A survey conducted in December 2020 assessing if news consumers in the United States had ever unknowingly shared fake news or information on social media found that 38.2 percent had done so. A similar share had not, whereas seven percent were unsure if they had accidentally disseminated misinformation on social networks.

Fake news in the U.S.

Fake news, or news that contains misinformation, has become a prevalent issue within the American media landscape. Fake news can be circulated online as news stories with deliberately misleading headings, or clickbait, but the rise of misinformation cannot be solely accredited to online social media. Forms of fake news are also found in print media, with 47 percent of Americans witnessing fake news in newspapers and magazines as of January 2019.

News consumers in the United States are aware of the spread of misinformation, with many Americans believing online news websites regularly report fake news stories. With such a high volume of online news websites publishing false information, it can be difficult to assess the credibility of a story. This can have damaging effects on society in that the public struggled to keep informed, creating a great deal of confusion about even basic facts and contributing to incivility.

The term ‘fake news’ is used in multiple different contexts, but officially refers simply to false information presented as legitimate news. Adults in the United States believe social media platforms and online news sites to be the most likely sources of fake news – 58 percent of respondents to a survey believed that Facebook was the most likely place in which they would encounter false news stories, and 49 percent said the same about Twitter.

A separate study revealed that 66 percent of U.S. adults believed that 76 percent or more of the news they saw on social media was biased. Social networks are generally not seen as credible or trustworthy news platforms – on a global level, social media was the least trusted source of general news and information.

Why does social media fuel or help to spread fake news?

Sadly, the main way in which fake news can be so quickly disseminated throughout not only one, but multiple social media platforms, is by users sharing such news with others (either knowingly or unknowingly). The ability to share content with friends and family is one of the key appeals of social networks, but the ease of doing so becomes somewhat sinister when it comes to the spread of false information.

Ten percent of U.S. adults admitted to knowingly sharing fake news or information online, 49 percent said that they shared such content and later found out it was inaccurate or made up, and 52 percent admitted to having done either of these things. This is a serious cause for concern. Sharing news in good faith and later discovering it was fake is one thing, but deliberately and knowingly passing such content on to others is another. Many social media users blame the networks themselves for the spread of fake news. Whilst social platforms do make attempts to regulate the content shared on their sites, the more users who actively take responsibility for the content they choose to share and hold themselves accountable, the greater the overall impact.

As of March of 2018, around 52 percent of Americans felt that online news websites regularly report fake news stories in the United States. Another 34 percent of respondents stated that they believed that online news websites occasionally report fake news stories. Just nine percent of adults said that they did not believe that fake news stories were being reported online.

Fake news

Coined by Donald Trump, the term ‘fake news’ is used to describe news stories or even entire networks believed to be spreading false information. Increasingly used by members of government and citizens on both sides of the political spectrum, the term is now a staple in debates regarding freedom of the press, corruption, and media bias. People of all ages now believe that over 60 percent of the news that they see on social media is fake and express similar concern over the accuracy of traditional news sources. While a cynical perspective regarding news and reporting may be positive in terms of holding guilty outlets accountable and ensuring responsible reporting, the fake news phenomenon has extended much farther than pure skepticism. As of 2018, around 35 percent of Republicans and 18 percent of Independents perceived the media to be an enemy of the American people.

Perhaps unsurprisingly, the main traffic source for false information online is social media, which generates 42 percent of fake news traffic. The nature of social networks, most notably the ease of sharing content, allows fake news to spread at a rapid rate – an issue further exacerbated by the fact that many U.S. adults sometimes believe fake news to be real.

Fake news: an ongoing problem

The presence of fake news would be less of an issue if users were more aware of how to identify it and were aware of the risks of sharing such content. Many U.S. news consumers have shared fake news online, and worryingly, ten percent did so deliberately. Adults who are part of that ten percent are just a small portion of people in the United States, and elsewhere in the world, who are responsible for spreading false information. More than 30 percent of U.S. children and teenagers have shared a fake news story online, and over 50 percent of adults in selected countries worldwide have wrongly believed a fake news story.

