According to a survey conducted in South Korea in early 2024, around ** percent of respondents stated that MBC News was their most trusted news brand. MBC News overtook YTN, South Korea's first 24-hour news channel, as the most trusted news source in 2023. Low trust in news According to the annual survey by Reuters Institute for Study Journalism, South Korea's trust in the news has increased slightly since last year. With a trust rate ranging from ** to ** percent depending on the age group, South Koreans are rather skeptical compared to respondents in other countries. Trust in broadcast news tended to be higher compared to newspapers. Online news are increasingly popular In 2021, around ** percent of the South Korean population were reading the news. The majority of South Koreans stated that they accessed their news online, including on social media. However, only a minority of surveyed South Koreans were paying for digital news in 2025.

According to a survey conducted in South Korea in 2025, around ** percent of respondents stated that they use YouTube as a news source. This was followed by the South Korean messenger app KakaoTalk at ** percent. Relative low trust in news The usage rate of YouTube as a news source was especially high for South Koreans aged between 55 years and older. Meanwhile, around one in five South Koreans stated that they trusted news on social media. All in all, South Koreans were found to be rather skeptic of news in their country. A survey by Reuters Institute for the Study of Journalism found that the share of South Koreans who trust the news was relatively low. News consumption  According to the South Korean government, the share of news readers in South Korea fell from around **** percent in 2011 to around **** percent in 2021. The share was especially low for South Korean aged 60 years and above, and especially high for South Koreans in their thirties. Meanwhile, the share of online newspaper readers among news readers increased up to **** percent in 2021.

This study investigates the features of fake news networks and how they spread during the 2020 South Korean election. Using Actor-Network Theory (ANT), we assessed the network's central players and how they are connected. Results reveal the characteristics of the videoclips and channel networks responsible for the propagation of fake news. Analysis of the videoclip network reveals a high number of detected fake news videos and a high density of connections among users. Assessment of news videoclips on both actual and fake news networks reveals that the real news network is more concentrated. However, the scale of the network may play a role in these variations. Statistics for network centralization reveal that users are spread out over the network, pointing to its decentralized character. A closer look at the real and fake news networks inside videos and channels reveals similar trends. We find that the density of the real news videoclip network is higher than that of the fake news network, whereas the fake news channel networks are denser than their real news counterparts, which may indicate greater activity and interconnectedness in their transmission. We also found that fake news videoclips had more likes than real news videoclips, whereas real news videoclips had more dislikes than fake news videoclips. These findings strongly suggest that fake news videoclips are more accepted when people watch them on YouTube. In addition, we used semantic networks and automated content analysis to uncover common language patterns in fake news which helps us better understand the structure and dynamics of the networks involved in the dissemination of fake news. The findings reported here provide important insights on how fake news spread via social networks during the South Korean election of 2020. The results of this study have important implications for the campaign against fake news and ensuring factual coverage.

What’s Included

Welcome to the Korean Extraction Type Prompt-Response Dataset, a meticulously curated collection of 1500 prompt and response pairs. This dataset is a valuable resource for enhancing the data extraction abilities of Language Models (LMs), a critical aspect in advancing generative AI.

This extraction dataset comprises a diverse set of prompts and responses where the prompt contains input text, extraction instruction, constraints, and restrictions while completion contains the most accurate extraction data for the given prompt. Both these prompts and completions are available in Korean language.

These prompt and completion pairs cover a broad range of topics, including science, history, technology, geography, literature, current affairs, and more. Each prompt is accompanied by a response, providing valuable information and insights to enhance the language model training process. Both the prompt and response were manually curated by native Korean people, and references were taken from diverse sources like books, news articles, websites, and other reliable references.

This dataset encompasses various prompt types, including instruction type, continuation type, and in-context learning (zero-shot, few-shot) type. Additionally, you'll find prompts and responses containing rich text elements, such as tables, code, JSON, etc., all in proper markdown format.

To ensure diversity, this extraction dataset includes prompts with varying complexity levels, ranging from easy to medium and hard. Additionally, prompts are diverse in terms of length from short to medium and long, creating a comprehensive variety. The extraction dataset also contains prompts with constraints and persona restrictions, which makes it even more useful for LLM training.

