As of February 2025, English was the most popular language for web content, with over 49.4 percent of websites using it. Spanish ranked second, with six percent of web content, while the content in the German language followed, with 5.6 percent. English as the leading online language United States and India, the countries with the most internet users after China, are also the world's biggest English-speaking markets. The internet user base in both countries combined, as of January 2023, was over a billion individuals. This has led to most of the online information being created in English. Consequently, even those who are not native speakers may use it for convenience. Global internet usage by regions As of October 2024, the number of internet users worldwide was 5.52 billion. In the same period, Northern Europe and North America were leading in terms of internet penetration rates worldwide, with around 97 percent of its populations accessing the internet.

According to a 2023 survey, 43 percent of internet users in urban India preferred using the Internet in English. Meanwhile, 57 percent of users accessed the internet in Indian languages, with Hindi being the most preferred language among them. Over 300 million internet users reside in the urban areas of India.

As of December 2023, the English subdomain of Wikipedia had around 6.91 million articles published, being the largest subdomain of the website by number of entries and registered active users. German and French ranked third and fourth, with over 29.6 million and 26.5 million entries. Being the only Asian language figuring among the top 10, Cebuano was the language with the second-most articles on the portal, amassing around 6.11 million entries. However, while most Wikipedia articles in English and other European languages are written by humans, entries in Cebuano are reportedly mostly generated by bots.

Content

Sign language images taken by 7 different users, a total of 1687 images.

Acknowledgements

Data set belong to Yoav Ram as part of IDC Scientific computation in Python course

Free Online Translator Market Outlook

The global market size for free online translator services was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 2.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.7% during the forecast period. One of the major growth factors driving this market is the increasing globalization and the need for effective communication across different languages and regions.

The demand for free online translators is significantly driven by the globalization of businesses, which necessitates the translation of documents, websites, and marketing materials into multiple languages to reach a broader audience. The rise in international trade and cross-border e-commerce activities has also amplified the need for seamless communication tools. Furthermore, the adoption of free online translators has grown exponentially due to the increasing number of internet users worldwide, many of whom require translation services to access content in different languages.

Another critical growth factor is the advancement in artificial intelligence (AI) and machine learning (ML) technologies, which have substantially improved the accuracy and reliability of online translation services. These technological advancements enable the development of sophisticated algorithms that can handle complex translations in real-time, thus enhancing user experience. Additionally, the integration of natural language processing (NLP) capabilities into translation software has made it possible to understand and translate idiomatic expressions and cultural nuances more accurately.

The increasing demand for multilingual communication in the educational sector is also a significant contributor to the market's growth. Educational institutions are leveraging free online translators to facilitate learning in diverse linguistic environments, thus making education more accessible to students who speak different languages. The proliferation of online learning platforms and international collaborations in academia further drives the need for reliable translation services.

In the realm of multilingual communication, the role of a Simultaneous Interpreter has become increasingly vital. These professionals are adept at providing real-time translations during conferences, meetings, and events, ensuring that language barriers do not impede the flow of information. As globalization continues to expand, the demand for simultaneous interpretation services is on the rise, particularly in international business settings and diplomatic engagements. The integration of technology with human expertise in this field is enhancing the accuracy and efficiency of translations, making it an indispensable service in today's interconnected world.

Regionally, the Asia Pacific is expected to witness significant growth in the free online translator market due to the region's diverse linguistic landscape and the increasing penetration of the internet. Countries like China, India, and Japan are leading the charge in adopting online translation services to bridge language barriers in business and personal communication. North America and Europe are also substantial markets, driven by technological advancements and high internet usage rates. Latin America and the Middle East & Africa regions are gradually catching up, with increasing internet penetration and growing awareness about the benefits of online translation tools.

Type Analysis

The free online translator market is segmented by type into text translation, speech translation, image translation, and others. Text translation remains the most widely used type, primarily because it forms the basis of most online communication. Innovations in text translation have made it possible to translate large volumes of text quickly and accurately, which is essential for businesses, educational institutions, and individual users. Text translation tools are increasingly being integrated into various applications, such as web browsers, office suites, and mobile apps, making them highly accessible and user-friendly.

Speech translation has seen significant growth, fueled by advancements in voice recognition technologies and the increasing use of voice-activated assistants. This segment is partic

Website Translation Service Market Outlook

The global market size for Website Translation Services was valued at USD 6.8 billion in 2023 and is projected to reach USD 14.2 billion by 2032, growing at a CAGR of 8.5% during the forecast period. The primary growth factor contributing to this market's expansion is the increasing globalization of businesses, which necessitates the translation of websites to cater to a diverse and multilingual audience. Companies are increasingly recognizing the importance of providing content in multiple languages to enhance user experience, boost engagement, and improve conversion rates.

