In 2025, there were around 1.53 billion people worldwide who spoke English either natively or as a second language, slightly more than the 1.18 billion Mandarin Chinese speakers at the time of survey. Hindi and Spanish accounted for the third and fourth most widespread languages that year. Languages in the United States The United States does not have an official language, but the country uses English, specifically American English, for legislation, regulation, and other official pronouncements. The United States is a land of immigration, and the languages spoken in the United States vary as a result of the multicultural population. The second most common language spoken in the United States is Spanish or Spanish Creole, which over than 43 million people spoke at home in 2023. There were also 3.5 million Chinese speakers (including both Mandarin and Cantonese),1.8 million Tagalog speakers, and 1.57 million Vietnamese speakers counted in the United States that year. Different languages at home The percentage of people in the United States speaking a language other than English at home varies from state to state. The state with the highest percentage of population speaking a language other than English is California. About 45 percent of its population was speaking a language other than English at home in 2023.

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.

In 2023, around 43.37 million people in the United States spoke Spanish at home. In comparison, approximately 998,179 people were speaking Russian at home during the same year. The distribution of the U.S. population by ethnicity can be accessed here. A ranking of the most spoken languages across the world can be accessed here.

The GlobalPhone corpus developed in collaboration with the Karlsruhe Institute of Technology (KIT) was designed to provide read speech data for the development and evaluation of large continuous speech recognition systems in the most widespread languages of the world, and to provide a uniform, multilingual speech and text database for language independent and language adaptive speech recognition as well as for language identification tasks. The entire GlobalPhone corpus enables the acquisition of acoustic-phonetic knowledge of the following 22 spoken languages: Arabic (ELRA-S0192), Bulgarian (ELRA-S0319), Chinese-Mandarin (ELRA-S0193), Chinese-Shanghai (ELRA-S0194), Croatian (ELRA-S0195), Czech (ELRA-S0196), French (ELRA-S0197), German (ELRA-S0198), Hausa (ELRA-S0347), Japanese (ELRA-S0199), Korean (ELRA-S0200), Polish (ELRA-S0320), Portuguese (Brazilian) (ELRA-S0201), Russian (ELRA-S0202), Spanish (Latin America) (ELRA-S0203), Swahili (ELRA-S0375), Swedish (ELRA-S0204), Tamil (ELRA-S0205), Thai (ELRA-S0321), Turkish (ELRA-S0206), Ukrainian (ELRA-S0377), and Vietnamese (ELRA-S0322).In each language about 100 sentences were read from each of the 100 speakers. The read texts were selected from national newspapers available via Internet to provide a large vocabulary. The read articles cover national and international political news as well as economic news. The speech is available in 16bit, 16kHz mono quality, recorded with a close-speaking microphone (Sennheiser 440-6). The transcriptions are internally validated and supplemented by special markers for spontaneous effects like stuttering, false starts, and non-verbal effects like laughing and hesitations. Speaker information like age, gender, occupation, etc. as well as information about the recording setup complement the database. The entire GlobalPhone corpus contains over 450 hours of speech spoken by more than 2100 native adult speakers.Data is shortened by means of the shorten program written by Tony Robinson. Alternatively, the data could be delivered unshorten.The Polish part of GlobalPhone was collected from altogether 102 native speakers in Poland, of which 48 speakers were female and 54 speakers were male. The majority of speakers are between 20 and 39 years old, the age distribution ranges from 18 to 65 years. Most of the speakers are non-smokers in good health conditions. Each speaker read on average about 100 utterances from newspaper articles, in total we recorded 10130 utterances. The speech was recorded using a close-talking microphone Sennheiser HM420 in a push-to-talk scenario. All data were recorded at 16kHz and 16bit resolution in PCM format. The data collection took place in small and large rooms, about half of the recordings took place under very quiet noise conditions, the other half with moderate background noise. Information on recording place and environmental noise conditions are provided in a separate speaker session file for each speaker. The text data used for reco...

Context

This is a dataset I found online through the Google Dataset Search portal.

Content

The American Community Survey (ACS) 2009-2013 multi-year data are used to list all languages spoken in the United States that were reported during the sample period. These tables provide detailed counts of many more languages than the 39 languages and language groups that are published annually as a part of the routine ACS data release. This is the second tabulation beyond 39 languages since ACS began.