To accommodate diverse learning experiences, our dataset incorporates different types of responses depending on the prompt. These formats include single-word, short phrase, single sentence, and paragraph type of response. These responses encompass text strings, numerical values, and date and time, enhancing the language model's ability to generate reliable, coherent, and contextually appropriate answers.

This fully labeled Korean Extraction Prompt Completion Dataset is available in JSON and CSV formats. It includes annotation details such as a unique ID, prompt, prompt type, prompt length, prompt complexity, domain, response, response type, and rich text presence.

Our dataset upholds the highest standards of quality and accuracy. Each prompt undergoes meticulous validation, and the corresponding responses are thoroughly verified. We prioritize inclusivity, ensuring that the dataset incorporates prompts and completions representing diverse perspectives and writing styles, maintaining an unbiased and discrimination-free stance.

The Korean version is grammatically accurate without any spelling or grammatical errors. No copyrighted, toxic, or harmful content is used during the construction of this dataset.

The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. Ongoing efforts are made to add more assets to this dataset, ensuring its growth and relevance. Additionally, FutureBeeAI offers the ability to gather custom extraction prompt and completion data tailored to specific needs, providing flexibility and customization options.

The dataset, created by FutureBeeAI, is now available for commercial use. Researchers, data scientists, and developers can leverage this fully labeled and ready-to-deploy Korean Extraction Prompt-Completion Dataset to enhance the data extraction abilities and accurate response generation capabilities of their generative AI models and explore new approaches to NLP tasks.

The Korean Open-Ended Question Answering Dataset is a meticulously curated collection of comprehensive Question-Answer pairs. It serves as a valuable resource for training Large Language Models (LLMs) and Question-answering models in the Korean language, advancing the field of artificial intelligence.

Dataset Content:

This QA dataset comprises a diverse set of open-ended questions paired with corresponding answers in Korean. There is no context paragraph given to choose an answer from, and each question is answered without any predefined context content. The questions cover a broad range of topics, including science, history, technology, geography, literature, current affairs, and more.

Each question is accompanied by an answer, providing valuable information and insights to enhance the language model training process. Both the questions and answers were manually curated by native Korean people, and references were taken from diverse sources like books, news articles, websites, and other reliable references.

This question-answer prompt completion dataset contains different types of prompts, including instruction type, continuation type, and in-context learning (zero-shot, few-shot) type. The dataset also contains questions and answers with different types of rich text, including tables, code, JSON, etc., with proper markdown.

Question Diversity:

To ensure diversity, this Q&A dataset includes questions with varying complexity levels, ranging from easy to medium and hard. Different types of questions, such as multiple-choice, direct, and true/false, are included. Additionally, questions are further classified into fact-based and opinion-based categories, creating a comprehensive variety. The QA dataset also contains the question with constraints and persona restrictions, which makes it even more useful for LLM training.

Answer Formats:

To accommodate varied learning experiences, the dataset incorporates different types of answer formats. These formats include single-word, short phrases, single sentences, and paragraph types of answers. The answer contains text strings, numerical values, date and time formats as well. Such diversity strengthens the Language model's ability to generate coherent and contextually appropriate answers.

Data Format and Annotation Details:

This fully labeled Korean Open Ended Question Answer Dataset is available in JSON and CSV formats. It includes annotation details such as id, language, domain, question_length, prompt_type, question_category, question_type, complexity, answer_type, rich_text.

Quality and Accuracy:

The dataset upholds the highest standards of quality and accuracy. Each question undergoes careful validation, and the corresponding answers are thoroughly verified. To prioritize inclusivity, the dataset incorporates questions and answers representing diverse perspectives and writing styles, ensuring it remains unbiased and avoids perpetuating discrimination.

Both the question and answers in Korean are grammatically accurate without any word or grammatical errors. No copyrighted, toxic, or harmful content is used while building this dataset.

Continuous Updates and Customization:

The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. Continuous efforts are made to add more assets to this dataset, ensuring its growth and relevance. Additionally, FutureBeeAI offers the ability to collect custom question-answer data tailored to specific needs, providing flexibility and customization options.