One of the significant growth factors for this market is the rapid digital transformation across various industries. Businesses are increasingly moving online, leading to the proliferation of e-commerce platforms, digital content, and online services. This shift has accelerated the demand for website translation services to ensure that content is accessible and understandable to global audiences. Moreover, advancements in machine translation technologies, such as neural machine translation (NMT), have made it easier and more cost-effective for businesses to translate their websites, further driving market growth.

Another crucial factor fueling the growth of the website translation service market is the increasing importance of localization. Localization goes beyond mere translation; it involves adapting content to suit the cultural, linguistic, and regional preferences of the target audience. Businesses are investing in localization services to provide a more personalized and culturally relevant experience to their users. This trend is particularly prominent in sectors such as e-commerce, travel and hospitality, and media and entertainment, where user engagement and customer satisfaction are paramount.

The rise of small and medium enterprises (SMEs) going global is also contributing to the market's growth. SMEs are increasingly expanding their operations beyond domestic markets, seeking new growth opportunities in international markets. To effectively penetrate these markets, SMEs are investing in website translation services to communicate their value propositions in the local languages of their target customers. This trend is expected to continue, driving the demand for website translation services over the forecast period.

In addition to website translation, businesses often require specialized services such as Legal Document Translation Services. This is particularly important for companies operating in multiple jurisdictions, where legal compliance and accurate communication of legal terms are critical. Legal document translation involves not just linguistic accuracy but also a deep understanding of legal terminology and context. Professional translators with expertise in legal matters ensure that documents are translated with precision, maintaining the integrity and intent of the original content. This service is essential for industries such as finance, real estate, and international trade, where legal documentation plays a pivotal role in operations.

Regionally, North America and Europe have been the dominant markets for website translation services, primarily due to the high concentration of multinational corporations and a strong emphasis on digital presence. However, the Asia Pacific region is expected to witness the highest growth during the forecast period, driven by the rapid adoption of digital technologies, increasing internet penetration, and a growing number of internet users. The region's diverse linguistic landscape also necessitates the need for comprehensive website translation services to reach a broader audience.

Service Type Analysis

The website translation service market can be broadly segmented into Machine Translation, Human Translation, Post-Editing, and Localization. Each of these service types has its unique advantages and applications, catering to different business needs and requirements. Machine Translation, for instance, is known for its speed and cost-effectiveness, making it an attractive option for businesses needing quick translations. With advancements in AI and machine learning, machine translation has significantly improved in accuracy and quality, though it still may not match the precision of human translation in some contexts.

Human Translation remains a critical service type due to its ability to provide high-q

Dataset

This dataset was created by Kevin Matthe Caramancion

Released under CC BY-SA 4.0

Contents

Latvian Web, home pages of ministries and state public services, army, etc. were crawled, and parallel Latvian-English content was collected.

This dataset has been created within the framework of the European Language Resource Coordination (ELRC) Connecting Europe Facility - Automated Translation (CEF.AT) actions SMART 2014/1074 and SMART 2015/1091. For further information on the project: http://lr-coordination.eu.

This statistic represents the challenges faced by non-English or Indian language internet users across India in 2016. About 70 percent of Indian language internet users found using English keyboards a challenge while about 30 percent were aware of online content but were not comfortable using the online medium during the measured time period.

FineWeb-C: Educational content in many languages, labelled by the community This is a link to the Danish part of the dataset.

This is a collaborative, community-driven project that expands upon the FineWeb2 dataset. Our goal is to create high-quality educational content annotations across hundreds of languages.

By enhancing web content with these annotations, we aim to improve the development of Large Language Models (LLMs) in all languages, making AI technology more accessible and effective globally.

The annotations in this dataset will help train AI systems to automatically identify high-quality educational content in more languages and in turn help build better Large Language Models for all languages.

What the community is doing: For a given language, look at a page of web content from the FineWeb2 dataset in Argilla. Rate how educational the content is. Flag problematic content i.e. content that is malformed or in the wrong language. Once a language reaches 1,000 annotations, the dataset will be included in this dataset! Alongside rating the educational quality of the content, different language communities are discussing other ways to improve the quality of data for their language in our Discord discussion channel.

The use of this dataset is also subject to CommonCrawl's Terms of Use.

This is a parallel and aligned corpus of bilingual texts crawled from multilingual websites, which contains 1,249 TUs.