The tables include all languages that were reported in each geography during the 2009 to 2013 sampling period. For the purpose of tabulation, reported languages are classified in one of 380 possible languages or language groups. Because the data are a sample of the total population, there may be languages spoken that are not reported, either because the ACS did not sample the households where those languages are spoken, or because the person filling out the survey did not report the language or reported another language instead.

The tables also provide information about self-reported English-speaking ability. Respondents who reported speaking a language other than English were asked to indicate their ability to speak English in one of the following categories: "Very well," "Well," "Not well," or "Not at all." The data on ability to speak English represent the person’s own perception about his or her own ability or, because ACS questionnaires are usually completed by one household member, the responses may represent the perception of another household member.

These tables are also available through the Census Bureau's application programming interface (API). Please see the developers page for additional details on how to use the API to access these data.

Acknowledgements

Sources:

Google Dataset Search: https://toolbox.google.com/datasetsearch

2009-2013 American Community Survey

Original dataset: https://www.census.gov/data/tables/2013/demo/2009-2013-lang-tables.html

Downloaded From: https://data.world/kvaughn/languages-county

Banner and thumbnail photo by Farzad Mohsenvand on Unsplash

​ “Middle East” LanguagesIndependent StudyFebruary 16, 2012Amanda DoyleCo-authors: Kevin Ragborg, Marc Puricelli, and Maria LindellDespite the relatively small geographical size of the “Middle East,” there is great diversity of the spoken languages within the region. The most common spoken language of the “Middle East” is Arabic, a Semitic language closely related to Hebrew that was developed beginning in the 8th century BC. Currently, around 280 million people speak Arabic in the regions of the “Middle East” and North Africa encompassing the countries between Morocco to Iraq. The Qur’an, the central religious text of Islam, is only allowed to be written in Arabic, giving the language a very important role in the Muslim world. Different from some other languages, there are many different dialects of Arabic, which can make it difficult for speakers from different areas of the Arabic speaking world to understand one another3. The next major language of the Middle East is Persian or Farsi, the national language of Iran. Persian is spoken by an estimated 65 million people, most of which are concentrated in Iran but there are significant Persian speaking populations in Afghanistan and the United Arab Emirates. Younger than Arabic, Persian was developed around 400 BC and is closely related to Hindi and Urdu. There are three main dialects of Persian: Iranian Persian (spoken in Iran), Dari Persian (spoken in Afghanistan) and Tajik Persian (spoken in Tajikistan.) 4Hebrew is spoken by roughly 3.8 million people in the “Middle East,” but this population is now concentrated in Israel and the neighboring countries. Though, not all Jews, even Israeli Jews, speak Hebrew since centuries ago, Hebrew ceased being a working language; however, due to Jewish nationalism, the Zionist movement, and the need for a unifying language between immigrants into Israel the language has been revived. Turkish, the national language of Turkey and the main spoken language of the Turkish nation is also spoken by roughly 170,000 people in Cyprus and by minorities in the Fertile Crescent area. Kurdish is the language that unifies the Kurds, a nation that spans a large geographical range from Beirut to Afghanistan. Additionally, almost all countries in the “Middle East” have several minority languages, such as Berber, spoken by many North Africans, including some parts of northwestern Egypt. Azeri, a minority Turkic language, is often spoken in northwestern Iran. Turkish tribes in the southern Zagros Mountains in Iran speak Qashqai, while Baluchi is spoken in southeastern and eastern Iran by the Baluch peoples and migrants in United Arab Emirates and Oman. Nomadic tribes in the Zagros Mountains can be found speaking Luri. Lastly, Armenian, due to its historical significance is spoken by minorities in urban centers such as Beirut, Damascus, Aleppo, Tehran, and Cairo1.Works Cited (1) Held, Colbert C. Middle East Patterns – Places, Peoples and Politics. 2nd ed. Westview Press, Inc.: Boulder, Co, 1994, pgs. 76-80.(2) The World Factbook. Central Intelligence Agency. 2011. https://www.cia.gov/library/publications/the-world-factbook/fields/2098.html?countryName=Jordan&countryCode=jo&regionCode=me&#jo.(3) "Learn Arabic - All About the Arabic Language." Innovative Language Learning. Web. 28 Mar. 2011. http://innovativelanguage.com/languagelearning/arabic-language.(4) UCLA, Language Materials Projects. "Persian Language." Iran Chamber Society. Web. 29 Mar. 2011. http://www.iranchamber.com/literature/articles/persian_language.php.

