Finding Influential Users in Social Media Using Association Rule Learning

Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods

Identifying the topic-specific influential users in Twitter

Social Influence can be described as the ability to have an effect on the thoughts or actions of others. Influential members in online communities are becoming the new media to market products and sway opinions. Also, their guidance and recommendations can save some people the search time and assist their selective decision making. The objective of this research is to detect the influential users in a specific topic on Twitter. In more detail, from a collection of tweets matching a specified query, we want to detect the influential users, in an online fashion. In order to address this objective, we first want to focus our search on the individuals who write in their personal accounts, so we investigate how we can differentiate between the personal and non-personal accounts. Secondly, we investigate which set of features can best lead us to the topic-specific influential users, and how these features can be expressed in a model to produce a ranked list of influential users. Finally, we look into the use of the language and if it can be used as a supporting feature for detecting the author\u27s influence. In order to decide on how to differentiate between the personal and non-personal accounts, we compared between the effectiveness of using SVM and using a manually assembled list of the non-personal accounts. In order to decide on the features that can best lead us to the influential users, we ran a few experiments on a set of features inspired from the literature. Two ranking methods were then developed, using feature combinations, to identify the candidate users for being influential. For evaluation we manually examined the users, looking at their tweets and profile page in order to decide on their influence. To address our final objective, we ran a few experiments to investigate if the SLM could be used to identify the influential users\u27 tweets. For user account classification into personal and non-personal accounts, the SVM was found to be domain independent, reliable and consistent with a precision of over 0.9. The results showed that over time the list performance deteriorates and when the domain of the test data was changed, the SVM performed better than the list with higher precision and specificity values. We extracted eight independent features from a set of 12, and ran experiments on these eight and found that the best features at identifying influential users to be the Followers count, the Average Retweets count, The Average Retweets Frequency and the Age_Activity combination. Two ranking methods were developed and tested on a set of tweets retrieved using a specific query. In the first method, these best four features were combined in different ways. The best combination was the one that took the average of the Followers count and the Average Retweets count, producing a precision at 10 value of 0.9. In the second method, the users were ranked according to the eight independent features and the top 50 users of each were included in separate lists. The users were then ranked according to their appearance frequency in these lists. The best result was obtained when we considered the users who appeared in six or more of the lists, which resulted in a precision of 1.0. Both ranking methods were then conducted on 20 different collections of retrieved tweets to verify their effectiveness in detecting influential users, and to compare their performance. The best result was obtained by the second method, for the set of users who appeared in six or more of the lists, with the highest precision mean of 0.692. Finally, for the SLM, we found a correlation between the users\u27 average Retweets counts and their tweets\u27 perplexity values, which consolidates the hypothesis that SLM can be trained to detect the highly retweeted tweets. However, the use of the perplexity for identifying influential users resulted in very low precision values. The contributions of this thesis can be summarized into the following. A method to classify the personal accounts was proposed. The features that help detecting influential users were identified to be the Followers count, the Average Retweets count, the Average Retweet Frequency and the Age_Activity combination. Two methods for identifying the influential users were proposed. Finally, the simplistic approach using SLM did not produce good results, and there is still a lot of work to be done for the SLM to be used for identifying influential users

Assessing Influential Users in Live Streaming Social Networks

abstract: Live streaming has risen to significant popularity in the recent past and largely this live streaming is a feature of existing social networks like Facebook, Instagram, and Snapchat. However, there does exist at least one social network entirely devoted to live streaming, and specifically the live streaming of video games, Twitch. This social network is unique for a number of reasons, not least because of its hyper-focus on live content and this uniqueness has challenges for social media researchers. Despite this uniqueness, almost no scientific work has been performed on this public social network. Thus, it is unclear what user interaction features present on other social networks exist on Twitch. Investigating the interactions between users and identifying which, if any, of the common user behaviors on social network exist on Twitch is an important step in understanding how Twitch fits in to the social media ecosystem. For example, there are users that have large followings on Twitch and amass a large number of viewers, but do those users exert influence over the behavior of other user the way that popular users on Twitter do? This task, however, will not be trivial. The same hyper-focus on live content that makes Twitch unique in the social network space invalidates many of the traditional approaches to social network analysis. Thus, new algorithms and techniques must be developed in order to tap this data source. In this thesis, a novel algorithm for finding games whose releases have made a significant impact on the network is described as well as a novel algorithm for detecting and identifying influential players of games. In addition, the Twitch network is described in detail along with the data that was collected in order to power the two previously described algorithms.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Influential users in Twitter: detection and evolution analysis