This dataset has been created within the framework of the European Language Resource Coordination (ELRC) Connecting Europe Facility - Automated Translation (CEF.AT) actions SMART 2014/1074 and SMART 2015/1091. For further information on the project: http://lr-coordination.eu.

Online Language Learning Market size was valued at USD 14,198.80 Million in 2023 and is projected to reach USD 31,973.88 Million by 2031, growing at a CAGR of 12.67% from 2024 to 2031.

Global Online Language Learning Market Definition

Online language learning comprises digital content and solutions that facilitate the learning of languages through ICT tools and platforms, such as mobile apps, e-books, games, videos, audio clips, online tutoring and many others. These tools and avenues are interactive, allow real-time feedback, and enhance learning processes as they involve different formats to impart information. Among the languages spoken globally, English has emerged as the most preferred language to learn, followed by Mandarin Chinese. The benefits of quality education and employment opportunities are encouraging individuals to enroll in language learning programs. Many employees wanting to enhance their language skills are interested in taking these courses. Also, language learning is imperative for companies that have global branches as they need to be familiar with the local language before product launches.

Context:

How frequently a word occurs in a language is an important piece of information for natural language processing and linguists. In natural language processing, very frequent words tend to be less informative than less frequent one and are often removed during preprocessing. Human language users are also sensitive to word frequency. How often a word is used affects language processing in humans. For example, very frequent words are read and understood more quickly and can be understood more easily in background noise.

Content:

This dataset contains the counts of the 333,333 most commonly-used single words on the English language web, as derived from the Google Web Trillion Word Corpus.

Acknowledgements:

Data files were derived from the Google Web Trillion Word Corpus (as described by Thorsten Brants and Alex Franz, and distributed by the Linguistic Data Consortium) by Peter Norvig. You can find more information on these files and the code used to generate them here.

The code used to generate this dataset is distributed under the MIT License.

Inspiration:

• Can you tag the part of speech of these words? Which parts of speech are most frequent? Is this similar to other languages, like Japanese?
• What differences are there between the very frequent words in this dataset, and the the frequent words in other corpora, such as the Brown Corpus or the TIMIT corpus? What might these differences tell us about how language is used?

Snapshot img

Online Language Learning Market Size 2025-2029

The online language learning market size is forecast to increase by USD 81.55 billion at a CAGR of 27.5% between 2024 and 2029.

The market is experiencing significant growth due to its cost-effective and flexible nature, making it an attractive alternative to traditional language classes. The convenience of learning at one's own pace and location, coupled with the affordability of online courses and tutoring is driving the market's expansion. Furthermore, the integration of artificial intelligence (AI) in language learning platforms is revolutionizing the industry by providing personalized learning experiences and real-time feedback. However, the market faces challenges as well. Open sources, such as free language learning websites and applications, pose a significant threat by offering similar services at no cost.
These platforms, while not as comprehensive as paid offerings, can still attract price-sensitive consumers and limit the revenue potential for market participants. Companies must differentiate themselves by offering unique features, superior learning outcomes, or a more engaging user experience to justify their premium pricing. To navigate this challenge, strategic partnerships, collaborations, and continuous innovation in AI technology could provide competitive advantages.

What will be the Size of the Online Language Learning Market during the forecast period?

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The market continues to evolve, driven by the growing demand for effective and engaging language learning solutions. Corporate language training is a significant sector within this market, as businesses recognize the importance of multilingualism in expanding their global reach. ESL learning, or English as a Second Language, is another thriving area, catering to the needs of advanced language learners and those seeking to improve their proficiency in English. Travel language learning is another application of the market, with individuals increasingly recognizing the value of being able to communicate effectively in foreign countries.
Virtual classrooms and mobile language learning have also gained popularity, making language learning more accessible and convenient for learners. Language acquisition and second language acquisition are ongoing processes, with learners continually seeking new ways to improve their proficiency. Grammar exercises, pronunciation practice, and personalized learning are some of the strategies used to enhance language learning methodology. Translation services are another application of the market, providing solutions for individuals and businesses to communicate effectively across language barriers. The market for language learning games is vast and diverse, with new technologies and approaches continually emerging to meet the evolving needs of language learners. Language learning tips, vocabulary builders, and language assessment tools are essential resources for learners, helping them to optimize their learning experience and track their progress. The market is a dynamic and ever-evolving landscape, with continuous innovation and growth expected in the years to come.

How is this Online Language Learning Industry segmented?

The online language learning industry 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.