The GlobalPhone corpus developed in collaboration with the Karlsruhe Institute of Technology (KIT) was designed to provide read speech data for the development and evaluation of large continuous speech recognition systems in the most widespread languages of the world, and to provide a uniform, multilingual speech and text database for language independent and language adaptive speech recognition as well as for language identification tasks. The entire GlobalPhone corpus enables the acquisition of acoustic-phonetic knowledge of the following 22 spoken languages: Arabic (ELRA-S0192), Bulgarian (ELRA-S0319), Chinese-Mandarin (ELRA-S0193), Chinese-Shanghai (ELRA-S0194), Croatian (ELRA-S0195), Czech (ELRA-S0196), French (ELRA-S0197), German (ELRA-S0198), Hausa (ELRA-S0347), Japanese (ELRA-S0199), Korean (ELRA-S0200), Polish (ELRA-S0320), Portuguese (Brazilian) (ELRA-S0201), Russian (ELRA-S0202), Spanish (Latin America) (ELRA-S0203), Swahili (ELRA-S0375), Swedish (ELRA-S0204), Tamil (ELRA-S0205), Thai (ELRA-S0321), Turkish (ELRA-S0206), Ukrainian (ELRA-S0377), and Vietnamese (ELRA-S0322).In each language about 100 sentences were read from each of the 100 speakers. The read texts were selected from national newspapers available via Internet to provide a large vocabulary. The read articles cover national and international political news as well as economic news. The speech is available in 16bit, 16kHz mono quality, recorded with a close-speaking microphone (Sennheiser 440-6). The transcriptions are internally validated and supplemented by special markers for spontaneous effects like stuttering, false starts, and non-verbal effects like laughing and hesitations. Speaker information like age, gender, occupation, etc. as well as information about the recording setup complement the database. The entire GlobalPhone corpus contains over 450 hours of speech spoken by more than 2100 native adult speakers.Data is shortened by means of the shorten program written by Tony Robinson. Alternatively, the data could be delivered unshorten.The Spanish (Latin America) corpus was produced using the La Nacion newspaper. It contains recordings of 100 speakers (44 males, 56 females) recorded in Heredia and San Jose, Costa Rica. The following age distribution has been obtained: 20 speakers are below 19, 54 speakers are between 20 and 29, 13 speakers are between 30 and 39, 5 speakers are between 40 and 49, and 8 speakers are over 50.

Mexico is the country with the largest number of native Spanish speakers in the world. As of 2024, 132.5 million people in Mexico spoke Spanish with a native command of the language. Colombia was the nation with the second-highest number of native Spanish speakers, at around 52.7 million. Spain came in third, with 48 million, and Argentina fourth, with 46 million. Spanish, a world language As of 2023, Spanish ranked as the fourth most spoken language in the world, only behind English, Chinese, and Hindi, with over half a billion speakers. Spanish is the official language of over 20 countries, the majority on the American continent, nonetheless, it's also one of the official languages of Equatorial Guinea in Africa. Other countries have a strong influence, like the United States, Morocco, or Brazil, countries included in the list of non-Hispanic countries with the highest number of Spanish speakers. The second most spoken language in the U.S. In the most recent data, Spanish ranked as the language, other than English, with the highest number of speakers, with 12 times more speakers as the second place. Which comes to no surprise following the long history of migrations from Latin American countries to the Northern country. Moreover, only during the fiscal year 2022. 5 out of the top 10 countries of origin of naturalized people in the U.S. came from Spanish-speaking countries.

