Review of: Everyone's an Influencer: Quantifying Influence on Twitter

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1 Process-driven Analysis of Dynamics in Online Social Interactions The paper used datasets collected from Renren (chinese counterpart to facebook) and facebook. The Renren dataset uses two groundset datasets. The first dataset from Renren was from one year usage of the platform, and has data back to the creation of the first edge for the service. The seconds dataset was over a measurement period, and contained 29M wall posts. The facebook dataset set was created with a crawl over one regional network with 1.6M users over 6-7 months. The facebook dataset was more limited and only included social link interactions with sender, receiver and timestamp, but no creation timestamp. The motivation for the paper was to comprehend a deeper understanding of user interaction in online social networks. The paper states that similar, but smaller types of research had been done before, but a specific model does not. By creating/proposing a generative model, this would advance general understanding of online social networks and would be applicable to research other social networks as well. Results show that users invite new friends to interact with them at a close to constant rate. They also show that people tend to interact with users that have significant overlap in social circles, and that interaction with old friends gradually decreases. Everyone s an influencer: Quantifying Influence on Twitter The paper aims to investigate influence twitter users has on other users. The research/data gathering was done in two different steps. First by tracking 74M events done by 1.6M different twitter users in a time period over 2 months. Total of 1.03B tweets were recorded, 87M was picked out as tweets with shortened URL s (bit.ly) and narrowed down to 74M from users which were active on both months of the survey. Secondly users who posted bit.ly links were palced in a queue to be crawled later, also crawling for their followers and do the same for them. This pool was consistent of 56M users. The motivation of the paper seems to be to fill an interest in and how and with what efficiency information or a info on a product can be spread through special individuals. Emphasis on social diffusion. By bringing this to online social media and twitter, the research want to take twitter users influence characteristics as an aspect into social diffusion reserach. The paper has in my eyes a clear economic structure and aim in mind, and reflects a lot on the efficiency in comparison to investment. The research suggest that using ordinary users or user without big amounts of influencers can be the most cost effective for a marketer to use. They suggest a price that correlates to number of followers or high influence, and gives back that these user will give less influence value per investment. However it is noted that these users have the advantage of not needing to identify a big number of influencer as their reach is big in itself. It is highly noted that the results should be viewed as a hypothesis and not as a concluding statement. Review of: Everyone's an Influencer: Quantifying Influence on Twitter The paper looks into how users of Twitter may influence each other, and discusses the cost of using several "ordinary influencers" versus the most influential users for marketing purposes. Influence on Twitter were measured on two datasets. 1.03B public tweets collected between September 13th 2009 and November 15th And a graph of all users who posted an URL during the same period. Tweets from the dataset were trimmed to only tweets containing a URL and were cleaned for typical spam links. Influence is measured by reposts of URL. The method used to determine influence can be seen as a lower bound on influence. As not all users will repost or retweet a URL.

