• Korean journal of communication and information
• /
• v.59
• /
• pp.138-161
• /
• 2012

This study is designed to explore the function of Twitter as a campaign platform during election campaign. For exploring the function of Twitter the form of tweet, the type of information on tweet and the way of opinion expression via Twitter were discussed by content analysis. This study finds, first, that, netizens express their opoinion of candidates without foundation and with emotional reactions. Second, they showed somewhat conflictive reactions according to their supporting candidates. This study conceptualized various kinds of public as 'blindly support public,' and 'blindly opposition public' in case of Park's supporters, 'rational support public,' and 'critical opposition public' in case of Na's supporters. Third, Park's supporters debated Na candidate's attitude of debate and her appearance blindly without foundation. Na's supporters argued Park's attitude of debate and his ignorance of Seoul Metropolitan government's policy blindly without foundation. Finally, this study discussed the relationship between the political discourse according to netizens' supporting via Twitter and the results of election. Park whose supporters attacked the opposing candidate by blaming her appearance and her attitude of debate won the election. Na didn't overcome her negative images. For her Twitter functioned as a media which is spreading negative factors about her. In conclusion, Twitter as a campaign platform during election times plays a key role in discussing candidates. However, netizens need to express their opinions with foundation and the candidates have to consider negative issue management. This study highlights the importance of peripheral factors which have a decisive effect on the results of election. The results of this study is useful for building political campaign strategy by candidates.

• International Journal of Computer Science & Network Security
• /
• v.23 no.10
• /
• pp.97-106
• /
• 2023

Sentiment analysis using social network platforms such as Twitter has achieved tremendous results. Twitter is an online social networking site that contains a rich amount of data. The platform is known as an information channel corresponding to different sites and categories. Tweets are most often publicly accessible with very few limitations and security options available. Twitter also has powerful tools to enhance the utility of Twitter and a powerful search system to make publicly accessible the recently posted tweets by keyword. As popular social media, Twitter has the potential for interconnectivity of information, reviews, updates, and all of which is important to engage the targeted population. In this work, numerous methods that perform a classification of tweet sentiment in Twitter is discussed. There has been a lot of work in the field of sentiment analysis of Twitter data. This study provides a comprehensive analysis of the most standard and widely applicable techniques for opinion mining that are based on machine learning and lexicon-based along with their metrics. The proposed work is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous, and polarized positive, negative or neutral. In order to validate the performance of the proposed framework, an extensive series of experiments has been performed on the real world twitter dataset that alter to show the effectiveness of the proposed framework. This research effort also highlighted the recent challenges in the field of sentiment analysis along with the future scope of the proposed work.

• Journal of Information Science Theory and Practice
• /
• v.5 no.3
• /
• pp.48-60
• /
• 2017

The purpose of this paper is to understand the profiles of users and their motivations in sharing research articles on Twitter. The goal is to contribute to the understanding of Twitter as a new altmetric measure for assessing impact of research articles. In this paper, we extended the previous study of tweet motivations by finding out the profiles of twitter users. In particular, we examined six characteristics of users: gender, geographic distribution, academic, non-academic, individual, and organization. Out of several, we would like to highlight here three key findings. First, a great majority of users (86%) were from North America and Europe indicating the possibility that, if in general, tweets for research articles are mainly in English, Twitter as an alternative metric has a Western bias. Second, several previous altmetrics studies suggested that tweets, and altmetrics in general, do not indicate scholarly impact due to their low correlation with citation counts. This study provides further details in this aspect by revealing that most tweets (77%) were by individual users, 67% of whom were nonacademic. Therefore, tweets mostly reflect impact of research articles on the general public, rather than on academia. Finally, analysis from profiles and motivations showed that the majority of tweets (from 42% to 57%) in all user types highlighted the summary or findings of the article indicating that tweets are a new way of communicating research findings.

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.8
• /
• pp.377-384
• /
• 2016

Our goal is to build the system which collects tweets from Twitter, analyzes the sentiment of each tweet, and helps users build a sentiment tagged corpus semi-automatically. After collecting tweets with the Twitter API, we analyzes the sentiments of them with a sentiment dictionary. With the proposed system, users can verify the results of the system and can insert new sentimental words or dependency relations where sentiment information exist. Sentiment information is tagged with the JSON structure which is useful for building or accessing the corpus. With a test set, the system shows about 76% on the accuracy in analysing the sentiments of sentences as positive, neutral, or negative.