The GlobalPhone corpus developed in collaboration with the Karlsruhe Institute of Technology (KIT) was designed to provide read speech data for the development and evaluation of large continuous speech recognition systems in the most widespread languages of the world, and to provide a uniform, multilingual speech and text database for language independent and language adaptive speech recognition as well as for language identification tasks. The entire GlobalPhone corpus enables the acquisition of acoustic-phonetic knowledge of the following 22 spoken languages: Arabic (ELRA-S0192), Bulgarian (ELRA-S0319), Chinese-Mandarin (ELRA-S0193), Chinese-Shanghai (ELRA-S0194), Croatian (ELRA-S0195), Czech (ELRA-S0196), French (ELRA-S0197), German (ELRA-S0198), Hausa (ELRA-S0347), Japanese (ELRA-S0199), Korean (ELRA-S0200), Polish (ELRA-S0320), Portuguese (Brazilian) (ELRA-S0201), Russian (ELRA-S0202), Spanish (Latin America) (ELRA-S0203), Swahili (ELRA-S0375), Swedish (ELRA-S0204), Tamil (ELRA-S0205), Thai (ELRA-S0321), Turkish (ELRA-S0206), Ukrainian (ELRA-S0377), and Vietnamese (ELRA-S0322).In each language about 100 sentences were read from each of the 100 speakers. The read texts were selected from national newspapers available via Internet to provide a large vocabulary. The read articles cover national and international political news as well as economic news. The speech is available in 16bit, 16kHz mono quality, recorded with a close-speaking microphone (Sennheiser 440-6). The transcriptions are internally validated and supplemented by special markers for spontaneous effects like stuttering, false starts, and non-verbal effects like laughing and hesitations. Speaker information like age, gender, occupation, etc. as well as information about the recording setup complement the database. The entire GlobalPhone corpus contains over 450 hours of speech spoken by more than 2100 native adult speakers.Data is shortened by means of the shorten program written by Tony Robinson. Alternatively, the data could be delivered unshorten.The Japanese corpus was produced using the Nikkei Shinbun newspaper. It contains recordings of 149 speakers (104 males, 44 females, 1 unspecified) recorded in Tokyo, Japan. The following age distribution has been obtained: 22 speakers are below 19, 90 speakers are between 20 and 29, 5 speakers are between 30 and 39, 2 speakers are between 40 and 49, and 1 speaker is over 50 (28 speakers age is unknown).

As of 2024, JavaScript and HTML/CSS were the most commonly used programming languages among software developers around the world, with more than 62 percent of respondents stating that they used JavaScript and just around 53 percent using HTML/CSS. Python, SQL, and TypeScript rounded out the top five most widely used programming languages around the world. Programming languages At a very basic level, programming languages serve as sets of instructions that direct computers on how to behave and carry out tasks. Thanks to the increased prevalence of, and reliance on, computers and electronic devices in today’s society, these languages play a crucial role in the everyday lives of people around the world. An increasing number of people are interested in furthering their understanding of these tools through courses and bootcamps, while current developers are constantly seeking new languages and resources to learn to add to their skills. Furthermore, programming knowledge is becoming an important skill to possess within various industries throughout the business world. Job seekers with skills in Python, R, and SQL will find their knowledge to be among the most highly desirable data science skills and likely assist in their search for employment.

Spanish is the second most widely-spoken language on Earth; over one in 20 humans alive today is a native speaker of Spanish. This medium-sized corpus contains 120 million words of modern Spanish taken from the Spanish-Language Wikipedia in 2010. This dataset is made up of 57 text files. Each contains multiple Wikipedia articles in an XML format. The text of each article is surrounded by tags. The initial tag also contains metadata about the article, including the article’s id and the title of the article. The text “ENDOFARTICLE.” appears at the end of each article, before the closing tag.

Hindi, with over *** million native speakers was the most spoken language across Indian homes, followed by Bengali with ** million speakers, as of 2011 census data. English native speakers accounted for about *** thousand during the measured time period. The colonial rule in India One of the most remarkable and widespread legacies that the British colonial rule left behind was the English language. Before independence, the English language was the solely used for higher education and in government and administrative processes. Post-independence, however, and till today, Hindi was claimed as the language with official government patronage. This lead to resistance from the southern states of India, where Hindi did not have prominence. Consequently, the Official Languages Act of 1963, was enacted by the parliament, which ensured the continued use of English for official purposes in conjunction with Hindi. Multi-linguistic cultures India has approximately ** major languages that are written in about ** different scripts. While the country’s official languages are both, English and Hindi, Hindi remains the most preferred language used online especially in the northern rural areas. The use of English is becoming increasingly popular in the urban areas. In addition, almost every state in India has its own official language that is studied in primary and secondary school as an obligatory second language. Among the most prominent are Bengali, Marathi, and Telugu.