The result of adults and young consumers alike not only believing fake news, but actively sharing it, is that small, illegitimate websites producing such content are able to grow more successful. Such websites have the potential to tarnish or seriously damage the reputation of any persons mentioned within a fake news article, promote events or policies which do not exist, and mislead readers about important topics they are trying to keep up with. A 2019 survey revealed that most adults believe that fake news and misinformation will get worse in the next five years, and the sad truth is that this will likely be the case unless news consumers grow more discerning about what they post and share online.

A study held in early 2023 found that Indonesian adults were the most concerned about the spread of false information on social media, with over 80 percent saying that they were very or somewhat worried about the matter. Whilst Swedish and Danish respondents were less concerned about misinformation on social media, the global average among all countries was 68 percent, highlighting the growing awareness and worry about false information worldwide.

Simulation data used with the research article "Digital cloning of online social networks for language-sensitive agent-based modeling of misinformation spread" currently under peer review

In a digital news consumption survey conducted in India in March 2023, 43 percent of respondents stated that observing how news spreads and its absence from other digital platforms was a common method they used to spot online misinformation. In comparison, 30 percent of the surveyed consumers selected poorly designed graphics or one-sided news as common indicators of online misinformation.

According to a survey conducted in the United Kingdom in May 2024, 75 percent of adults thought that digitally-altered content contributed to the spread of online misinformation. Additionally, 67 percent felt that AI-generated content contributed to the spread of misnformation on online platforms.

The spread of false and misleading information on social media is largely dependent on human action. Understanding the factors that lead social media users to amplify (or indeed intervene in) the spread of this content is an ongoing challenge. Prior research suggests that users are not only more likely to interact with misinformation that supports their ideology or their political beliefs, they may also feel it is more acceptable to spread. However, less is known about the influence of newer, issue-specific beliefs. Two online studies explored the relationship between the degree of belief-consistency of disinformation on users’ moral judgements and intentions to spread disinformation further. Four disinformation narratives were presented: disinformation that supported or undermined the UK Government’s handling of COVID-19, and disinformation that minimised or maximised the perceived risk of COVID-19. A novel scale for measuring intentions to contribute to the spread of social media content was also used in study 2. Participants reported greater likelihood of spreading false material that was consistent with their beliefs. More lenient moral judgements related to the degree of belief-consistency with disinformation, even when participants were aware the material was false or misleading. These moral judgements partially mediated the relationship between belief-consistency of content and intentions to spread it further on social media. While people are concerned about the spread of disinformation generally, they may evaluate belief-consistent disinformation differently from others in a way that permits them to spread it further. As social media platforms prioritise the ordering of feeds based on personal relevance, there is a risk that users could be being presented with disinformation that they are more tolerant of.

Overview

This dataset of medical misinformation was collected and is published by Kempelen Institute of Intelligent Technologies (KInIT). It consists of approx. 317k news articles and blog posts on medical topics published between January 1, 1998 and February 1, 2022 from a total of 207 reliable and unreliable sources. The dataset contains full-texts of the articles, their original source URL and other extracted metadata. If a source has a credibility score available (e.g., from Media Bias/Fact Check), it is also included in the form of annotation. Besides the articles, the dataset contains around 3.5k fact-checks and extracted verified medical claims with their unified veracity ratings published by fact-checking organisations such as Snopes or FullFact. Lastly and most importantly, the dataset contains 573 manually and more than 51k automatically labelled mappings between previously verified claims and the articles; mappings consist of two values: claim presence (i.e., whether a claim is contained in the given article) and article stance (i.e., whether the given article supports or rejects the claim or provides both sides of the argument).

The dataset is primarily intended to be used as a training and evaluation set for machine learning methods for claim presence detection and article stance classification, but it enables a range of other misinformation related tasks, such as misinformation characterisation or analyses of misinformation spreading.