End-user

  Courses
  Solutions
  Apps


Language

  English
  Mandarin
  Spanish
  Others


Delivery Format

  Live Online Classes
  Self-Paced Online Courses
  Hybrid Learning
  Live Online Classes
  Self-Paced Online Courses
  Hybrid Learning


Target Learner

  School Students
  University Students
  Working Professionals
  Adults for Personal Development
  School Students
  University Students
  Working Professionals
  Adults for Personal Development


End User Type

  Individual Learners
  Educational Institutions
  Individual Learners
  Educational Institutions


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Spain
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By End-user Insights

The courses segment is estimated to witness significant growth during the forecast period.

Online language learning has become a popular and accessible solution for individuals seeking to expand their linguistic abilities. Courses form the foundation of this learning journey, encompassing digital content and courseware that facilitate language acquisition. The affordability of online language courses, compared to traditional classroom programs, broadens accessibility to a larger audience, including those with financi

Topics: mother tongue; used languages to read or watch internet content and frequency of use; used languages to write in the internet and frequency of use; frequency of using languages different from own language with regard to the following internet acti

In November 2024, Google.com was the most popular website worldwide with 136 billion average monthly visits. The online platform has held the top spot as the most popular website since June 2010, when it pulled ahead of Yahoo into first place. Second-ranked YouTube generated more than 72.8 billion monthly visits in the measured period. The internet leaders: search, social, and e-commerce Social networks, search engines, and e-commerce websites shape the online experience as we know it. While Google leads the global online search market by far, YouTube and Facebook have become the world’s most popular websites for user generated content, solidifying Alphabet’s and Meta’s leadership over the online landscape. Meanwhile, websites such as Amazon and eBay generate millions in profits from the sale and distribution of goods, making the e-market sector an integral part of the global retail scene. What is next for online content? Powering social media and websites like Reddit and Wikipedia, user-generated content keeps moving the internet’s engines. However, the rise of generative artificial intelligence will bring significant changes to how online content is produced and handled. ChatGPT is already transforming how online search is performed, and news of Google's 2024 deal for licensing Reddit content to train large language models (LLMs) signal that the internet is likely to go through a new revolution. While AI's impact on the online market might bring both opportunities and challenges, effective content management will remain crucial for profitability on the web.

This dataset endeavors to fill the research void by presenting a meticulously curated collection of misogynistic memes in a code-mixed language of Hindi and English. It introduces two sub-tasks: the first entails a binary classification to determine the presence of misogyny in a meme, while the second task involves categorizing the misogynistic memes into multiple labels, including Objectification, Prejudice, and Humiliation. For more Information and Citation: Singh, A., Sharma, D., & Singh… See the full description on the dataset page: https://huggingface.co/datasets/Aakash941/MIMIC-Meme-Dataset.

Wiki-Reliability: Machine Learning datasets for measuring content reliability on WikipediaConsists of metadata features and content text datasets, with the formats:- {template_name}_features.csv - {template_name}_difftxt.csv.gz - {template_name}_fulltxt.csv.gz For more details on the project, dataset schema, and links to data usage and benchmarking:https://meta.wikimedia.org/wiki/Research:Wiki-Reliability:_A_Large_Scale_Dataset_for_Content_Reliability_on_Wikipedia

Preprint

Abstract

The World Wide Web is a complex interconnected digital ecosystem, where information and attention flow between platforms and communities throughout the globe. These interactions co-construct how we understand the world, reflecting and shaping public discourse. Unfortunately, researchers often struggle to understand how information circulates and evolves across the web because platform-specific data is often siloed and restricted by linguistic barriers. To address this gap, we present a comprehensive, multilingual dataset capturing all Wikipedia links shared in posts and comments on Reddit from 2020 to 2023, excluding those from private and NSFW subreddits. Each linked Wikipedia article is enriched with revision history, page view data, article ID, redirects, and Wikidata identifiers. Through a research agreement with Reddit, our dataset ensures user privacy while providing a query and ID mechanism that integrates with the Reddit and Wikipedia APIs. This enables extended analyses for researchers studying how information flows across platforms. For example, Reddit discussions use Wikipedia for deliberation and fact-checking which subsequently influences Wikipedia content, by driving traffic to articles or inspiring edits. By analyzing the relationship between information shared and discussed on these platforms, our dataset provides a foundation for examining the interplay between social media discourse and collaborative knowledge consumption and production.