Japan Centre of Excellence (JACEEX), is a brand under Jaceex Ventures LLP. Jaceex has been formed with a vision to create a world class workforce with skill sets, work and business ethics, sincerity and devotion as well as other great positive traits found in the Japanese workforce which has been responsible for having built world class Enterprises. For the Indian Students and youths stepping into this world, our objective is to provide life changing opportunity in the form of skill and work in Japan Japan Centre of Excellence (JACEEX) provides an integrated course schedule of learning through exploration, scrutiny and self reflection. We are offering Japanese Language and Culture training-Basic, Intermediate and High Levels. Our training is designed to make the trainee eligible to certify themselves with the globally recognised Japanese Language Proficiency Test (JLPT) Examination . This will help in building careers with Japanese companies in Japan , in India and also self employment.We also have the facility of Virtual Live class platform

JamPatoisNLI provides the first dataset for natural language inference in a creole language, Jamaican Patois. Many of the most-spoken low-resource languages are creoles. These languages commonly have a lexicon derived from a major world language and a distinctive grammar reflecting the languages of the original speakers and the process of language birth by creolization. This gives them a distinctive place in exploring the effectiveness of transfer from large monolingual or multilingual pretrained models. While our work, along with previous work, shows that transfer from these models to low-resource languages that are unrelated to languages in their training set is not very effective, we would expect stronger results from transfer to creoles. Indeed, our experiments show considerably better results from few-shot learning of JamPatoisNLI than for such unrelated languages, and help us begin to understand how the unique relationship between creoles and their high-resource base languages affect cross-lingual transfer. JamPatoisNLI, which consists of naturally-occurring premises and expert-written hypotheses, is a step towards steering research into a traditionally underserved language and a useful benchmark for understanding cross-lingual NLP.

The GlobalPhone corpus developed in collaboration with the Karlsruhe Institute of Technology (KIT) was designed to provide read speech data for the development and evaluation of large continuous speech recognition systems in the most widespread languages of the world, and to provide a uniform, multilingual speech and text database for language independent and language adaptive speech recognition as well as for language identification tasks. The entire GlobalPhone corpus enables the acquisition of acoustic-phonetic knowledge of the following 22 spoken languages: Arabic (ELRA-S0192), Bulgarian (ELRA-S0319), Chinese-Mandarin (ELRA-S0193), Chinese-Shanghai (ELRA-S0194), Croatian (ELRA-S0195), Czech (ELRA-S0196), French (ELRA-S0197), German (ELRA-S0198), Hausa (ELRA-S0347), Japanese (ELRA-S0199), Korean (ELRA-S0200), Polish (ELRA-S0320), Portuguese (Brazilian) (ELRA-S0201), Russian (ELRA-S0202), Spanish (Latin America) (ELRA-S0203), Swahili (ELRA-S0375), Swedish (ELRA-S0204), Tamil (ELRA-S0205), Thai (ELRA-S0321), Turkish (ELRA-S0206), Ukrainian (ELRA-S0377), and Vietnamese (ELRA-S0322).In each language about 100 sentences were read from each of the 100 speakers. The read texts were selected from national newspapers available via Internet to provide a large vocabulary. The read articles cover national and international political news as well as economic news. The speech is available in 16bit, 16kHz mono quality, recorded with a close-speaking microphone (Sennheiser 440-6). The transcriptions are internally validated and supplemented by special markers for spontaneous effects like stuttering, false starts, and non-verbal effects like laughing and hesitations. Speaker information like age, gender, occupation, etc. as well as information about the recording setup complement the database. The entire GlobalPhone corpus contains over 450 hours of speech spoken by more than 2100 native adult speakers.Data is shortened by means of the shorten program written by Tony Robinson. Alternatively, the data could be delivered unshorten.The Korean corpus was produced using the Hankyoreh Daily News. It contains recordings of 100 speakers (50 males, 50 females) recorded in Seoul, Korea. The following age distribution has been obtained: 7 speakers are below 19, 70 speakers are between 20 and 29, 19 speakers are between 30 and 39, and 3 speakers are between 40 and 49 (1 speaker age is unknown).