Its novelty and our main contributions lie in (1) focus on medical news article and blog posts as opposed to social media posts or political discussions; (2) providing multiple modalities (beside full-texts of the articles, there are also images and videos), thus enabling research of multimodal approaches; (3) mapping of the articles to the fact-checked claims (with manual as well as predicted labels); (4) providing source credibility labels for 95% of all articles and other potential sources of weak labels that can be mined from the articles' content and metadata.

The dataset is associated with the research paper "Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims" accepted and presented at ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '22).

The accompanying Github repository provides a small static sample of the dataset and the dataset's descriptive analysis in a form of Jupyter notebooks.

Options to access the dataset

There are two ways how to get access to the dataset:

1. Static dump of the dataset available in the CSV format
2. Continuously updated dataset available via REST API

In order to obtain an access to the dataset (either to full static dump or REST API), please, request the access by following instructions provided below.

References

If you use this dataset in any publication, project, tool or in any other form, please, cite the following papers:

@inproceedings{SrbaMonantPlatform, author = {Srba, Ivan and Moro, Robert and Simko, Jakub and Sevcech, Jakub and Chuda, Daniela and Navrat, Pavol and Bielikova, Maria}, booktitle = {Proceedings of Workshop on Reducing Online Misinformation Exposure (ROME 2019)}, pages = {1--7}, title = {Monant: Universal and Extensible Platform for Monitoring, Detection and Mitigation of Antisocial Behavior}, year = {2019} }

@inproceedings{SrbaMonantMedicalDataset, author = {Srba, Ivan and Pecher, Branislav and Tomlein Matus and Moro, Robert and Stefancova, Elena and Simko, Jakub and Bielikova, Maria}, booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '22)}, numpages = {11}, title = {Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims}, year = {2022}, doi = {10.1145/3477495.3531726}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3477495.3531726}, }

Dataset creation process

In order to create this dataset (and to continuously obtain new data), we used our research platform Monant. The Monant platform provides so called data providers to extract news articles/blogs from news/blog sites as well as fact-checking articles from fact-checking sites. General parsers (from RSS feeds, Wordpress sites, Google Fact Check Tool, etc.) as well as custom crawler and parsers were implemented (e.g., for fact checking site Snopes.com). All data is stored in the unified format in a central data storage.

Ethical considerations

The dataset was collected and is published for research purposes only. We collected only publicly available content of news/blog articles. The dataset contains identities of authors of the articles if they were stated in the original source; we left this information, since the presence of an author's name can be a strong credibility indicator. However, we anonymised the identities of the authors of discussion posts included in the dataset.

The main identified ethical issue related to the presented dataset lies in the risk of mislabelling of an article as supporting a false fact-checked claim and, to a lesser extent, in mislabelling an article as not containing a false claim or not supporting it when it actually does. To minimise these risks, we developed a labelling methodology and require an agreement of at least two independent annotators to assign a claim presence or article stance label to an article. It is also worth noting that we do not label an article as a whole as false or true. Nevertheless, we provide partial article-claim pair veracities based on the combination of claim presence and article stance labels.

As to the veracity labels of the fact-checked claims and the credibility (reliability) labels of the articles' sources, we take these from the fact-checking sites and external listings such as Media Bias/Fact Check as they are and refer to their methodologies for more details on how they were established.

Lastly, the dataset also contains automatically predicted labels of claim presence and article stance using our baselines described in the next section. These methods have their limitations and work with certain accuracy as reported in this paper. This should be taken into account when interpreting them.

Reporting mistakes in the dataset The mean to report considerable mistakes in raw collected data or in manual annotations is by creating a new issue in the accompanying Github repository. Alternately, general enquiries or requests can be sent at info [at] kinit.sk.

