firms, but not low litigation risk firms. Finally, we find that the tweeting of bad news and the subsequent retweeting of that news by a firm's followers are associated with more negative news articles written about the firm by the traditional media, highlighting a potential downside to Twitter dissemination.

In the current phase of the COVID-19 pandemic, we are witnessing the most massive vaccine rollout in human history. Like any other drug, vaccines may cause unexpected side effects, which need to be investigated in a timely manner to minimize harm in the population. If not properly dealt with, side effects may also impact public trust in the vaccination campaigns carried out by national governments. Monitoring social media for the early identification of side effects, and understanding the public opinion on the vaccines are of paramount importance to ensure a successful and harmless rollout. The objective of this study was to create a web portal to monitor the opinion of social media users on COVID-19 vaccines, which can offer a tool for journalists, scientists, and users alike to visualize how the general public is reacting to the vaccination campaign. We developed a tool to analyze the public opinion on COVID-19 vaccines from Twitter, exploiting, among other techniques, a state-of-the-art system for the identification of adverse drug events on social media; natural language processing models for sentiment analysis; statistical tools; and open-source databases to visualize the trending hashtags, news articles, and their factuality. All modules of the system are displayed through an open web portal. A set of 650,000 tweets was collected and analyzed in an ongoing process that was initiated in December 2020. The results of the analysis are made public on a web portal (updated daily), together with the processing tools and data. The data provide insights on public opinion about the vaccines and its change over time. For example, users show a high tendency to only share news from reliable sources when discussing COVID-19 vaccines (98% of the shared URLs). The general sentiment of Twitter users toward the vaccines is negative/neutral; however, the system is able to record fluctuations in the attitude toward specific vaccines in correspondence with specific events (eg, news about new outbreaks). The data also show how news coverage had a high impact on the set of discussed topics. To further investigate this point, we performed a more in-depth analysis of the data regarding the AstraZeneca vaccine. We observed how media coverage of blood clot-related side effects suddenly shifted the topic of public discussions regarding both the AstraZeneca and other vaccines. This became particularly evident when visualizing the most frequently discussed symptoms for the vaccines and comparing them month by month. We present a tool connected with a web portal to monitor and display some key aspects of the public's reaction to COVID-19 vaccines. The system also provides an overview of the opinions of the Twittersphere through graphic representations, offering a tool for the extraction of suspected adverse events from tweets with a deep learning model.

PurposeThe consumption of news from social media is the new trend, still news channels are the authentic source to transmit relevant news to audiences. Social media has gradually left an impact on the audience but the news channels have upgraded and providing various news services online on social media websites. The present study aims to study the type of news videos uploaded by the top five Hindi TV news channels on their YouTube channels with an aim to see which type of videos spark interest for YouTube viewers.Design/methodology/approachBy applying the techniques of content analysis, sentiment analysis and text mining the study aims to measure the average sentiments, top words and the trend of selected popular terms in the comments on uploaded news videos by the top five Hindi news channels over a period of one year.FindingsResults of the study indicate that the news channels are uploading more news videos about crime and investigation, politics, health and protests while uploading fewer news videos covering travel, science and technology, and religion. While the viewers of the participating news channels are more interested in giving their thoughts or opinions in the form of comments on news videos concerning crime, politics, protests and health or that these videos inspire conversation on YouTube.Research limitations/implicationsThe findings might be of interest to content managers of news channels to understand the interest of their audience.Originality/valueThe study's distinctiveness resides in the approach utilised to collect data and analyse the results in order to better understand the online behaviour of news channel audiences.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-01-2022-0007

Every day thousands of news are published on the web and filtering tools can be used to extract knowledge on specific topics. The categorization of news into a predefined set of topics is a subject widely studied in the literature, however, most works are restricted to documents in English. In this work, we make two contributions. First, we introduce a Portuguese news dataset collected from WikiNews an open-source media that provide news from different sources. Since there is a lack of datasets for Portuguese, and an existing one is from a single news channel, we aim to introduce a dataset from different news channels. The availability of comprehensive datasets plays a key role in advancing research. Second, we compare different architectures for Portuguese news classification, exploring different text representations (BoW, TF-IDF, Embedding) and classification techniques (SVM, CNN, DJINN, BERT) for documents in Portuguese, covering classical methods and current technologies. We show the trade-off between accuracy and training time for this application. We aim to show the capabilities of available algorithms and the challenges faced in the area.