There are over 500 known languages in Nigeria. While the official language is English, its use is largely confined to urban elites. The most commonly used languages are Hausa, Yoruba, Igbo (Ibo) and Fulfulde. Edo, Efik, Adamawa Fulfulde, Idoma, and Central Kanuri are also widely spoken. The area of greatest diversity is the ‘Middle Belt’, the band of territory stretching across the country between the large language blocs of the north and the south. The reason for this diversity remains unclear, but three of Africa's four language families meet in the Middle Belt of Nigeria. This has had sociolinguistic consequences where frequent conflicts have erupted between the culture and language of particular groups.

ISO3 - International Organization for Standardization 3-digit country code

LANG_FAM - Language family

LANG_SUBGP - Language sub-family

SOURCE_DT - Primary source creation date

SOURCE - Primary source

Collection

This shapefile created by using Anthromapper consists of language layers that have been based on The World Language Mapping System (WLMS). Geographical terrain features, combined with a watershed model, were also used to predict the likely extent of ethnic and linguistic influence. The HGIS and metadata were supplemented with anthropological information from peer-reviewed journals and published books. The interpretation of names often produces multiple spellings of the same language; therefore similarly spelled or phonetic titles may be referencing the same language group.

The data included herein have not been derived from a registered survey and should be considered approximate unless otherwise defined. While rigorous steps have been taken to ensure the quality of each dataset, DigitalGlobe Analytics is not responsible for the accuracy and completeness of data compiled from outside sources.

Sources (HGIS)

Anthromapper. DigitalGlobe Analytics, April 2013.

World Language Mapping System (WLMS) Version 16. World GeoDatasets, April 2013.

Sources (Metadata)

Roger, Blench. "Position Paper: The Dimensions of Ethnicity, Language, and Culture in Nigeria." Last modified 2013. Accessed March 26, 2013. http://www.rogerblench.info.

Roger, Blench. “The Status of the Languages of Central Nigeria.” Last modified 2013. Accessed March 26, 2013. http://www.rogerblench.info.

Please cite the following paper when using this dataset:

N. Thakur, “Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis”, Proceedings of the 7th International Conference on Machine Learning and Natural Language Processing (MLNLP 2024), Chengdu, China, October 18-20, 2024 (Paper accepted for publication, Preprint available at: https://arxiv.org/abs/2410.03293)

Abstract

The outbreak of COVID-19 served as a catalyst for content creation and dissemination on social media platforms, as such platforms serve as virtual communities where people can connect and communicate with one another seamlessly. While there have been several works related to the mining and analysis of COVID-19-related posts on social media platforms such as Twitter (or X), YouTube, Facebook, and TikTok, there is still limited research that focuses on the public discourse on Instagram in this context. Furthermore, the prior works in this field have only focused on the development and analysis of datasets of Instagram posts published during the first few months of the outbreak. The work presented in this paper aims to address this research gap and presents a novel multilingual dataset of 500,153 Instagram posts about COVID-19 published between January 2020 and September 2024. This dataset contains Instagram posts in 161 different languages. After the development of this dataset, multilingual sentiment analysis was performed using VADER and twitter-xlm-roberta-base-sentiment. This process involved classifying each post as positive, negative, or neutral. The results of sentiment analysis are presented as a separate attribute in this dataset.

For each of these posts, the Post ID, Post Description, Date of publication, language code, full version of the language, and sentiment label are presented as separate attributes in the dataset.

The Instagram posts in this dataset are present in 161 different languages out of which the top 10 languages in terms of frequency are English (343041 posts), Spanish (30220 posts), Hindi (15832 posts), Portuguese (15779 posts), Indonesian (11491 posts), Tamil (9592 posts), Arabic (9416 posts), German (7822 posts), Italian (5162 posts), Turkish (4632 posts)

There are 535,021 distinct hashtags in this dataset with the top 10 hashtags in terms of frequency being #covid19 (169865 posts), #covid (132485 posts), #coronavirus (117518 posts), #covid_19 (104069 posts), #covidtesting (95095 posts), #coronavirusupdates (75439 posts), #corona (39416 posts), #healthcare (38975 posts), #staysafe (36740 posts), #coronavirusoutbreak (34567 posts)