License:

The dataset, created by FutureBeeAI, is now ready for commercial use. Researchers, data scientists, and developers can utilize this fully labeled and ready-to-deploy Korean Open Ended Question Answer Dataset to enhance the language understanding capabilities of their generative ai models, improve response generation, and explore new approaches to NLP question-answering tasks.

We asked South Korean consumers about "Daily newspaper consumption by brand" and found that "Chosun Ilbo" takes the top spot, while "Segye Ilbo" is at the other end of the ranking.These results are based on a representative online survey conducted in 2024 among *** consumers in South Korea. Looking to gain valuable insights about print media consumers worldwide? Check out our

According to a survey conducted in South Korea in 2021, around **** percent of South Korean news readers aged 80 years and older stated that they read printed newspapers. Print newspapers are more popular among older age groups. Overall, only an average of **** percent of news readers read printed newspapers.

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).

Korean-language articles from North Korea's Korean Central News Agency (KCNA) website posted from 1 October 2013 to 31 January 2021. This represents all Korean articles we could find when the scrape was conducted in February 2021, just over 105,000 unique articles.

News Sentiment Index Trend data was reported at 104.830 Index in 02 May 2025. This records a decrease from the previous number of 106.560 Index for 30 Apr 2025. News Sentiment Index Trend data is updated daily, averaging 101.190 Index from Jan 2005 (Median) to 02 May 2025, with 7076 observations. The data reached an all-time high of 125.840 Index in 03 May 2021 and a record low of 59.790 Index in 15 Mar 2020. News Sentiment Index Trend data remains active status in CEIC and is reported by The Bank of Korea. The data is categorized under Global Database’s South Korea – Table KR.S013: News Sentiment Index.

Past studies of human rights discourse have centralized civil and political rights over other types of rights, and have thus yielded a biased understanding of how human rights have evolved. This scholarship has also neglected the role of the political slant of reporting agencies in framing topics and issues covering human rights. This study offers new theoretical and analytical solutions to such limits by analyzing a newly compiled data corpus comprising 101,689 South Korean newspaper articles—both right and left leaning—referencing the term “human rights”. The analysis of the data led us to reach three conclusions. First, South Korean media coverage demonstrates that human rights as a cultural symbol expanded tremendously in the country during the 1990s and 2000s. Second, there was an incredible degree of shift among multiple categories and topics of human rights during 26 years of that period. Initially, civil and political rights dominated but then gradually receded as economic, social, and cultural, and social minority rights moved to the forefront. Third, we found substantial variation in categories and topics of attention between conservative and progressive news sources; that is, progressive news sources allocated substantially more discussion to diverse categories and topics than conservative counterparts. These core findings lend support to imagery of a polarized embrace in which human rights are diffused yet in a highly polarized manner.

The USD/KRW exchange rate rose to 1,409.5500 on September 26, 2025, up 0.03% from the previous session. Over the past month, the South Korean Won has weakened 1.17%, and is down by 7.57% over the last 12 months. South Korean Won - values, historical data, forecasts and news - updated on September of 2025.

English-language articles from North Korea's Korean Central News Agency media outlet posted from 1 October 2008 to 31 December 2020. This represents all English articles we could find when the scrape was conducted in February 2021, just over 87,300 unique articles.

Although many Koreans’ attitudes towards Japan are marked by hostility and distrust due to the memories of Japanese colonization, others have different views on the past and different preferences for dealing with Japan. The current study examines the links between different construals of historical victimization (i.e., collective victim beliefs) and distinct intergroup strategies: collective action demanding apology and reparation from Japan, reconciliation, and retribution. A survey among 249 South Koreans identified four collective victim beliefs: preserving memories and supporting survivors, ingroup fragmentation about how to address the past, grievances concerning historical suffering and perpetrators’ denial, and perceived prevalence of ingroup resistance. Grievances concerning historical suffering and present-day denial predicted increased willingness to participate in collective action, desire for retribution, and decreased willingness for reconciliation. Perceived importance of preserving memories and supporting survivors predicted greater willingness to participate in collective action and more desire for retribution. Unexpectedly, perceived ingroup fragmentation and resistance also predicted willingness for reconciliation. Most of the effects of collective victim beliefs on intergroup strategies were mediated by resentment towards the Japanese. Overall, this study expands the literature on collective victimhood in the understudied context of Koreans’ collective memories of colonization. Dataset for: Jeong, H. Y., & Vollhardt, J. R. (2021). Koreans’ collective victim beliefs about Japanese colonization. Peace and Conflict: Journal of Peace Psychology, 27(4), 629–641. https://doi.org/10.1037/pac0000496 To understand the current relations between Japan and Korea, it is crucial to understand how Koreans construe the historical experience of victimization due to Japanese colonization. We analyzed news articles from major Korean newspapers, examining 3 time periods during which the conflict between Japan and South Korea over how to address the past was particularly salient. Thematic analysis revealed 3 categories with several themes each, describing challenges of victimization that are met with specific coping efforts and the transgenerational transmission of memories of historical victimization that keep these experiences salient. This article presents the first social psychological investigation of collective victim beliefs in Korea and contributes to the literature by integrating theoretical perspectives from appraisal theory and the collective victimhood literature, as well as expanding the diversity and complexity of collective victim beliefs. The current study also has practical implications for addressing strained relations between South Korea and Japan.