Dataset structure

Raw data

At first, the dataset contains so called raw data (i.e., data extracted by the Web monitoring module of Monant platform and stored in exactly the same form as they appear at the original websites). Raw data consist of articles from news sites and blogs (e.g. naturalnews.com), discussions attached to such articles, fact-checking articles from fact-checking portals (e.g. snopes.com). In addition, the dataset contains feedback (number of likes, shares, comments) provided by user on social network Facebook which is regularly extracted for all news/blogs articles.

Raw data are contained in these CSV files (and corresponding REST API endpoints):

sources.csv

articles.csv

article_media.csv

article_authors.csv

discussion_posts.csv

discussion_post_authors.csv

fact_checking_articles.csv

fact_checking_article_media.csv

claims.csv

feedback_facebook.csv

Note: Personal information about discussion posts' authors (name, website, gravatar) are anonymised.

Annotations

Secondly, the dataset contains so called annotations. Entity annotations describe the individual raw data entities (e.g., article, source). Relation annotations describe relation between two of such entities.

Each annotation is described by the following attributes:

category of annotation (annotation_category). Possible values: label (annotation corresponds to ground truth, determined by human experts) and prediction (annotation was created by means of AI method).

type of annotation (annotation_type_id). Example values: Source reliability (binary), Claim presence. The list of possible values can be obtained from enumeration in annotation_types.csv.

method which created annotation (method_id). Example values: Expert-based source reliability evaluation, Fact-checking article to claim transformation method. The list of possible values can be obtained from enumeration methods.csv.

its value (value). The value is stored in JSON format and its structure differs according to particular annotation type.

At the same time, annotations are associated with a particular object identified by:

entity type (parameter entity_type in case of entity annotations, or source_entity_type and target_entity_type in case of relation annotations). Possible values: sources, articles, fact-checking-articles.

entity id (parameter entity_id in case of entity annotations, or source_entity_id and target_entity_id in case of relation annotations).

The dataset provides specifically these entity annotations:

Source reliability (binary). Determines validity of source (website) at a binary scale with two options: reliable source and unreliable source.

Article veracity. Aggregated information about veracity from article-claim pairs.

The dataset provides specifically these relation annotations:

Fact-checking article to claim mapping. Determines mapping between fact-checking article and claim.

Claim presence. Determines presence of claim in article.

Claim stance. Determines stance of an article to a claim.

Annotations are contained in these CSV files (and corresponding REST API endpoints):

entity_annotations.csv

relation_annotations.csv

Note: Identification of human annotators authors (email provided in the annotation app) is anonymised.

Biological invasions are a major human induced global change that is threatening global biodiversity by homogenizing the world's fauna and flora. Species spread because humans have moved species across geographic boundaries and have changed ecological factors that structure ecosystems, such as nitrogen deposition, disturbance, etc. Many biological invasions are caused accidentally, as a byproduct of human travel and commerce driven product shipping. However, humans also have spread many species intentionally because of perceived benefits. Of interest is the role of the recent exponential growth in information exchange via internet social media in driving biological invasions. To date, this has not been examined. Here we show that for one such invasive species, goldenrod, social networks spread misleading and incomplete information that is enhancing the spread of goldenrod invasions into new environments. We show that the notion of goldenrod honey as a "superfood" with unsupported healing properties is driving a demand that leads beekeepers to produce goldenrod honey. Social networks provide a forum for such information exchange and this is leading to further spread of goldenrod in many countries where goldenrod is not native, such as Poland. However, this informal social information exchange ignores laws that focus on preventing the further spread of invasive species and the strong negative effects that goldenrod has on native ecosystems, including floral resources that negatively impact honeybee performance. Thus, scientifically unsupported information on "superfoods" such as goldenrod honey that is disseminated through social internet networks has real world consequences such as increased goldenrod invasions into novel geographical regions which decreases native biodiversity.