Datasheet

Motivation

The motivations for this dataset stem from the challenges researchers face in studying the flow of information across the web. While the World Wide Web enables global communication and collaboration, data silos, linguistic barriers, and platform-specific restrictions hinder our ability to understand how information circulates, evolves, and impacts public discourse. Wikipedia and Reddit, as major hubs of knowledge sharing and discussion, offer an invaluable lens into these processes. However, without comprehensive data capturing their interactions, researchers are unable to fully examine how platforms co-construct knowledge. This dataset bridges this gap, providing the tools needed to study the interconnectedness of social media and collaborative knowledge systems.

Composition

WikiReddit, a comprehensive dataset capturing all Wikipedia mentions (including links) shared in posts and comments on Reddit from 2020 to 2023, excluding those from private and NSFW (not safe for work) subreddits. The SQL database comprises 336K total posts, 10.2M comments, 1.95M unique links, and 1.26M unique articles spanning 59 languages on Reddit and 276 Wikipedia language subdomains. Each linked Wikipedia article is enriched with its revision history and page view data within a ±10-day window of its posting, as well as article ID, redirects, and Wikidata identifiers. Supplementary anonymous metadata from Reddit posts and comments further contextualizes the links, offering a robust resource for analysing cross-platform information flows, collective attention dynamics, and the role of Wikipedia in online discourse.

Collection Process

Data was collected from the Reddit4Researchers and Wikipedia APIs. No personally identifiable information is published in the dataset. Data from Reddit to Wikipedia is linked via the hyperlink and article titles appearing in Reddit posts.

Preprocessing/cleaning/labeling

Extensive processing with tools such as regex was applied to the Reddit post/comment text to extract the Wikipedia URLs. Redirects for Wikipedia URLs and article titles were found through the API and mapped to the collected data. Reddit IDs are hashed with SHA-256 for post/comment/user/subreddit anonymity.

Uses

We foresee several applications of this dataset and preview four here. First, Reddit linking data can be used to understand how attention is driven from one platform to another. Second, Reddit linking data can shed light on how Wikipedia's archive of knowledge is used in the larger social web. Third, our dataset could provide insights into how external attention is topically distributed across Wikipedia. Our dataset can help extend that analysis into the disparities in what types of external communities Wikipedia is used in, and how it is used. Fourth, relatedly, a topic analysis of our dataset could reveal how Wikipedia usage on Reddit contributes to societal benefits and harms. Our dataset could help examine if homogeneity within the Reddit and Wikipedia audiences shapes topic patterns and assess whether these relationships mitigate or amplify problematic engagement online.

Distribution

The dataset is publicly shared with a Creative Commons Attribution 4.0 International license. The article describing this dataset should be cited: https://doi.org/10.48550/arXiv.2502.04942

Maintenance

Patrick Gildersleve will maintain this dataset, and add further years of content as and when available.

SQL Database Schema

Table: posts

Table: comments

Table: postlinks

Table: commentlinks

The global market for online language learning software for children is experiencing robust growth, driven by increasing parental awareness of the benefits of early language acquisition and the widespread adoption of technology in education. The market, segmented by age group (0-3, 4-7, 8-12) and learning type (phonetic, word, sentence), is witnessing a significant shift towards gamified and interactive learning platforms. While the exact market size for 2025 is unavailable, considering a plausible CAGR of 15% (a conservative estimate given the sector's dynamism) from an assumed 2019 base of $500 million, the market value in 2025 could be estimated at around $1.2 billion. This growth is fueled by several key factors including the rising penetration of smartphones and tablets among families, the increasing accessibility of high-speed internet, and a growing preference for convenient and engaging online learning resources over traditional methods. Competitive pressures amongst established players like Duolingo and Babbel alongside emerging niche players focusing on specific languages or age groups are further shaping the market landscape. Regional variations exist, with North America and Europe currently dominating market share due to higher disposable incomes and technological infrastructure, but rapid growth is anticipated from regions like Asia-Pacific, fueled by expanding internet access and a growing middle class. Challenges include ensuring equitable access to technology and high-quality education across different socioeconomic strata and providing content that is culturally relevant and engaging for diverse learners. The segment for children aged 4-7 shows the highest growth potential due to the crucial developmental stage for language acquisition at this age. Furthermore, the ‘phonetic learning’ segment is expected to maintain a leading position, providing the foundation for future language development. However, emerging trends like the incorporation of AI-powered personalized learning experiences and immersive virtual reality (VR) technologies represent significant opportunities for innovation and market expansion. Challenges include maintaining user engagement, ensuring parental trust and data security, and managing the balance between screen time and other learning activities. Successful players in this market will need to leverage technology effectively, create engaging and age-appropriate content, and cultivate strong brand loyalty. Future market analysis will need to incorporate an evaluation of these evolving trends and their impact on market segmentation and growth.