• The Journal of the Korea Contents Association
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• v.13 no.5
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• pp.396-407
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• 2013

The role of influentials to maximize word-of-mouth effect can be seen to be very important. In this paper, we have the perspective of corporate marketing to understand Twitter influentials. We start from the point of view of who can induce eventually most exposure of tweets when he tweets the company's specific marketing messages. From this perspective, we observe both the follower influentials who have many followers and the retweet influentials who induce many retweets by visualizing graphs from network data collected via Twitter Search API. Although some users have small followers they may bring much more exposure than follower influentials if they can induce retweets by follower influentials. On the contrary, some retweet influentials who don't induce retweets by follower influentials may bring very little exposure. This suggests the fact that some small users who can induce retweets by influentials might have more important role than influentials themselves in order to increase the exposure of tweets. These users also are seen to have high centrality measures in the network structure.

• Journal of Digital Convergence
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• v.14 no.3
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• pp.165-170
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• 2016

The purpose of this study was to explore the differences in sentiment on social networking sites among six languages (English, German, Russian, Spanish, Turkish and Dutch). A total of 204 million tweets were collected using Streaming API. Subjective/objective ratio, sentiment strength, positive/negative ratio, number of retweets and boundary impermeability were analyzed with SentiStrength to estimate the trends of emotional expression via Twitter. The results showed that subjective/objective ratio and the positive/negative ratio of tweets were significantly different by languages (p<0.001). And, there were significant effects of language on sentiment strength, boundary impermeability and the number of retweets (p<0.001). The results also indicate that the cross-cultural, language differences should be taken into account in sentiment analysis on SNS.

• Journal of Digital Contents Society
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• v.14 no.2
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• pp.207-214
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• 2013

It was the 18th presidential election TV debate Twitter participation of SNS. Began to diverge as the era of social media, combined with SNS through in the mass media, media web 2.0. Search tweets, retweets, while the formation of policy issues, the agenda of Twitter users to listen to the statements of the candidates using the Internet or a smartphone. The highest number of tweets immediately issue statements were made. Content during the progressive tweets core keywords you do not often discussed, followed by the negative information increases the number of tweets has become a policy issue. Top retweets was to evaluate the process of debate, regardless of the issue. Tweeter complements the TV so Twitter has made public opinion. Smart phones and SNS Twitter, combined with the TV and the participation and direct democracy, voters vote one instrument was realized. Should forward approval ratings, real-time Twitter subtitles on the TV screen in TV debate Twitter influence in the election will be greatly expanded.

• Journal of Information Science Theory and Practice
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• v.7 no.4
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• pp.33-44
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• 2019

Altmetrics measure the frequency of references about an article on social media platforms, like Twitter. This paper studies a variety of factors that affect the popularity of articles (i.e., the number of article mentions) in the field of psychology on Twitter. Firstly, in this study, we classify Twitter users mentioning research articles as academic versus non-academic users and experts versus non-experts, using a machine learning approach. Then we build a negative binomial regression model with the number of Twitter mentions of an article as a dependant variable, and nine Twitter related factors (the number of followers, number of friends, number of status, number of lists, number of favourites, number of retweets, number of likes, ratio of academic users, and ratio of expert users) and seven article related factors (the number of authors, title length, abstract length, abstract readability, number of institutions, citation count, and availability of research funding) as independent variables. From our findings, if a research article is mentioned by Twitter users with a greater number of friends, status, favourites, and lists, by tweets with a large number of retweets and likes, and largely by Twitter users with academic and expertise knowledge on the field of psychology, the article gains more Twitter mentions. In addition, articles with a greater number of authors, title length, abstract length, and citation count, and articles with research funding get more attention from Twitter users.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.879-886
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• 2018

The basic search engine on Twitter shows not only tweets that contain search keywords, but also all articles written by users with nicknames containing search keywords. Since the tweets unrelated to the search keyword are exposed as search results, it is inconvenient to many users who want to search only tweets that include the keyword. To solve this inconvenience, this study improved the search function of Twitter by developing an algorithm that searches only tweets that contain search keywords. The improved functionality is implemented as a Web service using ASP.NET MVC5 and is available to many users. We used a powerful collection method in C# to retrieve the results of an object, and it was also possible to output them according to the number of 'retweets' or 'favorites'. If the number of retrieved numbers is less than a given number, we also added an exclusion filter function. Thus, sorting search results by the number of retweets or favorites, user can quickly search for opinions that are of interest to many users. It is expected that many users and data analysts will find the developed function convenient to search on Twitter.

• Asia pacific journal of information systems
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• v.26 no.1
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• pp.124-142
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• 2016

Companies operating in social network platforms continue to improve and expand their marketing techniques. This study examines the practice of "gating", which involves virtual barriers between social network users and company content. Gates demand mandatory user endorsements, in the form of a Facebook "Likes", Twitter "retweets" etc., to gain access to company content, such as coupons and rewards,. Gating practices demand a mandatory endorsement before any content consumption takes place. Thus, while user endorsements are assumed to arise voluntarily from trusted known sources, gating practices would appear to violate this assumption. However, whether this violation lessens the effectiveness of gating practices still requires empirical validation. We investigate this question through the use of a unique panel data set that includes data on "like" endorsements obtained from a number of real-world Facebook business pages. Results of the study show that gating practices are effective for endorsement solicitation; however, gates may interfere with more traditional marketing activities.