The following is a description of the attributes present in this dataset

• Post ID: Unique ID of each Instagram post
• Post Description: Complete description of each post in the language in which it was originally published
• Date: Date of publication in MM/DD/YYYY format
• Language code: Language code (for example: “en”) that represents the language of the post as detected using the Google Translate API
• Full Language: Full form of the language (for example: “English”) that represents the language of the post as detected using the Google Translate API
• Sentiment: Results of sentiment analysis (using the preprocessed version of each post) where each post was classified as positive, negative, or neutral

Open Research Questions

This dataset is expected to be helpful for the investigation of the following research questions and even beyond:

1. How does sentiment toward COVID-19 vary across different languages?
2. How has public sentiment toward COVID-19 evolved from 2020 to the present?
3. How do cultural differences affect social media discourse about COVID-19 across various languages?
4. How has COVID-19 impacted mental health, as reflected in social media posts across different languages?
5. How effective were public health campaigns in shifting public sentiment in different languages?
6. What patterns of vaccine hesitancy or support are present in different languages?
7. How did geopolitical events influence public sentiment about COVID-19 in multilingual social media discourse?
8. What role does social media discourse play in shaping public behavior toward COVID-19 in different linguistic communities?
9. How does the sentiment of minority or underrepresented languages compare to that of major world languages regarding COVID-19?
10. What insights can be gained by comparing the sentiment of COVID-19 posts in widely spoken languages (e.g., English, Spanish) to those in less common languages?

All the Instagram posts that were collected during this data mining process to develop this dataset were publicly available on Instagram and did not require a user to log in to Instagram to view the same (at the time of writing this paper).

Abstract. English has long been set as the “universal language.” Basically most, if not all countries in the world know how to speak English or at least try to use it in their everyday communications for the purpose of globalizing. This study is designed to help the students learn from one or all of the four most commonly used foreign languages in the field of Information Technology namely; Korean, Mandarin Chinese, Japanese, and Spanish. Composed of a set of words, phrases, and sentences, the program is intended to quickly teach the students in the form of basic, intermediate, and advanced levels. This study has used the Agile model in system development. Functionality, reliability, usability, efficiency, and portability were also considered in determining the level of the system’s acceptability in terms of ISO 25010:2011. This interactive foreign language trainer is built to associate fun with learning, to remedy the lack of perseverance by some in learning a new language, and to make learning the user’s favorite playtime activity. The study allows the user to interact with the program which provides support for their learning. Moreover, this study reveals that integrating feedback modalities in the training and assessment modules of the software strengthens and enhances the memory in learning the language.

Keywords: Feedback, Assessment, Learning Modalities, Language Trainer, Interactive Technology

The GlobalPhone corpus developed in collaboration with the Karlsruhe Institute of Technology (KIT) was designed to provide read speech data for the development and evaluation of large continuous speech recognition systems in the most widespread languages of the world, and to provide a uniform, multilingual speech and text database for language independent and language adaptive speech recognition as well as for language identification tasks.

The entire GlobalPhone corpus enables the acquisition of acoustic-phonetic knowledge of the following 22 spoken languages: Arabic (ELRA-S0192), Bulgarian (ELRA-S0319), Chinese-Mandarin (ELRA-S0193), Chinese-Shanghai (ELRA-S0194), Croatian (ELRA-S0195), Czech (ELRA-S0196), French (ELRA-S0197), German (ELRA-S0198), Hausa (ELRA-S0347), Japanese (ELRA-S0199), Korean (ELRA-S0200), Polish (ELRA-S0320), Portuguese (Brazilian) (ELRA-S0201), Russian (ELRA-S0202), Spanish (Latin America) (ELRA-S0203), Swahili (ELRA-S0375), Swedish (ELRA-S0204), Tamil (ELRA-S0205), Thai (ELRA-S0321), Turkish (ELRA-S0206), Ukrainian (ELRA-S0377), and Vietnamese (ELRA-S0322).