The Gross Domestic Product (GDP) in South Korea was worth 1712.79 billion US dollars in 2023, according to official data from the World Bank. The GDP value of South Korea represents 1.62 percent of the world economy. This dataset provides - South Korea GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in South Korea was last recorded at 2.50 percent. This dataset provides - South Korea Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The total population in South Korea was estimated at 51.8 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides - South Korea Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

What’s Included

Welcome to the Korean Closed Ended Classification Prompt-Response Dataset—an extensive collection of 3000 meticulously curated prompt and response pairs. This dataset is a valuable resource for training Language Models (LMs) to classify input text accurately, a crucial aspect in advancing generative AI.

This closed-ended classification dataset comprises a diverse set of prompts and responses where the prompt contains input text to be classified and may also contain task instruction, context, constraints, and restrictions while completion contains the best classification category as response. Both these prompts and completions are available in Korean language. As this is a closed-ended dataset, there will be options given to choose the right classification category as a part of the prompt.

These prompt and completion pairs cover a broad range of topics, including science, history, technology, geography, literature, current affairs, and more. Each prompt is accompanied by a response, providing valuable information and insights to enhance the language model training process. Both the prompt and response were manually curated by native Korean people, and references were taken from diverse sources like books, news articles, websites, and other reliable references.

This closed-ended classification prompt and completion dataset contains different types of prompts, including instruction type, continuation type, and in-context learning (zero-shot, few-shot) type. The dataset also contains prompts and responses with different types of rich text, including tables, code, JSON, etc., with proper markdown.

To ensure diversity, this closed-ended classification dataset includes prompts with varying complexity levels, ranging from easy to medium and hard. Different types of prompts, such as multiple-choice, direct, and true/false, are included. Additionally, prompts are diverse in terms of length from short to medium and long, creating a comprehensive variety. The classification dataset also contains prompts with constraints and persona restrictions, which makes it even more useful for LLM training.

To accommodate diverse learning experiences, our dataset incorporates different types of responses depending on the prompt. These formats include single-word, short phrase, and single sentence type of response. These responses encompass text strings, numerical values, and date and time formats, enhancing the language model's ability to generate reliable, coherent, and contextually appropriate answers.

This fully labeled Korean Closed Ended Classification Prompt Completion Dataset is available in JSON and CSV formats. It includes annotation details such as a unique ID, prompt, prompt type, prompt length, prompt complexity, domain, response, response type, and rich text presence.

Our dataset upholds the highest standards of quality and accuracy. Each prompt undergoes meticulous validation, and the corresponding responses are thoroughly verified. We prioritize inclusivity, ensuring that the dataset incorporates prompts and completions representing diverse perspectives and writing styles, maintaining an unbiased and discrimination-free stance.

The Korean version is grammatically accurate without any spelling or grammatical errors. No copyrighted, toxic, or harmful content is used during the construction of this dataset.

The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. Ongoing efforts are made to add more assets to this dataset, ensuring its growth and relevance. Additionally, FutureBeeAI offers the ability to gather custom closed-ended classification prompt and completion data tailored to specific needs, providing flexibility and customization options.

