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Detecting Implicitly Abusive Language by Applying Out-of-Distribution Problem
Jisu Shin, Hoyun Song, Jong C. Park
http://doi.org/10.5626/JOK.2022.49.11.948
Implicitly abusive language detection is a difficult problem to solve due to diversity of expressions and absence of a clear definition. Previous studies have claimed that implicitly abusive language should be classified and defined in detail, accompanied by corresponding datasets. However, this is not only inefficient, but also hard to flexibly respond to language changes. Our work proposes an efficient and effective method that processes implicitly abusive language as Out-of-Distribution data for the first time. In our experiments, a model with the proposed method performed better than a general pre-trained model and lexicon-based models. We also performed sentiment analysis and a case study to analyze characteristics of implicitly abusive language in detail and differences between a general pre-trained model and our model.
Location Information Sharing Objects (LISO): Context-aware Intelligence Service Using Social Media Data Based on User Interest
http://doi.org/10.5626/JOK.2020.47.9.814
By analyzing social media (SNS) data with learning algorithms that can be obtained from social networks, it is possible to extract the information of personal or social concerns. The learning algorithms have a problem, however, in that the higher accuracy from analysis requires larger volumes of information but thus increases the analysis duration. To resolve this issue effectively, we propose a system, termed Location Information Sharing Objects (LISO). The LISO has two types of objects. The LISO learns from big data by classifying the role of the objects for analyzing social concerns based on their type. The fixed-position objects manage collecting and analyzing a wide range of location-specific social media data for obtaining social concerns. The mobile objects manage analyzing the information regarding frequently varying situations as well as users’ personal concerns. This role-sharing method for analyzing big social media data based on the type of the objects in the LISO can distribute the load of analyzing.
A Model for Nowcasting Commodity Price based on Social Media Data
(Jaewoo Kim, Meeyoung Cha, Jong Gun Lee
http://doi.org/10.5626/JOK.2017.44.12.1258
Capturing real-time daily information on food prices is invaluable to help policymakers and development organizations address food security problems and improve public welfare. This study analyses the possible use of large-scale online data, available due to growing Internet connectivity in developing countries, to provide updates on food security landscape. We conduct a case study of Indonesia to develop a time-series prediction model that nowcasts daily food prices for four types of food commodities that are essential in the region: beef, chicken, onion and chilli. By using Twitter price quotes, we demonstrate the capability of social data to function as an affordable and efficient proxy for traditional offline price statistics.
User Reputation Management Method Based on Analysis of User Activities on Social Media
Jinkyung Yun, Jiwon Jeong, Suji Lee, Jongtae Lim, Kyungsoo Bok, Jaesoo Yoo
Recently, social network services have changed by moving towards an open platform where, as well as simply allowing the building of relationships among users, various types of information can be generated and shared. Since existing user reputation management methods evaluate user reliability based on user profiles, explicit relations, and evaluation, they are not suitable for determining user reliability on social media due to few explicit evaluation. In this paper, we analyze social activities on social media and propose a new user reputation management method that considers implicit evaluation as well as explicit evaluation. The proposed method derives positive and negative implicit evaluation from social activities, and generates user reputation information by field in order to consider user expertise. It also considers the number of users that participate in evaluation in order to measure user influence. As a result, it generates the reputation information of users who have no explicit evaluation and creates user reputation information that is more suitable for social media.
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