In each language about 100 sentences were read from each of the 100 speakers. The read texts were selected from national newspapers available via Internet to provide a large vocabulary. The read articles cover national and international political news as well as economic news. The speech is available in 16bit, 16kHz mono quality, recorded with a close-speaking microphone (Sennheiser 440-6). The transcriptions are internally validated and supplemented by special markers for spontaneous effects like stuttering, false starts, and non-verbal effects like laughing and hesitations. Speaker information like age, gender, occupation, etc. as well as information about the recording setup complement the database. The entire GlobalPhone corpus contains over 450 hours of speech spoken by more than 2100 native adult speakers.

Data is shortened by means of the shorten program written by Tony Robinson. Alternatively, the data could be delivered unshorten.

The Turkish corpus was produced using the Zaman newspaper. It contains recordings of 100 speakers (28 males, 72 females) recorded in Istanbul, Turkey. The following age distribution has been obtained: 30 speakers are below 19, 30 speakers are between 20 and 29, 23 speakers are between 30 and 39, 14 speakers are between 40 and 49, and 3 speakers are over 50.

Pinyin Input Method Market Outlook

The Pinyin Input Method Market has been experiencing a significant trajectory in market size, with global figures estimated at $1.5 billion in 2023 and projected to reach approximately $2.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 7%. This robust growth can be attributed to several key factors, including the increasing digitalization across various sectors, the proliferation of smartphones, and the growing demand for efficient input methods that cater to Mandarin-speaking populations worldwide. The escalation of internet usage and the need for seamless communication in one of the most spoken languages globally is further propelling the market's upward trend.

One of the primary growth factors driving the Pinyin Input Method Market is the rapid digital transformation across industries. As businesses and educational institutions increasingly adopt digital platforms, there is a heightened need for effective input methods that can cater to Chinese-speaking users. The Pinyin input method, being one of the most efficient and widely used systems for Chinese character input, aligns perfectly with the needs of this growing user base. Additionally, the rise of e-learning platforms and remote work has necessitated reliable input methods, further contributing to market growth. The integration of Pinyin input across multiple devices and platforms, such as smartphones, tablets, and computers, has broadened its accessibility and usability, making it indispensable in the digital age.

Another significant growth factor is the increasing penetration of smartphones and mobile internet services. With Asia, particularly China, witnessing a surge in smartphone adoption, the demand for user-friendly and efficient input methods like Pinyin has soared. Mobile users require quick and intuitive typing solutions that can seamlessly integrate with their devices and applications. The Pinyin input method, with its ease of use and compatibility, perfectly meets these demands, thereby driving market expansion. Moreover, ongoing technological advancements in natural language processing and machine learning have enhanced the accuracy and predictive capabilities of Pinyin input systems, further boosting their adoption across diverse user segments.

The expansion of the Pinyin Input Method Market is also fueled by globalization and the growing significance of the Chinese language in international business, education, and cultural exchanges. As more non-native speakers seek to learn Mandarin for professional and personal reasons, the demand for effective learning tools, including Pinyin input methods, has surged. Educational institutions and language learning platforms are increasingly incorporating Pinyin input systems to facilitate the learning process and improve user engagement. This trend is expected to continue as the Chinese language gains prominence on the global stage, contributing to sustained market growth.

Regionally, Asia Pacific dominates the Pinyin Input Method Market due to the high concentration of Mandarin speakers and the widespread adoption of digital technologies. North America and Europe are also witnessing growth, driven by the increasing interest in Mandarin language learning and cross-cultural communications. In Latin America and the Middle East & Africa, the market is gradually expanding as more educational and business entities recognize the value of integrating Chinese language capabilities. The regional outlook highlights the global significance of the Pinyin input method in facilitating communication and bridging linguistic gaps in an increasingly interconnected world.

Product Type Analysis

The Pinyin Input Method Market can be segmented by product type into software and hardware. Software solutions dominate this market segment, primarily due to their versatility and wide applicability across various devices and platforms. These solutions can be easily installed and integrated into existing systems, making them a preferred choice for both individual users and organizations. Software-based Pinyin input methods offer extensive customization options, allowing users to tailor their typing experience to their preferences, which enhances user satisfaction and drives market growth. The continuous development of advanced features, such as predictive text and voice recognition, further elevates the value proposition of software solutions in this market.

On the other hand, hardware solutions, although a smaller segment, play a crucial role in specific applications. Dedicated Pinyin input hardware, such as keyboards