2 The paper suggests that "ordinary influencers" might in many cases be the most cost effective alternative. However, further research are required. Review of: Process-driven Analysis of Dynamics in Online Social Interactions Previous research have looked into how users link to and interact with each other in social networks, however, little research have been done on the detailed interaction over time. The paper presents an analysis of a complete record of data from the Chinise Renren network, and a comparison of this data with a sample from Facebook. The paper also presents two generative models of social interactions, and compare those with the ground truth data from Renren. The interaction data for Renren in the paper were provided by Renren, and were a complete sample from November 2005 until December The Facebook data were sampled between January 2008 and June The paper shows that users of social networks adds new connections at a constant rate. Users also tend to interact less with each other over time. The paper also presents two generative models to analyse social interactions. The co-evolution model and the naive model. The co-evolution model yields the best results of the two. The paper suggests further research on the interaction graph on a community level. Process-driven Analysis of Dynamics in Online Social Interactions This paper analyze the first complete record of full interaction and network dynamics in a large online social network and develop and evaluate a co-evolution model (the first generative model for interactions on OSNs) that generates interactions across social links. They derived large scale datasets from Renren and Face- book where users invite new friends to interact at a nearly constant rate, prefer to interact with friends with whom they share significant overlaps in social circles, and most social links drop in interaction frequency over time. This paper mainly makes a new generative model that combines the growth of social links with the generation of user interaction events on those links. This will help to design of network interaction models and it will broader implications in other areas, such as friend recommendation, information diffusion, and news feed ranking. Everyone s an Influencer: Quantifying Influence on Twitter In this paper, they investigate the attributes and relative influence of users of Twitter by tracking many diffusion events that took place on the Twitter follower graph over a two months interval. The result of statistical modeling of observational data defines ordinary influencers individuals who exert average, or even less than average influence are under many circumstances more cost-effective, is intriguing. Their model included the Seed user attributes (followers, friends, tweets, date of joining 2) and Past influence of seed users ((a) average, minimum, and maximum total influence (b) average, minimum, and maximum local influence) features as predictors. One of most important finding of this paper is consistent with previous theoretical work that has also questioned the feasibility of word-ofmouth strategies that depend on triggering social epidemics by targeting special individuals. Their observation that large cascades are rare is likely to apply in other contexts as well in case of Twitter. Process-driven Analysis of dynamics in online social interactions This paper develop and evaluate a co-evolution model, it generates interactions across social links. Insight behind this model are driven from large scale datasets from Renren and Facebook. This reveals that users invite new friends to interact at a nearly constant rate, prefer to interact with friends with

3 whom they share significant overlaps in social circles and gradually lose interest in interacting with old friends. A co-evaluation model has two complementary processes: one concerned with forming social links and another generates interactions along the links. Everyone s an influencer: Quantifying influence on Twitter In this paper, they crawled data and calculate the influence on Twitter. In light of the emphasis placed on prominent individuals as optimal vehicles for disseminating information, the possibility that ordinary influencers -individuals who exert average, or even less-than average influence- are under many circumstances more cost-effective, is intriguing. It is quite possible that content seeded by outside sources may diffuse quite different than content selected by users themselves. World-of-mouth information spreads via many small cascades, mostly triggered by ordinary individuals, is also likely to apply generally, as has been suggested elsewhere. Process-driven Analysis of Dynamics in Online Social Interactions The main goal of this paper was to create a generative model of social interactions. For the creation the authors used a dataset from the OSN Renren, where they at first studied the trends of OSN activity. These trends include the following processes: 1) forgetting process: users decrease their interaction over time, 2) reinforcement process: the more two users interact, the more valuable their relationship is and 3) exploration process: users tend to search for new users at a constant rate. The authors used two datasets: 1) Renren: includes data from Renren s first year (large growth) and 2) Facebook: the data come from the time FB was already a mature OSN and there were some missing data. Only reciprocal posts were used. Next, they describe the analysis that led them to the discovery of the processes mentioned above. Furthermore, they thoroughly describe their model with all its parameters. They evaluate the model by theoretical data by proving a widely known observation. Finally, they fit their model on the real data and evaluate its trustworthiness. Everyone s an influencer: quantifying influence on twitter The second paper describes