The dataset, created by FutureBeeAI, is now available for commercial use. Researchers, data scientists, and developers can leverage this fully labeled and ready-to-deploy Korean Closed Ended Classification Prompt-Completion Dataset to enhance the classification abilities and accurate response generation capabilities of their generative AI models and explore new approaches to NLP tasks.

What’s Included

Welcome to the Korean Chain of Thought prompt-response dataset, a meticulously curated collection containing 3000 comprehensive prompt and response pairs. This dataset is an invaluable resource for training Language Models (LMs) to generate well-reasoned answers and minimize inaccuracies. Its primary utility lies in enhancing LLMs' reasoning skills for solving arithmetic, common sense, symbolic reasoning, and complex problems.

This COT dataset comprises a diverse set of instructions and questions paired with corresponding answers and rationales in the Korean language. These prompts and completions cover a broad range of topics and questions, including mathematical concepts, common sense reasoning, complex problem-solving, scientific inquiries, puzzles, and more.

Each prompt is meticulously accompanied by a response and rationale, providing essential information and insights to enhance the language model training process. These prompts, completions, and rationales were manually curated by native Korean people, drawing references from various sources, including open-source datasets, news articles, websites, and other reliable references.

Our chain-of-thought prompt-completion dataset includes various prompt types, such as instructional prompts, continuations, and in-context learning (zero-shot, few-shot) prompts. Additionally, the dataset contains prompts and completions enriched with various forms of rich text, such as lists, tables, code snippets, JSON, and more, with proper markdown format.

To ensure a wide-ranging dataset, we have included prompts from a plethora of topics related to mathematics, common sense reasoning, and symbolic reasoning. These topics encompass arithmetic, percentages, ratios, geometry, analogies, spatial reasoning, temporal reasoning, logic puzzles, patterns, and sequences, among others.

These prompts vary in complexity, spanning easy, medium, and hard levels. Various question types are included, such as multiple-choice, direct queries, and true/false assessments.

To accommodate diverse learning experiences, our dataset incorporates different types of answers depending on the prompt and provides step-by-step rationales. The detailed rationale aids the language model in building reasoning process for complex questions.

These responses encompass text strings, numerical values, and date and time formats, enhancing the language model's ability to generate reliable, coherent, and contextually appropriate answers.

This fully labeled Korean Chain of Thought Prompt Completion Dataset is available in JSON and CSV formats. It includes annotation details such as a unique ID, prompt, prompt type, prompt complexity, prompt category, domain, response, rationale, response type, and rich text presence.

Our dataset upholds the highest standards of quality and accuracy. Each prompt undergoes meticulous validation, and the corresponding responses and rationales are thoroughly verified. We prioritize inclusivity, ensuring that the dataset incorporates prompts and completions representing diverse perspectives and writing styles, maintaining an unbiased and discrimination-free stance.

The Korean version is grammatically accurate without any spelling or grammatical errors. No copyrighted, toxic, or harmful content is used during the construction of this dataset.

The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. Ongoing efforts are made to add more assets to this dataset, ensuring its growth and relevance. Additionally, FutureBeeAI offers the ability to gather custom chain of thought prompt completion data tailored to specific needs, providing flexibility and customization options.

The dataset, created by FutureBeeAI, is now available for commercial use. Researchers, data scientists, and developers can leverage this fully labeled and ready-to-deploy Korean Chain of Thought Prompt Completion Dataset to enhance the rationale and accurate response generation capabilities of their generative AI models and explore new approaches to NLP tasks.

According to a survey among YouTube users in 2019, more than half of respondents stated that their favorite content on YouTube was music. The same survey found that fun was the leading reason behind using YouTube.

YouTube

In South Korea, the international platform YouTube seems unstoppable. In a survey in 2019, less than one percent of surveyed have not heard about YouTube at all. It was the leading video streaming app by far in 2018 and it is unlikely to change any time soon.

Influence of YouTube

YouTube is known for influencing users around the world in all kinds of topics. In South Korea, trust in YouTube as a source seems high. A survey in 2020 found that around ** percent of respondents aged 45 years and above were using YouTube as a news source, while around ** percent of respondents agreed that social media platforms are the best and most accurate sources of news and information in 2019. Nevertheless, South Korean culture has a big influence on YouTube as well, as popular contents originate there, such as K-pop as well as the viral video trend called Mukbang.