Digital literacy is receiving increased scholarly attention as a potential explanatory factor in the spread of misinformation and other online pathologies. As a concept, however, it remains surprisingly elusive, with little consensus on definitions or measures. We provide a digital literacy framework for political scientists and test survey items to measure it with an application to online information retrieval tasks. There exists substantial variation in levels of digital literacy in the population, which we show is correlated with age and could confound observed relationships. However, this is obscured by researchers' reliance on online convenience samples that select for people with computer and internet skills. We discuss the implications of these measurement and sample selection considerations for effect heterogeneity in studies of online political behavior. We argue that there is no universally applicable formula for selecting a given non-probability sample or operationalization of the concept of digital literacy; instead, we conclude, researchers should make theoretically informed arguments about how they select both sample and measure.

According to a global study conducted in 2019, 62 percent of respondents felt that there was a fair extent or great deal of fake news on online websites and platforms. By comparison, 10 percent less said the same about TV, radio, newspapers, and magazines. Traditional media in general is still considered more trustworthy than online formats, despite social networks being the preferred choice for many.

Meanwhile, as some consumers around the world now turn to influencers for news instead of journalists, the risk of them being exposed to inaccurate, incorrect, or deliberately false information continues to grow, and journalists face pressure to battle fake content whilst finding new ways to keep audiences engaged.

Fake news and journalism

More than 50 percent of journalists responding to a global survey believed that the public had lost trust in the media over the past year. Whilst the reasons for this are many, the role of fake news cannot be undermined, particularly given the speed with which false content can spread and reach vulnerable or misinformed audiences. Either unintentionally or deliberately, fake news is often shared by those who encounter it, which only serves to worsen the problem. Indeed, journalists consider regular citizens to be the main source of disinformation, followed by political leaders and internet trolls.

Despite the threats fake news poses, journalists themselves feel that concerns about disinformation could positively impact the quality of journalism. There are also growing expectations from the public and journalists alike for governments and companies to do more to help boost quality journalism and curb the dissemination and influence of fake news. News industry leaders rated Google as being the best platform for supporting journalism, but the likes of Amazon and Snapchat have a long way to go before organizations consider them reliable in this respect.

Data and code needed to replicate original empirical analyses for the following article: Freelon, D., Marwick, A., & Kreiss, D. (forthcoming). False Equivalencies: Online activism from left to right. Science. Abstract: Digital media are critical for contemporary activism—even low-effort “clicktivism” is politically consequential and contributes to offline participation. We argue that in the US and industrialized West, left- and right-wing activists use digital and legacy media differently to achieve political goals. While left-wing actors operate primarily through “hashtag activism” and offline protest, right-wing activists manipulate legacy media, migrate to alternative platforms, and work strategically with partisan media to spread their messages. Although scholarship suggests the right has embraced strategic disinformation and conspiracy theories more than the left, more research is needed to reveal the magnitude and character of left-wing disinformation. Such ideological asymmetries between left and right-wing activism hold critical implications for democratic practice, social media governance, and the interdisciplinary study of digital politics.

The Reddit Subreddit Dataset by Dataplex offers a comprehensive and detailed view of Reddit’s vast ecosystem, now enhanced with appended AI-generated columns that provide additional insights and categorization. This dataset includes data from over 2.1 million subreddits, making it an invaluable resource for a wide range of analytical applications, from social media analysis to market research.

Dataset Overview:

This dataset includes detailed information on subreddit activities, user interactions, post frequency, comment data, and more. The inclusion of AI-generated columns adds an extra layer of analysis, offering sentiment analysis, topic categorization, and predictive insights that help users better understand the dynamics of each subreddit.

2.1 Million Subreddits with Enhanced AI Insights: The dataset covers over 2.1 million subreddits and now includes AI-enhanced columns that provide: - Sentiment Analysis: AI-driven sentiment scores for posts and comments, allowing users to gauge community mood and reactions. - Topic Categorization: Automated categorization of subreddit content into relevant topics, making it easier to filter and analyze specific types of discussions. - Predictive Insights: AI models that predict trends, content virality, and user engagement, helping users anticipate future developments within subreddits.