the question whether there are some more influential users on Twitter, meaning users, through which is an URL content spread faster. The authors focus solely on those who are the source of the content. They mention some problems: classifying a repost as an influential action might just be a sign of homophily; reposting an URL is somewhat stronger signal rather than just clicking on the link (however, there were no data for experimenting with this). The results state that users who have been influential in the past are most likely to be influential again in the future. However, this statistics are only correct on average. Next, they evaluate the factor of content character. They found out that interesting and positive content spreads farther. The final outcome of the paper is somewhat intriguing ordinary influencers are more cost-effective. Also, when seeded with wholly different content, the process might behave differently. Dynamics in Social Networks The first paper Process-driven Analysis of Dynamics in Online Social Interactions consists of two parts. Firstly authors analyse data of user interactions in social networks, secondly based on this data they build a model for user interactions and test it. It is very important to note that the there are two datasets used in the study, first one comes from crawling facebook, which is a standard technique, while the second one is a complete record of full user interactions on Renren social network, which is very rare in the studies. Moreover Renren dataset comes from the time when the service vas very young and number of users was growing rapidly. The main results of the analysis are that users invite new friends to interact at a nearly constant rate, prefer to interact with friends with whom they share significant overlaps in social circles, and gradually lose interest in interacting with old friends. The second paper Everyone s an Influencer: Quantifying Influence on Twitter investigates the

4 influence flow on Twitter, the popular microblogging service. The main aim of the authors is to find the influencers (users whose tweets reach a wide audience) within users of Twitter. Tweets analysed for the purpose of the study are only ones containing urls, coming from a url-shortener bit.ly. Big part of the analysis covers using influencers as means of advertising, and one of the results is that instead of using users which reach the widest audience, similar goal can be achieved by using ordinary influencers - less influential users. Everyone s an Influencer: Quantifying Influence on Twitter This paper tries to discover the factors of being influential in Twitter. They do this by watching 1.6M Twitter users over a two months interval in They record every diffusion event happened in this period of time for the users. They managed to gather 74 million diffusion events. What they found out is that the influential history is important in deciding whether a user will be influential in future or not. Another important factor is the number of followers. They also found out that predicting whether a specific user or URL will generate a huge cascade is a relatively difficult thing to do. They then looked into some marketing strategies trying to analyze the cost of identifying potential influencers versus compensating them by choosing ordinary influencers. What they found is that in most cases (not always) the most cost-effective way of marketing is through ordinary influencers. They define ordinary influencers as the users who cause average or even less-than-average influence Process-driven Analysis of Dynamics in Online Social Interactions This paper tries to analyze a large amount of record of full interaction and network dynamics in a large online social network. Their first contribution is gathering the first complete record of interaction in an OSN. They chose Renren, the chinese online social network, which is very similar to Facebook. Their recorded dataset covers all wall posts, new user events, and new social link events during the first full year of Renren, including 623K new users, 8.2 million new links, and 29 million wall posts. Based on their recorded data, they managed to find some interesting results regarding users interaction development in a time period. One example of their finding is that users tend to add new friends at a constant rate. Their second contribution is deriving a generative model of social interactions that accurately captures both their results and previously observed network properties. To develop this model, they used the large scale data set they gathered from Renren. Process-driven analysis of dynamics in online social interactions The strength of social ties reflects the relationship between users. If it can be evaluated correctly, designers can optimal the network topology and realize low latency communication. However, there is no standard method to evaluate the strength of social ties. One of the ways is to analyze the frequency of interactions between the linked users. Due to lack of data set, few works can give a detailed examination of a large amount of users interaction. This paper makes up this weakness. Depending on the complete data set from RenRen that is the largest social network in China, authors get a chance to analyze the record of full interaction and network dynamics in a large online social network. This paper presents three interesting facts by analyzing the data set from RenRen. First, it shows a user invites new interaction partners at a constant speed after the first week. Second, users who have common friends tend to interact with each other, which is confirmed by the comparison between RenRen data set and Facebook data set. Third, users who communicate frequently in the past tend to build new interactions while with the time pass, less pair of users tend to build new interactions.

