• KIPS Transactions on Computer and Communication Systems
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• v.5 no.9
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• pp.229-236
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• 2016

In the past, crime prediction methods utilized previous records to accurately predict crime occurrences. Yet these crime prediction models had difficulty in updating immense data. To enhance the crime prediction methods, some approaches used social network service (SNS) data in crime prediction studies, but the relationship between SNS data and crime records has not been studied thoroughly. Hence, in this paper, we analyze the relationship between SNS data and criminal occurrences in the perspective of crime prediction. Using Latent Dirichlet Allocation (LDA), we extract tweets that included any words regarding criminal occurrences and analyze the changes in tweet frequency according to the crime records. We then calculate the number of tweets including crime related words and investigate accordingly depending on crime occurrences. Our experimental results demonstrate that there is a difference in crime related tweet occurrences when criminal activity occurs. Moreover, our results show that SNS data analysis will be helpful in crime prediction model as there are certain patterns in tweet occurrences before and after the crime.

• The Journal of the Korea Contents Association
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• v.13 no.8
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• pp.72-82
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• 2013

The present study explored types of Twitter-mediated communication and emotional responses of Twitter users toward celebrities. Three perspectives of para-social interactions, information hub, and fandom were proposed as communication types on Twitter. Celebrities were classified by entertainer, politician, specialist, and blogger. Communication patterns according to each category of celebrities were analyzed. The patterns of emotional responses, which represents the use of emoticons and emotional expressions were also analyzed. The results show that the type of para-social interactions was frequently accepted for the interactions with politicians and specialists, while fandom style was salient for the entertainers. For the power bloggers, the users tend to adopt the type of information hub interaction. The use of emotions and emotional expressions were most frequent in case of fandom style communication and the messages to the entertainers. Implications were further discussed.

• Information and Communications Magazine
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• v.34 no.6
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• pp.43-50
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• 2017

본고에서는 소셜 빅데이터에서 공공안전에 위협되고 사회적으로 이슈가 되는 재난사건을 추출하기 위한 방법으로 소셜 네트워크상에서 사용자 행동 분석과 시간분석을 반영한 토픽 모델링 기법을 알아본다. 소셜 사용자의 글 수, 리트윗 반응, 활동주기, 팔로워 수, 팔로잉 수 등 사용자의 행동 분석을 통하여 활동적이고 신뢰성 있는 사용자를 분류함으로써 트윗에서 스팸성과 광고성을 제외하고 이슈에 대해 신뢰성 높은 사용자가 쓴 트윗을 중요하게 반영한다. 또한, 트위터 데이터에서 새로운 이슈가 발생한 것을 탐지하기 위해 시간별 핵심어휘 빈도의 분포 변화를 측정하고, 이슈 트윗에 대해 감성 표현 분석을 통해 핵심이슈에 대해 사건 어휘를 추출한다. 소셜 빅데이터의 특성상 같은 날짜에 여러 이슈에 대한 트윗이 많이 생성될 수 있기 때문에, 트윗들을 토픽별로 그룹핑하는 것이 필요하므로, 최근 많이 사용되고 있는 LDA 토픽모델링 기법에 시간 특성과 사용자 특성을 분석한 시간상에서의 중요한 사건 어휘를 반영하고, 해당이슈에 대한 신뢰성 있는 사용자가 쓴 트윗을 중요시 반영하도록 토픽모델링 기법을 개선한 소셜 사건 탐지 방법에 대해 알아본다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1380-1386
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• 2015

Recently, the importance of users' geographic location information has been highlighted with a rapid increase of online social network services. In this paper, by utilizing geo-tagged tweets that provides high-precision location information of users, we first identify both Twitter users' exact location and the corresponding timestamp when the tweet was sent. Then, we analyze a relationship between the tweet frequency and the average user velocity. Specifically, we introduce a tweet-frequency computing algorithm, and show analysis results by country and by city. As a main result, it is shown that the tweet frequency according to user velocity follows a power-law distribution (i.e., Zipf' distribution or a Pareto distribution). In addition, by performing a comparison between the United States and Japan, one can see that the exponent of the distribution in Japan is smaller than that in the United States.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.731-738
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• 2013

We analyze public opinion via a sentiment analysis of tweets collected by using recent topic keywords extracted from newspaper articles. Newspaper articles collected within a certain period of time are clustered by using K-means algorithm and topic keywords for each cluster are extracted by using term frequency. A sentiment analyzer learned by a machine learning method can classify tweets according to their polarity values. We have an assumption that tweets collected by using these topic keywords deal with the same topics as the newspaper articles mentioned if the tweets and the newspapers are generated around the same time. and we tried to verify the validity of this assumption.