In this paper, we study how to detect the most influential users in the microblogging social network platform Twitter and their evolution over time. To this aim, we consider the Dynamic Retweet Graph (DRG) proposed in Amati et al. (2016) and partially analyzed in Amati et al. (IADIS Int J Comput Sci Inform Syst, 11(2) 2016), Amati et al. (2016). The model of the evolution of the Twitter social network is based here on the retweet relationship. In a DRGs, the last time a tweet has been retweeted we delete all the edges representing this tweet. In this way we model the decay of tweet life in the social platform. To detect the influential users, we consider the central nodes in the network with respect to the following centrality measures: degree, closeness, betweenness and PageRank-centrality. These measures have been widely studied in the static case and we analyze them on the sequence of DRG temporal graphs with special regard to the distribution of the 75% most central nodes. We derive the following results: (a) in all cases, applying the closeness measure results into many nodes with high centrality, so it is useless to detect influential users; (b) for all other measures, almost all nodes have null or very low centrality and (c) the number of vertices with significant centrality are often the same; (d) the above observations hold also for the cumulative retweet graph and, (e) central nodes in the sequence of DRG temporal graphs have high centrality in cumulative graph

Social influence analysis in microblogging platforms - a topic-sensitive based approach

The use of Social Media, particularly microblogging platforms such as Twitter, has proven to be an effective channel for promoting ideas to online audiences. In a world where information can bias public opinion it is essential to analyse the propagation and influence of information in large-scale networks. Recent research studying social media data to rank users by topical relevance have largely focused on the “retweet", “following" and “mention" relations. In this paper we propose the use of semantic profiles for deriving influential users based on the retweet subgraph of the Twitter graph. We introduce a variation of the PageRank algorithm for analysing users’ topical and entity influence based on the topical/entity relevance of a retweet relation. Experimental results show that our approach outperforms related algorithms including HITS, InDegree and Topic-Sensitive PageRank. We also introduce VisInfluence, a visualisation platform for presenting top influential users based on a topical query need

How did Ebola information spread on twitter : broadcasting or viral spreading?

BACKGROUND: Information and emotions towards public health issues could spread widely through online social networks. Although aggregate metrics on the volume of information diffusion are available, we know little about how information spreads on online social networks. Health information could be transmitted from one to many (i.e. broadcasting) or from a chain of individual to individual (i.e. viral spreading). The aim of this study is to examine the spreading pattern of Ebola information on Twitter and identify influential users regarding Ebola messages. METHODS: Our data was purchased from GNIP. We obtained all Ebola-related tweets posted globally from March 23, 2014 to May 31, 2015. We reconstructed Ebola-related retweeting paths based on Twitter content and the follower-followee relationships. Social network analysis was performed to investigate retweeting patterns. In addition to describing the diffusion structures, we classify users in the network into four categories (i.e., influential user, hidden influential user, disseminator, common user) based on following and retweeting patterns. RESULTS: On average, 91% of the retweets were directly retweeted from the initial message. Moreover, 47.5% of the retweeting paths of the original tweets had a depth of 1 (i.e., from the seed user to its immediate followers). These observations suggested that the broadcasting was more pervasive than viral spreading. We found that influential users and hidden influential users triggered more retweets than disseminators and common users. Disseminators and common users relied more on the viral model for spreading information beyond their immediate followers via influential and hidden influential users. CONCLUSIONS: Broadcasting was the dominant mechanism of information diffusion of a major health event on Twitter. It suggests that public health communicators can work beneficially with influential and hidden influential users to get the message across, because influential and hidden influential users can reach more people that are not following the public health Twitter accounts. Although both influential users and hidden influential users can trigger many retweets, recognizing and using the hidden influential users as the source of information could potentially be a cost-effective communication strategy for public health promotion. However, challenges remain due to uncertain credibility of these hidden influential users