Automated Twitter data collecting tool and case study with rule-based analysis

Byun, Changhyun; Kim, Yanggon; Lee, Hyeoncheol; Kim, Kwangmi Ko

doi:10.1145/2428736.2428768

Cited by 10 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to overcome these data access problems, we developed the Twitter Data Collecting Tool in a previous research [7]. To collect Twitter data about Super Bowl 2014, the Twitter Data Collecting Tool is selected and used in this research.…”

Section: Data Collectionmentioning

confidence: 99%

“…However, it is impossible to collect enough data to apply data analysis techniques and filter unnecessary data, such as spam messages without an automated data collecting and filtering system. In order to overcome these data access problems, some of researchers shed light on collecting and storing data from Twitter [7][8][9][10]. This allows us, as well as other researchers, to build their own Twitter database.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Influence of Social Media: Twitter Usage Pattern during the 2014 Super Bowl Game

Shin¹,

Byun

Lee

2015

IJMUE

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Influence of Social Media: Twitter Usage Pattern during the 2014 Super Bowl Game

Shin¹,

Byun

Lee

2015

IJMUE

Self Cite

View full text Add to dashboard Cite

show abstract

“…Black et al [4] presented a methodological approach and a technology architecture, which examined Twitter as a transport protocol in different socio-technical contexts, in order to capture, transfer, and analyze the twitter interactions. Byun et al [5] developed a java-based data gathering tool with the design specifications to continuously and automatically collect social data from Twitter and filter noisy data, which can benefit the analysis of twitter messages, and further assist the detections of hot issues and topics and the discoveries of groups or communities. Wang et al [33] proposed a hashtag-based sentiment classification method for the topic sentiment analysis in Twitter, in which a graph model was introduced to deal with the hashtag-level information with three inference algorithms (loopy belief propagation, relaxation labeling and iterative classification algorithms) for classification.…”

Section: Social Media Application and Analysismentioning

confidence: 99%

A heuristic approach to discovering user correlations from organized social stream data

Zhou

Jin

2014

Multimed Tools Appl

View full text Add to dashboard Cite

Recently, with the widespread popularity of SNS (Social Network Service), such as Twitter, Facebook, people are increasingly accustomed to sharing feeling, experience and knowledge with each other on Internet. The high accessibility of these web sites has allowed the information to be spread across the social media more quickly and widely, which leads to more and more populations being engaged into this so-called social stream environment. All these make the organization of user relationships become increasingly important and necessary. In this study, we try to discover the potential and dynamical user correlations using those organized social streams in accordance with users' current interests and needs, in order to assist the collaborative information seeking process. We develop a heuristic approach to build a Dynamically Socialized User Networking (DSUN) model, and define a set of measures (such as interest degree, and popularity degree) and concepts (such as complementary tie, weak tie, and strong tie), to discover and represent users' current profiling and dynamical correlations. The corresponding algorithms are developed respectively. Finally, the architecture of the functional modules is presented, and the experiment results are demonstrated and discussed based on an application of the proposed model.

show abstract

“…Since this data has the potential of holding interesting hidden patterns, many researchers have attempted to aggregate and organize it, in order to try to find relationships, relevant changes and anomalies [1,2].…”

Section: Introductionmentioning

confidence: 99%

RetweetPatterns: Detection of Spatio-Temporal Patterns of Retweets

Rodrigues

Cunha

Ienco

et al. 2016

New Advances in Information Systems and Technologies

View full text Add to dashboard Cite

Abstract. Social media is strongly present in people's everyday life and Twitter is one example that stands out. The data within these types of services can be analyzed in order to discover useful knowledge. One interesting approach is to use data mining techniques to perceive hidden behaviours and patterns. The primary focus of this paper is the identification of patterns of retweets and to understand how information spreads over time in Twitter. The aim of this work lies in the adaptation of the GetMove tool, that is capable of extracting spatio-temporal pattern trajectories, and TweeProfiles, that identifies tweet profiles regarding several dimensions: spatial, temporal, social and content. We hope that the more flexible clustering strategy from TweeProfiles will enhance the results extracted by GetMove. We study the application of said mechanism to one case study and developed a visualization tool to interpret the results.

show abstract

Automated Twitter data collecting tool and case study with rule-based analysis

Cited by 10 publications

References 12 publications

The Influence of Social Media: Twitter Usage Pattern during the 2014 Super Bowl Game

The Influence of Social Media: Twitter Usage Pattern during the 2014 Super Bowl Game

A heuristic approach to discovering user correlations from organized social stream data

RetweetPatterns: Detection of Spatio-Temporal Patterns of Retweets

Contact Info

Product

Resources

About