The digital era has ushered in an unprecedented volume of readily accessible information, including news coverage of current events. Research has shown that the sentiment of news articles can evoke emotional responses from readers on a daily basis with specific evidence for increased anxiety and depression in response to coverage of the recent COVID-19 pandemic. Given the primacy and relevance of such information exposure, its daily impact on the mental health of the general population within this modality warrants further nuanced investigation. Using the COVID-19 pandemic as a subject-specific example, this work aimed to profile and examine associations between the dynamics of semantic affect in online local news headlines and same-day online mental health term search behavior over time across the United States. Using COVID-19-related news headlines from a database of online news stories in conjunction with mental health-related online search data from Google Trends, this paper first explored the statistical and qualitative affective properties of state-specific COVID-19 news coverage across the United States from January 23, 2020, to October 22, 2020. The resultant operationalizations and findings from the joint application of dictionary-based sentiment analysis and the circumplex theory of affect informed the construction of subsequent hypothesis-driven mixed effects models. Daily state-specific counts of mental health search queries were regressed on circumplex-derived features of semantic affect, time, and state (as a random effect) to model the associations between the dynamics of news affect and search behavior throughout the pandemic. Search terms were also grouped into depression symptoms, anxiety symptoms, and nonspecific depression and anxiety symptoms to model the broad impact of news coverage on mental health. Exploratory efforts revealed patterns in day-to-day news headline affect variation across the first 9 months of the pandemic. In addition, circumplex mapping of the most frequently used words in state-specific headlines uncovered time-agnostic similarities and differences across the United States, including the ubiquitous use of negatively valenced and strongly arousing language. Subsequent mixed effects modeling implicated increased consistency in affective tone (Spin β=-.207; P<.001) as predictive of increased depression-related search term activity, with emotional language patterns indicative of affective uncontrollability (Flux β=.221; P<.001) contributing generally to an increase in online mental health search term frequency. This study demonstrated promise in applying the circumplex model of affect to written content and provided a practical example for how circumplex theory can be integrated with sentiment analysis techniques to interrogate mental health-related associations. The findings from pandemic-specific news headlines highlighted arousal, flux, and spin as potentially significant affect-based foci for further study. Future efforts may also benefit from more expansive sentiment analysis approaches to more broadly test the practical application and theoretical capabilities of the circumplex model of affect on text-based data.

Does new media promote accountability in nondemocratic countries, where offline media is often suppressed? We show that blog posts, which exposed corruption in Russian state-controlled companies, had a negative causal impact on their market returns. For identification, we exploit the precise timing of blog posts by looking at within-day results with company-day fixed effects. Furthermore, we show that the posts are ultimately associated with higher management turnover and less minority shareholder conflicts. Taken together, our results suggest that social media can discipline corruption even in a country with limited political competition and heavily censored traditional media.

We propose the visual attention hypothesis that visuals in firm earnings announcements increase attention to the earnings news. We find that visuals in firms’ Twitter earnings announcements are associated with more retweets, consistent with greater user engagement with announcements that have visuals. This result holds for earnings tweets sent by the same firm and on the same day in firm-level and tweet-level analyses. Consistent with managerial opportunism, firms are more likely to use visuals in their earnings tweets when performance is good but less persistent. Consistent with visuals increasin g investor attention, the initial return response to earnings news is stronger and the post-announcement response is lower when visuals are used. Our evidence of a post-announcement return reversal indicates that visuals can be a double-edged sword. Furthermore, the higher earnings response coefficient from visuals is more pronounced on days with high investor distraction (when many other firms are also announcing earnings).

We examine how CEOs can facilitate the development of investor trust that helps mitigate the effects of negative information. Results from an experiment show that investors trust the CEO more and are more willing to invest in the firm when the CEO communicates firm news followed by a negative earnings surprise through a personal Twitter account than when the news and surprise comes from the CEO via a website or from the firm's Investor Relations Twitter account or website. A follow-up experiment shows that repeating the negative news does not incrementally affect investors who received the news from the CEO's Twitter account, but does further negatively impact investors who received the news via other disclosure mediums, especially those who received the news via the Investor Relations Twitter account. Our results have implications for firms and executives considering the costs and benefits of communicating with investors via Twitter.

Abstract Polarization, misinformation, declining trust, and wavering support for democratic norms are pressing threats to the US Exposure to verified and balanced news may make citizens more resilient to these threats. This project examines how to enhance users’ exposure to and engagement with verified and ideologically balanced news in an ecologically valid setting. We rely on a 2-week long field experiment on 28,457 Twitter users. We created 28 bots utilizing GPT-2 that replied to users tweeting about sports, entertainment, or lifestyle with a contextual reply containing a URL to the topic-relevant section of a verified and ideologically balanced news organization and an encouragement to follow its Twitter account. To test differential effects by gender of the bots, the treated users were randomly assigned to receive responses by bots presented as female or male. We examine whether our intervention enhances the following of news media organizations, sharing and liking of news content (determined by our extensive list of news media outlets), tweeting about politics, and liking of political content (determined using our fine-tuned RoBERTa NLP transformer-based model). Although the treated users followed more news accounts and the users in the female bot treatment liked more news content than the control, these results were small in magnitude and confined to the already politically interested users, as indicated by their pretreatment tweeting about politics. In addition, the effects on liking and posting political content were uniformly null. These findings have implications for social media and news organizations and offer directions for pro-social computational interventions on platforms.