소셜네트워크 분석과 클러스터 분석 방법을 활용한 스타트업 회사의 트위터 팔로워 네트워크에 대한 탐색적 연구 Exploring Twitter Follower-Networks of Startup Companies Employing Social Network Analysis and Cluster Analysis원문보기
기업의 소셜미디어 활용이 빠른 속도로 증가함에 따라 성공적인 소셜미디어 활용전략의 중요성이 커지고 있다. 이러한 중요성은 새로이 시장에 진입하여 신속하게 시장에서의 인지도를 확대하고 미래고객을 확보해야 할 필요성이 큰 스타트업 회사에게 더욱 절실하다고 할 수 있다. 본 연구의 목적은 스타트업 회사의 소셜미디어 활용의 특징을 보여주는 지표를 탐색적으로 조사, 분석하는데 두고 있다. 주요 지표는 전반적인 소셜미디어 관련 활동을 보여주는 지표와 소셜미디어 서비스을 통해 형성된 소셜네트워크 구조의 특성과 관련 지표를 포함한다. 스타트업 회사의 이러한 지표를 좀 더 객관적으로 평가하기 위하여 잘 갖춰진 기존 회사의 지표와 비교, 분석 하였다. 본 연구를 위해 여러 소셜미디어 서비스 중 트위터를 선정하고, 트위터 REST API를 통해 측정지표와 관련된 데이터와 팔로워네트워크(follower-network)에 대한 데이터를 수집하였다. 주요 분석방법으로 각 회사의 소셜네트워크 구조의 특성을 분석하기 위해 소셜네트워크분석기법이 활용되었으며, 클러스터분석 기법을 이용하여 스타트업 회사와 기존 회사의 측정지표를 비교, 분석하였다. 분석결과에 따르면 대부분의 측정지표에서 스타트업 회사와 기존 회사 간에 유의미한 차이를 보여주고 있다. 특징적인 분석결과의 하나로 스타트업 회사들이 상대적으로 많은 수의 인플루언서 (influencer)를 팔로워네트워크에 가지고 있다는 점이다. 또한, 스타트업 회사를 포함하는 클러스터의 네트워크 모듈성(modularity)과 추이성(transitivity)이 기존 회사에 비해 상대적으로 높은 것으로 나타났다. 이러한 결과는 스타트업 회사의 소셜네트워크 안에 기존 회사에 비해 내부결속력이 높은 상대적으로 많은 수의 커뮤니티가 존재한다는 점을 시사한다고 할 수 있다. 스타트업 회사의 이러한 특징은 잠재고객 및 비즈니스 파트너와의 효과적인 정보교환을 촉진할 수 있으며, 따라서 향후 일반적인 스타트업 회사의 소셜미디어 노력은 어떻게 인플루언서를 확보할 것인지, 또한 어떻게 내부결속력이 높은 긴밀한 네트워크를 구축할 것인지에 초점을 두어야 할 필요성이 있음을 시사하고 있다.
기업의 소셜미디어 활용이 빠른 속도로 증가함에 따라 성공적인 소셜미디어 활용전략의 중요성이 커지고 있다. 이러한 중요성은 새로이 시장에 진입하여 신속하게 시장에서의 인지도를 확대하고 미래고객을 확보해야 할 필요성이 큰 스타트업 회사에게 더욱 절실하다고 할 수 있다. 본 연구의 목적은 스타트업 회사의 소셜미디어 활용의 특징을 보여주는 지표를 탐색적으로 조사, 분석하는데 두고 있다. 주요 지표는 전반적인 소셜미디어 관련 활동을 보여주는 지표와 소셜미디어 서비스을 통해 형성된 소셜네트워크 구조의 특성과 관련 지표를 포함한다. 스타트업 회사의 이러한 지표를 좀 더 객관적으로 평가하기 위하여 잘 갖춰진 기존 회사의 지표와 비교, 분석 하였다. 본 연구를 위해 여러 소셜미디어 서비스 중 트위터를 선정하고, 트위터 REST API를 통해 측정지표와 관련된 데이터와 팔로워네트워크(follower-network)에 대한 데이터를 수집하였다. 주요 분석방법으로 각 회사의 소셜네트워크 구조의 특성을 분석하기 위해 소셜네트워크분석기법이 활용되었으며, 클러스터분석 기법을 이용하여 스타트업 회사와 기존 회사의 측정지표를 비교, 분석하였다. 분석결과에 따르면 대부분의 측정지표에서 스타트업 회사와 기존 회사 간에 유의미한 차이를 보여주고 있다. 특징적인 분석결과의 하나로 스타트업 회사들이 상대적으로 많은 수의 인플루언서 (influencer)를 팔로워네트워크에 가지고 있다는 점이다. 또한, 스타트업 회사를 포함하는 클러스터의 네트워크 모듈성(modularity)과 추이성(transitivity)이 기존 회사에 비해 상대적으로 높은 것으로 나타났다. 이러한 결과는 스타트업 회사의 소셜네트워크 안에 기존 회사에 비해 내부결속력이 높은 상대적으로 많은 수의 커뮤니티가 존재한다는 점을 시사한다고 할 수 있다. 스타트업 회사의 이러한 특징은 잠재고객 및 비즈니스 파트너와의 효과적인 정보교환을 촉진할 수 있으며, 따라서 향후 일반적인 스타트업 회사의 소셜미디어 노력은 어떻게 인플루언서를 확보할 것인지, 또한 어떻게 내부결속력이 높은 긴밀한 네트워크를 구축할 것인지에 초점을 두어야 할 필요성이 있음을 시사하고 있다.