Sourced Directly from Reddit:

All social media data in this dataset is sourced directly from Reddit, ensuring accuracy and authenticity. The dataset is updated regularly, reflecting the latest trends and user interactions on the platform. This ensures that users have access to the most current and relevant data for their analyses.

Key Features:

• Subreddit Metrics: Detailed data on subreddit activity, including the number of posts, comments, votes, and user participation.
• User Engagement: Insights into how users interact with content, including comment threads, upvotes/downvotes, and participation rates.
• Trending Topics: Track emerging trends and viral content across the platform, helping you stay ahead of the curve in understanding social media dynamics.
• AI-Enhanced Analysis: Utilize AI-generated columns for sentiment analysis, topic categorization, and predictive insights, providing a deeper understanding of the data.

Use Cases:

• Social Media Analysis: Researchers and analysts can use this dataset to study online behavior, track the spread of information, and understand how content resonates with different audiences.
• Market Research: Marketers can leverage the dataset to identify target audiences, understand consumer preferences, and tailor campaigns to specific communities.
• Content Strategy: Content creators and strategists can use insights from the dataset to craft content that aligns with trending topics and user interests, maximizing engagement.
• Academic Research: Academics can explore the dynamics of online communities, studying everything from the spread of misinformation to the formation of online subcultures.

Data Quality and Reliability:

The Reddit Subreddit Dataset emphasizes data quality and reliability. Each record is carefully compiled from Reddit’s vast database, ensuring that the information is both accurate and up-to-date. The AI-generated columns further enhance the dataset's value, providing automated insights that help users quickly identify key trends and sentiments.

Integration and Usability:

The dataset is provided in a format that is compatible with most data analysis tools and platforms, making it easy to integrate into existing workflows. Users can quickly import, analyze, and utilize the data for various applications, from market research to academic studies.

User-Friendly Structure and Metadata:

The data is organized for easy navigation and analysis, with metadata files included to help users identify relevant subreddits and data points. The AI-enhanced columns are clearly labeled and structured, allowing users to efficiently incorporate these insights into their analyses.

Ideal For:

• Data Analysts: Conduct in-depth analyses of subreddit trends, user engagement, and content virality. The dataset’s extensive coverage and AI-enhanced insights make it an invaluable tool for data-driven research.
• Marketers: Use the dataset to better understand your target audience, tailor campaigns to specific interests, and track the effectiveness of marketing efforts across Reddit.
• Researchers: Explore the social dynamics of online communities, analyze the spread of ideas and information, and study the impact of digital media on public discourse, all while leveraging AI-generated insights.

This dataset is an essential resource for anyone looking to understand the intricacies of Reddit's vast ecosystem, offering the data and AI-enhanced insights needed to drive informed decisions and strategies across various fields. Whether you’re tracking emerging trends, analyzing user behavior, or conduc...

Automated social media accounts, known as bots, have been shown to spread disinformation and manipulate online discussions. We study the behavior of retweet bots on Twitter during the first impeachment of U.S. President Donald Trump. We collect over 67.7 million impeachment related tweets from 3.6 million users, along with their 53.6 million edge follower network. We find although bots represent 1% of all users, they generate over 31% of all impeachment related tweets. We also find bots share more disinformation, but use less toxic language than other users. Among supporters of the Qanon conspiracy theory, a popular disinformation campaign, bots have a prevalence near 10%. The follower network of Qanon supporters exhibits a hierarchical structure, with bots acting as central hubs surrounded by isolated humans. We quantify bot impact using the generalized harmonic influence centrality measure. We find there are a greater number of pro-Trump bots, but on a per bot basis, anti-Trump and pro-Trump bots have similar impact, while Qanon bots have less impact. This lower impact is due to the homophily of the Qanon follower network, suggesting this disinformation is spread mostly within online echo-chambers.