5 The paper also propose a new model based on the result of data analysis. The proposed social coevolution model is a generative model which takes time and user interaction into account. It combines social graph model with interaction model. Relying on the experiment, it illustrates the results from proposed model match the true data set better compared with the basic model that assume there is no connection between the evolution of social network structure and user interaction. This paper gives a clear view about what will be discussed in this paper and experiments are designed properly. However, there are still some problems that could be researched further. For example, different group people may have different behavior patterns. Some related problems also could be considered. How long each interaction lasts between two users? Shall we classify the interaction? How to evaluate the influence of interactions on friendship? Everyone is an influencer: Quantifying influence on Twitter Information diffusion has been researched for many years in different areas. With the popularity of social networks, more and more researchers pay attention on the mechanism of information diffusion in social network. Due to the limit of volume and variety of users and content, it is hard to understand the principle of information spread in social network. This paper presents the investigation result from 1.6M Twitter users who generate 74 million diffusion events. It first shows that the distribution of repost cascade size is approximately power-law and the distribution of repost cascade depth is right skewed. Then the conclusion that individual-level prediction of influence is relatively unreliable is presented based on the attribute analysis including the past influence, date of joining and number of followers, friends and tweets. For the prediction of individual influence, a regression tree model is built and the experimental result shows it fits actual data very well. Finally, the influence of content category is concerned and experimental result shows more interesting content tends to generate larger cascade on average. This paper also gives a comparison between regression tree model and ordinary linear regression model and shows the former is more suitable in prediction of individual influence. At the end of this paper, the targeting strategies are analyzed and it proposes the most cost-efficient influencers are ordinary users. The paper gives a clear description about each experiment and presents some interesting observations at the end of each experiment. However, some weaknesses lower the quality of this paper. For example, in the chapter of targeting strategies the parameter I is unknown as no description is provided and the label of y-axis in figure11 is a bit confusing. Summary of process-driven analysis of dynamics in online social interactions This paper proposes an interaction model for online social networks. The ground-truth dataset was obtained from Renren, including 623K new users, 8.2M new links, and 29M wall posts. The model was constructed based on the analysis of this data set. First, the authors discovered that users tended to interact with their friends immediately after they join the social network and then became less interactive. Next, the authors examined how users choose new partners to interact with. They found that friends with high neighborhood overlap are more likely to be chosen as interaction partners. Finally, they studied temporal dynamics of established interaction relationship. Study showed that the interactions between friends reinforce their relationship, leading to more future interactions. However, in a long run, a given pair of users tends to interact less over time because they may forget each other as time goes by. Based on the observations, the authors constructed a social co-evolution model. The social link generation model is based on the microscopic evolution model. The user interaction model simulates real world user behaviors. A user may interact with a user or does nothing. If the user decides to interact with someone, she may interact with a user with whom she has never interacted before or with a user that she has previous interaction. A user tends to select the one with whom she has most

6 frequent interactions (old contact) or the one with whom she has the largest overlapping neighborhood (new contact). The model was evaluated against the real dataset and a base model. Evaluation results show that the co-evolution model produces very close results to the real dataset and it outperforms the base model. Summary of everyone s an influencer: quantifying influence on Twitter This paper investigates the most cost-effective method to disseminating information on social networks like Twitter. The authors recorded 1.03 billion public tweets on Twitter during two months and extracted 87 million tweets that included bit.ly URLs, which are used as the dataset of the analysis. Analysis shows that the distribution of cascade sizes is approximately power-law, and the depth of the cascade resembles an exponential distribution. The results reveal that most events do not spread at all. The authors used the number of followers and other attributes of seed users as well as past influence of seed users to predict future influence. The results show that large follower count and past success are necessary features for future success, but not sufficient. To study the impact of tweet content, Turkers were hired to score the content based on their interestingness and positive. Due to the employment of humans, the number of URLs that were analyzed was only 795. Analysis shows that content that is rated more interesting and positive tends to generate larger cascades. The authors added rated interestingness and positive feeling etc. to their new experiments to predict future influence. However, the prediction accuracy was not improved. On the contrary, it became worse mainly due to the small number of URLs analyzed. The authors next studied the cost of hiring tweeters. They discovered that paying a large amount of money to a few most influential users can be most cost-effective under some circumstances, but paying a little to many less influential users can achieve similar performance.