The importance of business strategy for successful social media engagement has quickly increased as more businesses engage in social media. The importance is even greater for startup companies because startup companies are genuinely new to business, and they need to increase their presence in the ma...
The importance of business strategy for successful social media engagement has quickly increased as more businesses engage in social media. The importance is even greater for startup companies because startup companies are genuinely new to business, and they need to increase their presence in the market, and quickly access future customers. The objective of this paper lies in exploring key indicators of social media engagements by selected startup companies. The key indicators include two aspects of social media usages by the companies: i) overall social media activities, and ii) properties of network structure of the information flow platform provided by social media service. To better assess and evaluate the key indicators of social media usages by startup companies, the indicators will be compared with those of selected large established companies. Twitter is selected as a social media service for the analysis of this paper, and using Twitter REST API, data regarding the key indicators of overall Twitter activities and the Twitter follower-network of each company in the sample are collected. Then, the data are analyzed using social network analysis and hierarchical clustering analysis to examine the characteristics of the follower-network structures and to compare the characteristics between startup companies and established companies. The results show that most indicators are significantly different across startup companies and established companies. One key interesting finding is that the startup companies have proportionally more influencers in their follower-networks than the established companies have. Another interesting finding is that the follower-networks of startup companies in the sample have higher modularity and higher transitivity, suggesting that the startup companies tend to have a proportionally larger number of communities of users in their follower-networks, and the users in the networks are more tightly connected and cohesive internally. The key business implication for the future social media engagement efforts by startup companies in general is that startup companies may need to focus on getting more attention from influencers and promoting more cohesive communities in their follower-networks to appreciate the potential benefits of social media in the early stage of business of startup companies.
The importance of business strategy for successful social media engagement has quickly increased as more businesses engage in social media. The importance is even greater for startup companies because startup companies are genuinely new to business, and they need to increase their presence in the market, and quickly access future customers. The objective of this paper lies in exploring key indicators of social media engagements by selected startup companies. The key indicators include two aspects of social media usages by the companies: i) overall social media activities, and ii) properties of network structure of the information flow platform provided by social media service. To better assess and evaluate the key indicators of social media usages by startup companies, the indicators will be compared with those of selected large established companies. Twitter is selected as a social media service for the analysis of this paper, and using Twitter REST API, data regarding the key indicators of overall Twitter activities and the Twitter follower-network of each company in the sample are collected. Then, the data are analyzed using social network analysis and hierarchical clustering analysis to examine the characteristics of the follower-network structures and to compare the characteristics between startup companies and established companies. The results show that most indicators are significantly different across startup companies and established companies. One key interesting finding is that the startup companies have proportionally more influencers in their follower-networks than the established companies have. Another interesting finding is that the follower-networks of startup companies in the sample have higher modularity and higher transitivity, suggesting that the startup companies tend to have a proportionally larger number of communities of users in their follower-networks, and the users in the networks are more tightly connected and cohesive internally. The key business implication for the future social media engagement efforts by startup companies in general is that startup companies may need to focus on getting more attention from influencers and promoting more cohesive communities in their follower-networks to appreciate the potential benefits of social media in the early stage of business of startup companies.
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문제 정의
(2010) found that news dissemination on Twitter fell into tree-like broadcast patterns, and Himelboim & Han(2013) found that star-shaped clusters disappeared as soon as their core actors stopped tweeting about the topic, exhibiting the vulnerability of star-shaped network despite its higher level of degree centralization. In this paper, degree centralization, closeness centralization and eigenvector centralization metrics are examined to explore the centralization characteristics of Twitter-follower networks.
Then, the data are analyzed using social network analysis and hierarchical clustering analysis to examine the characteristics of structure of the follower-networks and to compare the characteristics between two sample groups: startup companies and established companies. The findings will help us to gain a better understanding and useful insights of the key aspects of social media engagements by startup companies. The findings are also expected to have valuable implications for developing viable social media engagement strategies for startup companies.
