In this paper, we propose a method of ranking recently created Twitter accounts according to their prospective popularity. Early detection of new promising accounts is useful for trend prediction, viral marketing, user recommendation, and so on. New accounts are, however, difficult to evaluate because they have not established their reputations, and we cannot apply existing link-based or other popularity-based account evaluation methods. Our method first finds "early adopters", i.e., users who often find new good information sources earlier than others. Our method then regards new accounts followed by good early adopters as promising, even if they do not have many followers now. In order to find good early adopters, we estimate the frequency of link propagation from each account, i.e., how many times the follow links from the account have been copied by its followers. If its followers have copied many of its follow links in the past, the account must be an early adopter, who find good information sources earlier than its followers. We develop a method of inferring which links are created by copying which links. One advantage of our method is that our method only uses information that can be easily obtained only by crawling neighbors of the target accounts in the current Twitter graph. We evaluated our method by an experiment on Twitter data. We chose then-new accounts from an old snapshot of Twitter, compute their ranking by our method, and compare it with the number of followers the accounts currently have. The result shows that our method produces better rankings than various baseline methods, especially for new accounts that have only a few followers.
There have also been many studies on estimation of the influential power of nodes in social network. For example, @cite_4 compared three indicators, PageRank, the number of followers, and the number of retweets, for the estimation of popularity of Twitter accounts, and they showed that there is a discrepancy between the number of followers of an account and the popularity of tweets by the account, which suggests that the number of followers is not an only major factor of influential power of nodes. @cite_15 also proposed a method for estimating influential power of Twitter accounts. Their method is based on the number of followers, but they also consider the interests of the followers and compute the probability that each tweet is actually read by the followers. These two studies focus on influential power of information sources in information dissemination, while the early adopter score used in our method indicates the influential power of nodes in link propagation.
@cite_15 @cite_4
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This paper focuses on the problem of identifying influential users of micro-blogging services. Twitter, one of the most notable micro-blogging services, employs a social-networking model called "following", in which each user can choose who she wants to "follow" to receive tweets from without requiring the latter to give permission first. In a dataset prepared for this study, it is observed that (1) 72.4 of the users in Twitter follow more than 80 of their followers, and (2) 80.5 of the users have 80 of users they are following follow them back. Our study reveals that the presence of "reciprocity" can be explained by phenomenon of homophily. Based on this finding, TwitterRank, an extension of PageRank algorithm, is proposed to measure the influence of users in Twitter. TwitterRank measures the influence taking both the topical similarity between users and the link structure into account. Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank. The new social media such as Twitter and Sina Weibo has become an increasingly popular channel for spreading influence, challenging traditional media such as TVs and newspapers. The most influential and verified users, also called big-V accounts on Sina Weibo often attract million of followers and fans, creating massive “celebrity-centric” social networks on the social media, which play a key role in disseminating breaking news, latest events, and controversial opinions on social issues. Given the importance of these accounts, it is very crucial to understand social networks and user influence of these accounts and profile their followers' behaviors. Towards this end, this paper monitors a selected group of influential users on Sina Weibo and collects their tweet streams as well as retweeting and commenting activities on these tweets from their followers. Our analysis on tweet data streams from Sina Weibo reveals when and what the followers comment on the tweets of these influential users, and discovers different temporal patterns and word diversity in the comments. Based on the insight gained from follower characteristics, we further develop simple and intuitive algorithms for classifying the followers into spammers and normal fans. Our experimental results demonstrate that the proposed algorithms are able to achieve an average accuracy of 95.20 in detecting spammers from the followers who have commented on the tweets of these influential accounts. Previous studies have established the link between one's actions (e.g., engaging with others vs. minding one's own business) and one's national culture (e.g., collectivist vs. individualistic), and such actions have been shown to be important as they are collectively affiliated with a country's economic outcomes (e.g., Gross Domestic Product). Hitherto there has not been any systematic study of whether one's action on Twitter (e.g., deciding when to post messages) is linked to one's culture (e.g., country's Pace of Life). To fix that, we build different network snapshots starting from 55,000 seed users on Twitter, and we do so for 10 weeks across 30 countries (after filtering those with low penetration rates) for a total of 2.34 M profiles. Based on Hofstede's theory of cultural dimensions and Levine's Pace of Life theory, we consider three behavioral patterns on Twitter (i.e., temporal predictability of tweets, engaging with others, and supporting others who are less popular) and associate them with three different dimensions derived from the two theories: Pace of Life, Individualism and Power Distance. We find the following strong correlations: activity predictability negatively correlates with Pace of Life (r=-0.62), tweets with mentions negatively correlates with Individualism (r = -0.55), and power (e.g, Twitter popularity) imbalance in relationships (between, for example, two users mentioning each other) is correlated with Power Distance (r=0.62). These three cultural dimensions matter because they are associated with a country's socio-economic aspects - with GDP per capita, income inequality, and education expenditure. Influential people have an important role in the process of information diffusion. However, there are several ways to be influential, for example, to be the most popular or the first that adopts a new idea. In this paper we present a methodology to find trendsetters in information networks according to a specific topic of interest. Trendsetters are people that adopt and spread new ideas influencing other people before these ideas become popular. At the same time, not all early adopters are trendsetters because only few of them have the ability of propagating their ideas by their social contacts through word-of-mouth. Differently from other influence measures, a trendsetter is not necessarily popular or famous, but the one whose ideas spread over the graph successfully. Other metrics such as node in-degree or even standard Pagerank focus only in the static topology of the network. We propose a ranking strategy that focuses on the ability of some users to push new ideas that will be successful in the future. To that end, we combine temporal attributes of nodes and edges of the network with a Pagerank based algorithm to find the trendsetters for a given topic. To test our algorithm we conduct innovative experiments over a large Twitter dataset. We show that nodes with high in-degree tend to arrive late for new trends, while users in the top of our ranking tend to be early adopters that also influence their social contacts to adopt the new trend.