Korean Companies’ SNS Analytics Data provides important information to analyze consumer opinions and trends on social media. This data includes social media posts, comments, and likes. Collected from platforms such as Twitter, Facebook, and Instagram, it helps investors analyze consumer sentiment, evaluate brand awareness, and optimize marketing strategies, helping in the valuation of Korean companies based on their social media presence and consumer engagement.

This report investigates media developments and market concentration trends across nearly two dozen communication, internet and media industries that make up the South Korean network media economy. We start by studying each of the sectors covered individually before grouping them into three larger mid-range categories of similar types of services: telecoms and internet access sectors, online and traditional media services, and core internet sectors. Ultimately, we bring all these sectors together to give a composite view of the network media economy over the period from 2019 to 2021. In a country where there is little established research on the topic of media concentration and ownership, the goal of this report is to simultaneously examine specific media sectors so that we can understand their unique features, dynamics, and trends and move from there toward a holistic understanding of the South Korean network media economy.

Data and codes for reproducing the results in the paper "North Korean Refugees’ Media Use and Social Capital: A Focus on Trust, Network, and Adaptation."

This data set is a replication data for "Estimation of Media Slants in South Korean News Agencies Using News Reports on the Sewol Ferry Disaster." It contains two Rdata files, one bugs code, and one R code. The two Rdata files include phrase frequencies computed by preprocessed text document of news reports on the Sewol Ferry Disaster from 28 news agencies in South Korea. The R code implements main results of the analysis in the paper. The computation of the model is done by JAGS.

RISA-Korea provides insight into Korean retail investor sentiment and interest in 4200 KRX securities (with 3100+ KRX stocks) by analyzing 60 million posts and 80+ million replies in Naver, the most popular web portal in South Korea since 2017.

By analyzing the discussions on Naver's stock forum, RISA-Korea provides valuable information about the sentiments, opinions, and trends expressed by retail investors regarding various securities. The inclusion of a wide range of securities in the analysis ensures that RISA-Korea captures a holistic understanding of retail investor sentiment across the market, and the dataset serves as a valuable resource for studying retail investor behavior, identifying market trends, and assessing the impact of retail investors on specific securities.

In particular, in addition to the statistical analysis of each security, this dataset provides record-level post analysis, such as information on sentiment, related stocks and hotness. for each post, which allows users to group posts according to their needs, such as identifying popular posts or excluding machine posts, to gain in-depth insights.

• Coverage: 4200+ KRX securities (3100+stocks, 800+ETFs and 300+ ETNs) • History: From 2017-06-07 • Update Frequency: Daily

Please cite the following paper when using this dataset:

N. Thakur, “Mpox narrative on Instagram: A labeled multilingual dataset of Instagram posts on mpox for sentiment, hate speech, and anxiety analysis,” arXiv [cs.LG], 2024, URL: https://arxiv.org/abs/2409.05292

Abstract

The world is currently experiencing an outbreak of mpox, which has been declared a Public Health Emergency of International Concern by WHO. During recent virus outbreaks, social media platforms have played a crucial role in keeping the global population informed and updated regarding various aspects of the outbreaks. As a result, in the last few years, researchers from different disciplines have focused on the development of social media datasets focusing on different virus outbreaks. No prior work in this field has focused on the development of a dataset of Instagram posts about the mpox outbreak. The work presented in this paper (stated above) aims to address this research gap. It presents this multilingual dataset of 60,127 Instagram posts about mpox, published between July 23, 2022, and September 5, 2024. This dataset contains Instagram posts about mpox in 52 languages. For each of these posts, the Post ID, Post Description, Date of publication, language, and translated version of the post (translation to English was performed using the Google Translate API) are presented as separate attributes in the dataset.

After developing this dataset, sentiment analysis, hate speech detection, and anxiety or stress detection were also performed. This process included classifying each post into

one of the fine-grain sentiment classes, i.e., fear, surprise, joy, sadness, anger, disgust, or neutral,

hate or not hate

anxiety/stress detected or no anxiety/stress detected.

These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for sentiment, hate speech, and anxiety or stress detection, as well as for other applications.

The 52 distinct languages in which Instagram posts are present in the dataset are English, Portuguese, Indonesian, Spanish, Korean, French, Hindi, Finnish, Turkish, Italian, German, Tamil, Urdu, Thai, Arabic, Persian, Tagalog, Dutch, Catalan, Bengali, Marathi, Malayalam, Swahili, Afrikaans, Panjabi, Gujarati, Somali, Lithuanian, Norwegian, Estonian, Swedish, Telugu, Russian, Danish, Slovak, Japanese, Kannada, Polish, Vietnamese, Hebrew, Romanian, Nepali, Czech, Modern Greek, Albanian, Croatian, Slovenian, Bulgarian, Ukrainian, Welsh, Hungarian, and Latvian.

The following table represents the data description for this dataset

Attribute Name

Attribute Description

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

Language of the post as detected using the Google Translate API

Translated Post Description

Translated version of the post description. All posts which were not in English were translated into English using the Google Translate API. No language translation was performed for English posts.

Sentiment

Results of sentiment analysis (using translated Post Description) where each post was classified into one of the sentiment classes: fear, surprise, joy, sadness, anger, disgust, and neutral

Hate

Results of hate speech detection (using translated Post Description) where each post was classified as hate or not hate

Anxiety or Stress

Results of anxiety or stress detection (using translated Post Description) where each post was classified as stress/anxiety detected or no stress/anxiety detected.