• Spatial Information Research
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• v.23 no.2
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• pp.69-81
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• 2015

If one can infer the residential area of SNS users by analyzing the SNS big data, it can be an alternative by replacing the spatial big data researches which result from the location sparsity and ecological error. In this study, we developed the way of utilizing the daily life activity pattern, which can be found from timeline data of tweet users, to infer the residential areas of tweet users. We recognized the daily life activity pattern of tweet users from user's movement pattern and the regional cognition words that users text in tweet. The models based on user's movement and text are named as the daily movement pattern model and the daily activity field model, respectively. And then we selected the variables which are going to be utilized in each model. We defined the dependent variables as 0, if the residential areas that users tweet mainly are their home location(HL) and as 1, vice versa. According to our results, performed by the discriminant analysis, the hit ratio of the two models was 67.5%, 57.5% respectively. We tested both models by using the timeline data of the stress-related tweets. As a result, we inferred the residential areas of 5,301 users out of 48,235 users and could obtain 9,606 stress-related tweets with residential area. The results shows about 44 times increase by comparing to the geo-tagged tweets counts. We think that the methodology we have used in this study can be used not only to secure more location data in the study of SNS big data, but also to link the SNS big data with regional statistics in order to analyze the regional phenomenon.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.163-176
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• 2014

Social media is becoming the platform for users to communicate their activities, status, emotions, and experiences to other people. In recent years, microblogs, such as Twitter, have gained in popularity because of its ease of use, speed, and reach. Compared to a conventional web blog, a microblog lowers users' efforts and investment for content generation by recommending shorter posts. There has been a lot research into capturing the social phenomena and analyzing the chatter of microblogs. However, measuring television ratings has been given little attention so far. Currently, the most common method to measure TV ratings uses an electronic metering device installed in a small number of sampled households. Microblogs allow users to post short messages, share daily updates, and conveniently keep in touch. In a similar way, microblog users are interacting with each other while watching television or movies, or visiting a new place. In order to measure TV ratings, some features are significant during certain hours of the day, or days of the week, whereas these same features are meaningless during other time periods. Thus, the importance of features can change during the day, and a model capturing the time sensitive relevance is required to estimate TV ratings. Therefore, modeling time-related characteristics of features should be a key when measuring the TV ratings through microblogs. We show that capturing time-dependency of features in measuring TV ratings is vitally necessary for improving their accuracy. To explore the relationship between the content of microblogs and TV ratings, we collected Twitter data using the Get Search component of the Twitter REST API from January 2013 to October 2013. There are about 300 thousand posts in our data set for the experiment. After excluding data such as adverting or promoted tweets, we selected 149 thousand tweets for analysis. The number of tweets reaches its maximum level on the broadcasting day and increases rapidly around the broadcasting time. This result is stems from the characteristics of the public channel, which broadcasts the program at the predetermined time. From our analysis, we find that count-based features such as the number of tweets or retweets have a low correlation with TV ratings. This result implies that a simple tweet rate does not reflect the satisfaction or response to the TV programs. Content-based features extracted from the content of tweets have a relatively high correlation with TV ratings. Further, some emoticons or newly coined words that are not tagged in the morpheme extraction process have a strong relationship with TV ratings. We find that there is a time-dependency in the correlation of features between the before and after broadcasting time. Since the TV program is broadcast at the predetermined time regularly, users post tweets expressing their expectation for the program or disappointment over not being able to watch the program. The highly correlated features before the broadcast are different from the features after broadcasting. This result explains that the relevance of words with TV programs can change according to the time of the tweets. Among the 336 words that fulfill the minimum requirements for candidate features, 145 words have the highest correlation before the broadcasting time, whereas 68 words reach the highest correlation after broadcasting. Interestingly, some words that express the impossibility of watching the program show a high relevance, despite containing a negative meaning. Understanding the time-dependency of features can be helpful in improving the accuracy of TV ratings measurement. This research contributes a basis to estimate the response to or satisfaction with the broadcasted programs using the time dependency of words in Twitter chatter. More research is needed to refine the methodology for predicting or measuring TV ratings.

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

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.91-94
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• 2011

The LBSNS(Location-Based Social Networking Service) service has been well-received by researchers and end-users, such as Twitter. Location-Based service of Twitter is now structured that users could not subscribe the information of their interesting local area. Those who being following from someone tweet message included information of local area to them just for their own interesting. However, follower may receive that kind of tweet. In order to handle the problem, we propose filtering technique using spatial join. The first work for filtering technique is to add a location information to tweets and users. In this paper, location information is represented by MBR(Minimum Bounding Rectangle). Location information is divided into dynamic property and static property. Suppose that users are continuously moving, that means one of the dynamic property's example. At this time, a massive continous query could cause the problem in server. In this paper, we create Virtual Grid on Google Map for reducing frequency of query, and conclude that it is useful for server.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.881-884
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• 2017

스마트 디바이스가 대중화 되면서 각종 사건 사고에 대한 데이터가 SNS 상에 실시간으로 업데이트 된다. SNS의 이런 특성을 이용하여 이용자 개개인이 사고감지센서의 역할을 하면 빠른 사고감지가 가능하다. 하지만 기존 연구들은 단순히 키워드의 출현 빈도로 사고를 판단하는 방식과, 문법파괴 요소가 많은 트위터의 특성으로 인해 정확성에서 한계를 보인다. 본 연구에서는 사고감지의 정확도를 높이기 위해 형태소로 분석한 트윗을 벡터화하여 다층퍼셉트론신경망으로 학습시키는 모델을 구현하였다. 연구 결과 일반명사로 이루어진 40개의 단어를 사용했을 때 가장 높은 82.58%의 정확도를 얻었다.