This study examined the key indicators of Twitter engagements by startup companies and compare the indicators with those of established companies to facilitate more sensible interpretation of the indicators. The key findings and implications of this study may help startup companies to make more effective and efficient social media engagement efforts. In addition, although this study is an exploratory study, the findings of this study may also provide a useful framework for more rigorous confirmatory studies investigating Twitter engagement efforts by startup companies in the future.
Examining Twitter-follower network in the perspective of social network analysis will help us understand the key social network metrics of Twitter-follower networks and identify the key characteristics of the Twitter follower-networks of startup companies especially in comparison to those of established companies. The major social network metrics examined in this paper are concerned about two key aspects of social networks, interconnectedness and centralization. The specific metrics are briefly explained below.
The objective of this paper lies in exploring key indicators of social media engagements by selected startup companies. The key indicators cover two aspects of social media usages by the companies: i) overall social media activities, and ii) properties of network structure of the information flow platform provided by social media service.
This study may contribute to both practice and research. This study examined the key indicators of Twitter engagements by startup companies and compare the indicators with those of established companies to facilitate more sensible interpretation of the indicators. The key findings and implications of this study may help startup companies to make more effective and efficient social media engagement efforts.
제안 방법
It is one of the popular unsupervised machine learning techniques, which is suitable for an exploratory data mining. Considering that the main objective of this study is to explore the key indicators of Twitter activities and metrics of Twitter follower-networks of startup companies and compare them with those of established companies as a reference group, cluster analysis seems to serve the objective quite well.
As stated previously, this paper examines the key Twitter activity indicators and the key metrics of social network structure of Twitter follower-networks. In particular, employing hierarchical cluster analysis method, further analysis will examine whether the two sample groups differ each other on the key network metrics by investigating if companies in one group are more similar to each other than companies in the other group. In the next section, a cluster analysis will be done to see if the startup companies and the established companies are categorized into different clusters, and the clusters will be further examined to see how the companies assigned to each cluster are different from each other on the key network metrics.
In particular, employing hierarchical cluster analysis method, further analysis will examine whether the two sample groups differ each other on the key network metrics by investigating if companies in one group are more similar to each other than companies in the other group. In the next section, a cluster analysis will be done to see if the startup companies and the established companies are categorized into different clusters, and the clusters will be further examined to see how the companies assigned to each cluster are different from each other on the key network metrics. In this section, as a preliminary analysis, sample means of the indicators and metrics of the two sample groups are simply compared to see if there exist any meaningful differences between the two groups before the companies are categorized into different clusters based on a cluster analysis.
The follower-network is selected because the number of followers is the most representative indicator of social media engagement, and therefore, follower-networks seem best suited for investigating how well social media plays its role as an effective and efficient platform for information flow. Then, the data are analyzed using social network analysis and hierarchical clustering analysis to examine the characteristics of structure of the follower-networks and to compare the characteristics between two sample groups: startup companies and established companies. The findings will help us to gain a better understanding and useful insights of the key aspects of social media engagements by startup companies.
대상 데이터
Data on the overall Twitter activities were collected for two sample groups: selected startup companies and selected established companies. 20 startup companies and another 20 Fortune 100 companies were selected from the lists provided by Sajid(2019) and Ranker(2019), respectively. The data collection was done through Twitter REST API using R packages, twitteR(Gentry, 2015) and rtweet(Kearney, 2019), in July 2019.
Data on the overall Twitter activities were collected for two sample groups: selected startup companies and selected established companies. 20 startup companies and another 20 Fortune 100 companies were selected from the lists provided by Sajid(2019) and Ranker(2019), respectively.
20 startup companies and another 20 Fortune 100 companies were selected from the lists provided by Sajid(2019) and Ranker(2019), respectively. The data collection was done through Twitter REST API using R packages, twitteR(Gentry, 2015) and rtweet(Kearney, 2019), in July 2019.
데이터처리
Given that the data is non-normal, testing for comparing sample means was conducted using Kruskal-Wallis rank sum test(Campbell & Swinscow, 2009).
Given that the data is non-normal, the sample means across two clusters were tested using Kruskal-Wallis rank sum test(Campbell & Swinscow, 2009).
성능/효과
The cluster 1 and 2 are mostly represented by startup companies and established companies, respectively. One key finding from the analysis is that the cluster 1 has higher average path length and diameter, suggesting that the companies assigned to the cluster 1 tend to have wider networks in their sampled follower-networks. The cluster 1 also has higher transitivity, eigenvector centralization and modularity, showing that the companies in the cluster 1 tend to have several clusters and have dense and tight connections between followers within clusters.
후속연구
The key findings and implications of this study may help startup companies to make more effective and efficient social media engagement efforts. In addition, although this study is an exploratory study, the findings of this study may also provide a useful framework for more rigorous confirmatory studies investigating Twitter engagement efforts by startup companies in the future.
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