• Journal of Information Processing Systems
• /
• v.13 no.4
• /
• pp.987-999
• /
• 2017

Extraction of influential people from their respective domains has attained the attention of scholastic community during current epoch. This study introduces an innovative interaction strength metric for retrieval of the most influential users in the online social network. The interactive strength is measured by three factors, namely re-tweet strength, commencing intensity and mentioning density. In this article, we design a novel algorithm called IPRank that considers the communications from perspectives of followers and followees in order to mine and rank the most influential people based on proposed interaction strength metric. We conducted extensive experiments to evaluate the strength and rank of each user in the micro-blog network. The comparative analysis validates that IPRank discovered high ranked people in terms of interaction strength. While the prior algorithm placed some low influenced people at high rank. The proposed model uncovers influential people due to inclusion of a novel interaction strength metric that improves results significantly in contrast with prior algorithm.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2014.11a
• /
• pp.737-740
• /
• 2014

최근 SNS 사용자가 급증하면서 매우 다양하고 방대한 양의 글이 여러 종류의 SNS를 통해 생성되고 있다. 그중 트위터는 정보의 전달 및 확산에 상당히 유용한 도구로 사용되고 있다. 이러한 트위터의 사용자 트윗은 뉴스, 음악, 사진, 여행 등 다양한 형태로 등장한다. 또한 트위터는 해시태그라는 사용자 정의 태그를 사용하는데 이는 트윗의 키워드 및 핵심을 쉽게 표현할 수 있도록 해주는 효과적인 수단이다. 최근 상당히 많은 양의 트윗의 생성에도 불구하고 이를 다양한 카테고리별로 분류할 수 있는 연구가 많이 진행되지 않았다. 따라서 본 논문에서는 해시태그를 이용해 트윗의 핵심을 파악하고 수많은 트윗을 다양한 토픽별로 분류할 수 있는 기법을 제안한다. 우선 다양한 카테고리의 인기 해시태그가 포함된 트윗을 수집하고 수집한 트윗에서 해시태그별 키워드를 추출한다. 그리고 코사인 유사도를 통해 해시태그별 내용 유사도를 파악하여 각 카테고리 내의 해시태그가 얼마나 유사한 내용을 지니고 있는지 파악한다. 마지막으로 사용자 트윗이 입력되면 모든 카테고리와 유사도를 비교하여 가장 유사도가 높은 카테고리를 찾아 추천해준다. 제안된 기법을 바탕으로 프로토타입을 구현하고 실험을 통해 성능을 평가한다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2018.05a
• /
• pp.267-270
• /
• 2018

구제역으로 인하여 국내 축산업계 및 관련 산업분야는 매년 막대한 피해를 입고 있다. 구제역과 관련한 다양한 학술적 연구들이 현재 진행되고는 있으나, 구제역의 발병에 따른 사회적 파급효과에 관한 공학적 분석 연구는 매우 제한적이다. 본 연구에서는 구제역에 관한 일반 시민들의 감성적 반응을 텍스트 마이닝 방법론을 사용하여 분석하는 체계적인 방법론을 제안한다. 제안하는 시스템은 먼저, 트위터에 게시된 트윗 중 구제역과 관련된 데이터를 수집한 후, 감성사전을 기반으로 극성탐지 과정을 거친다. 둘째, 토픽 모델링의 대표적인 기법 중 하나인 LDA를 활용하여 트윗으로 부터 키워드들을 추출하고, 추출된 키워드들로부터 극성별 동시출현 키워드 네트워크를 구성한다. 셋째, 키워드 네트워크을 통해 각 구간별 구제역의 사회적 파급효과를 분석한다. 사례 분석으로써, 2010년 7월부터 2011년 12월까지 국내에서 발생한 구제역에 관한 일반 시민들의 감성적 변화를 분석하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.12
• /
• pp.3868-3888
• /
• 2022

A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.

• The Journal of the Korea Contents Association
• /
• v.16 no.11
• /
• pp.477-487
• /
• 2016

In 1995, the filming location of the drama had been famous, and as a result it brings the effect of increasing tourists of that areas. After that, many local governments try to host the filming on their regions to be potential tourist attractions. With the same stream, Jeonju also has attempted to host International Film Festival and to set up Jeonju Film Commission and Jeonju Cinema Complex. However, although the city already has rich infrastructure facilities to make films, the city hardly tries to use the filming locations as tourist attractions. This study suggests four ways of using filming locations as tourist attractions to activate Jeonju economy and improve Jeonju's cultural image. We firstly collect social bigdata related with tourists of filming locations and tourist attractions in Jeonju from Twitter, which is the most representative SNS, and then perform frequency and trend analysis. We also investigate major factors of visits to tourist's attractions based on content analysis of tweet mentions.

• Journal of Korean Library and Information Science Society
• /
• v.46 no.1
• /
• pp.115-133
• /
• 2015

This study investigates the contents of twitters serviced by metropolitans and citizens' questions to propose improvements. Using content analysis as a research method, this study recorded and analyzed all the tweets of six metropolitans (Seoul, Busan, Daegu, Incheon, Daejeon, Gwangju) for three months. As the results, the frequency analysis of tweets revealed that Busan posted more tweets than other cities, and Seoul posted the highest number of tweet using URL link. The results of content analysis showed that the most frequently provided information from tweeters was about convenience of citizens living. Tweets using URL link were focused on information about citizen living, prize contest, and service announcement. Citizens had a request for information about their life and traffic. For public information service using tweeter in the future, this study provided several important suggestions.