We used supervised machine-learning techniques to examine ideological asymmetries in online rumor transmission. Although liberals were more likely than conservatives to communicate in general about the 2013 Boston Marathon bombings (Study 1, N = 26,422) and 2020 death of the sex trafficker Jeffrey Epstein (Study 2, N = 141,670), conservatives were more likely to share rumors. Rumor-spreading decreased among liberals following official correction, but it increased among conservatives. Marathon rumors were spread twice as often by conservatives pre-correction, and nearly 10 times more often post-correction. Epstein rumors were spread twice as often by conservatives pre-correction, and nearly 8 times more often post-correction. With respect to ideologically congenial rumors, conservatives circulated the rumor that the Clinton family was involved in Epstein’s death 18.6 times more often than liberals circulated the rumor that the Trump family was involved. More than 96% of all fake news domains were shared by conservative Twitter users.

A July 2021 survey of online adults in the United States found that 34 percent of respondents felt that the user who originally posted the false information are most responsible for the spread of coronavirus misinformation on social media. Additionally, 27 percent of respondents stated that social media companies were most responsible.

Snapshot img

Newspaper Publishing Market Size 2025-2029

The newspaper publishing market size is forecast to increase by USD 4.12 billion at a CAGR of 1.1% between 2024 and 2029.

The market is experiencing significant shifts, driven by both trends and challenges. One key trend is the increasing demand for newspapers in developing countries, where literacy rates are rising and a growing middle class seeks reliable news sources. Presses are embracing virtual reality, video, digital audio/podcasts, and other digital platforms to reach readers. The penetration rate of affordable internet and online media platforms continues to grow, putting pressure on print newspapers, magazines, and industrial printing. 
Another trend is the growing adoption of subscription-based models, as publishers seek to offset declining printed circulation. However, the market also faces challenges, including the decline in printed newspaper circulation due to the rise of digital media. This trend is particularly pronounced in developed countries, where the shift to digital news consumption is more advanced. Despite these challenges, the market continues to evolve, with publishers exploring new business models and technologies to adapt to the changing media landscape.

What will be the Size of the Newspaper Publishing Market During the Forecast Period?

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In the dynamic world of media, publishing is undergoing a significant digital transformation. Traditional print media, including newspapers, face increasing pressure from digital technology and mobile platforms. Image advertisers, once a staple revenue stream, are shifting towards digital advertising technology. Business models for newspapers are evolving, with subscription models gaining popularity.
Digital publishing offerd flexibility and accessibility, allowing readers to consume news on-demand. Virtual reality and immersive content are emerging trends, providing new opportunities for engaging storytelling. However, this transition comes with challenges. Trust in journalists and quality journalism remain crucial, as digital platforms and social media can spread misinformation.
Daily paid circulation continues to decline, forcing publishers to adapt. Advertisers, too, are embracing digital transformation, investing in video, digital audio/podcasts, and advertising technology. Newspapers must innovate to stay competitive, offering unique content and experiences to attract and retain readers.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Platform

  Traditional
  Digital


Type

  General news
  Specific news


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France
    Italy
    Spain


  APAC

    China
    India
    Japan


  Middle East
  South America

By Platform Insights

The traditional segment is estimated to witness significant growth during the forecast period. The traditional market encompasses the production and distribution of physical newspapers. This format offers a tangible reading experience, allowing audiences to hold and read the printed pages. Newspapers are distributed via various channels, including newsstands, retail outlets, subscriptions, and home delivery services. In contrast to digital formats, print newspapers provide a tactile experience that some readers prefer. However, the industry is undergoing digital transformation, with increasing numbers of readers turning to online sources for news. This shift is driven by the convenience and accessibility of digital platforms, as well as the ability to customize content and engage with social media. Advertisers are also moving towards digital advertising, utilizing subscription models, advertising technology, and video content. Despite these changes, trust in journalists and the importance of quality journalism remain key factors in the market.

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The traditional segment was valued at USD 44.88 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 44% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The North American market is a mature and competitive industry undergoing significant transformation due to digitalization. The US, being the largest market in the region, faces challenges such as declining print circulation, decreasing advertising revenue, and rising produc

Tweet data summary.