• Asia pacific journal of information systems
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• v.25 no.2
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• pp.239-264
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• 2015

This study aims to investigate the effect of message content and source user identity on information diffusion in Twitter networks. For the empirical study, we collected 11,346 tweets pertaining to the three major mobile telecom carriers in Korea for three months, from September to December 2011. These tweets generated 59,111 retweets (RTs) and were retweeted at least once. Our analysis indicates that information diffusion in Twitter in terms of RT volume is affected primarily by the type of message content, such as the inclusion of corporate social responsibility activities. However, the effect of message content on information diffusion is heterogeneous to the identity of the information source. We argue that user identity affects recipients' perception of the credibility of focal information. Our study offers insights into the information diffusion mechanism in online social networks and provides managerial implications on the strategic utilization of online social networks for marketing communications with customers.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.131-142
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• 2011

In recent years, Social Network Service, which is defined as a web-based service that allows an individual to construct a public or a semi-public profile within a bounded system, articulates a list of other users with whom they share connections, and traverses their list of connections. For example, Facebook and Twitter are the representative sites of Social Network Service, and these sites are the big issue in the world. A lot of people use Social Network Services to connect and maintain social relationship. Recently the users of Social Network Services have increased dramatically. Accordingly, many organizations become interested in Social Network Services as means of marketing, media, communication with their customers, and so on, because social network services can offer a variety of benefits to organizations such as companies and associations. In other words, organizations can use Social Network Services to respond rapidly to various user's behaviors because Social Network Services can make it possible to communicate between the users more easily and faster. And marketing cost of the Social Network Service is lower than that of existing tools such as broadcasts, news papers, and direct mails. In addition, Social network Services are growing in market place. So, the organizations such as companies and associations can acquire potential customers for the future. However, organizations uniformly communicate with users through Social Network Service without consideration of the characteristics of the networks although networks have different effects on information deliveries. For example, members' cohesion in an offline communication is higher than that in an online communication because the members of the offline communication are very close. that is, the network of the offline communication has a strong tie. Accordingly, information delivery is fast in the network of the offline communication. In this study, we compose two networks which have different characteristic of communication in Twitter. First network is constructed with data based on an offline communication such as friend, family, senior and junior in school. Second network is constructed with randomly selected data from users who want to associate with friends in online. Each network size is 250 people who divide with three groups. The first group is an ego which means a person in the center of the network. The second group is the ego's followers. The last group is composed of the ego's follower's followers. We compare the networks through social network analysis and follower's reaction analysis. We investigate density and centrality to analyze the characteristic of each network. And we analyze the follower's reactions such as replies and retweets to find differences of information delivery in each network. Our experiment results indicate that density and centrality of the offline communicationbased network are higher than those of the online-based network. Also the number of replies are larger than that of retweets in the offline communication-based network. On the other hand, the number of retweets are larger than that of replies in the online based network. We identified that the effect of information delivery in the offline communication-based network was different from those in the online communication-based network through experiments. So, you configure the appropriate network types considering the characteristics of the network if you want to use social network as an effective marketing tool.

• The Journal of Society for e-Business Studies
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• v.17 no.2
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• pp.73-85
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• 2012

Recently, as Social Network Services(SNS), such as Twitter, Facebook, are becoming more popular, much research has been doing actively. However, since SNS has been launched recently, related researches are also infant level. Especially, search engines serviced in web potals simply show the postings in order of upload time. Searching the postings in Twitter should be different from web search, which is based on traditional TF-IDF. In this paper, we present the new method of searching and ranking the interesting postings in Twitter. In proposed method, we utilize the frequency of retweets as a major factor for estimating the quality of postings. It can be an important criteria since users tend to retweet the valuable postings. Experimental results show that proposed method can be applied successfully in Twitter search system.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.195-201
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• 2024

With the Covid-19(Corona Virus) spread all around the world, people are using this propaganda and the desperate need of the citizens to know the news about this mysterious virus by spreading fake news. Some Countries arrested people who spread fake news about this, and others made them pay a fine. And since Social Media has become a significant source of news, .there is a profound need to detect these fake news. The main aim of this research is to develop a web-based model using a combination of machine learning algorithms to detect fake news. The proposed model includes an advanced framework to identify tweets with fake news using Context Analysis; We assumed that Natural Language Processing(NLP) wouldn't be enough alone to make context analysis as Tweets are usually short and do not follow even the most straightforward syntactic rules, so we used Tweets Features as several retweets, several likes and tweet-length we also added statistical credibility analysis for Twitter users. The proposed algorithms are tested on four different benchmark datasets. And Finally, to get the best accuracy, we combined two of the best algorithms used SVM ( which is widely accepted as baseline classifier, especially with binary classification problems ) and Naive Base.