Nicolas Dugué

Detecting Real-World Influence through Twitter

In this paper, we investigate the issue of detecting the real-life influence of people based on their Twitter account. We propose an overview of common Twitter features used to characterize such accounts and their activity, and show that these are inefficient in this context. In particular, retweets and followers numbers, and Klout score are not relevant to our analysis. We thus propose several Machine Learning approaches based on Natural Language Processing and Social Network Analysis to label Twitter users as Influencers or not. We also rank them according to a predicted influence level. Our proposals are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art ranking methods.

On the importance of considering social capitalism when measuring influence on Twitter

Influence on Twitter has drawn a lot of attention these past few years since this microblogging service is used to share, seek or debate about any kind of information. Several tools providing so-called influential scores have thus been proposed. However, the algorithms behind them are kept secret and it is not clear how they consider influence. Yet, many users rely on such tools to evaluate and even try to improve their influence in the Twitter network. In a recent work, it has been shown that automatic accounts can obtain high influential scores with no intuitive reason. Extending and completing this work, we show that such measures fail at distinguishing so-called social capitalists from real, truthful users. This enlights the fact that actual scores do not seem to consider the way followers and interactions are obtained on the network. To overcome this issue, we define a classifier that discriminates social capitalists from truthful users. To that aim, we crawled the Twitter network to gather examples of certified social capitalists and regular users and obtained features related to the profile and behavior of each user. We then use such a classifier to balance Klouts score to adjust influential scores. We also developed an application that allows using our classifier online. We believe our work should raise the question of the legitimacy of influence on Twitter, and lead to significant improvements in the way it is measured.

Identifying the community roles of social capitalists in the Twitter network

In the context of Twitter, social capitalists are users trying to increase their number of followers and interactions by any means. They are not healthy for the service, because they introduce a bias in the way user influence and visibility are perceived. Understanding their behavior and position in the network is thus of important interest. In this work, we propose to do so by focusing on the community structure level. We first extend an existing method based on the notion of community role, on three different points: 1) handling of directed networks, 2) more precise modeling of the community-related connectivity and 3) unsupervised role identification. We then take advantage of an existing tool to detect social capitalists, and apply our method to analyze their organization and how their links spread across the network. The specific community roles they hold in the network let us know that they reach to obtain high visibility.

A reliable and evolutive web application to detect social capitalists

On Twitter, social capitalists use dedicated hashtags and mutual subscriptions to each other in order to gain followers and to be retweeted. Their methods are successful enough to make them appear as influent users. Indeed, applications dedicated to the influence measurement such as Klout and Kred give high scores to most of these users. Meanwhile, their high number of retweets and followers are not due to the relevance of the content they tweet, but to their social capitalism techniques. In order to be able to detect these users, we train a classifier using a dataset of social capitalists and regular users. We then implement this classifier in a web application that we call DDP. DDP allows users to test whether a Twitter account is a social capitalist or not and to visualize the data we use to make the prediction. DDP allows administrator to crawl data from a lot of users automatically. Furthermore, administrators can manually label Twitter accounts as social capitalists or regular users to add them into the dataset. Finally, administrators can train new classifiers in order to take into account the new Twitter accounts added to the dataset, and thus making evolve the classifier with these new recently collected data. The web application is thus a way to collect data, make evolve the knowledge about social capitalists and to keep detecting them efficiently.

Detecting Social Capitalists on Twitter Using Similarity Measures

Social networks such as Twitter or Facebook are part of the phenomenon called Big Data, a term used to describe very large and complex data sets. To represent these networks, the connections between users can be easily represented using (directed) graphs. In this paper, we are mainly focused on two different aspects of social network analysis. First, our goal is to find an efficient and high-level way to store and process a social network graph, using reasonable computing resources (processor and memory).We believe that this is an important research interest, since it provides a more democratic method to deal with large graphs.Next, we turn our attention to the study of social capitalists, a specific kind of users on Twitter. Roughly speaking, such users try to gain visibility by following other users regardless of their contents. Using two similarity measures called overlap index and ratio, we show that such users may be detected and classified very efficiently.

A community role approach to assess social capitalists visibility in the Twitter network

In the context of Twitter, social capitalists are specific users trying to increase their number of followers and interactions by any means. These users are not healthy for the service, because they are either spammers or real users flawing the notions of influence and visibility. Studying their behavior and understanding their position in Twitter is thus of important interest. It is also necessary to analyze how these methods effectively affect user visibility. Based on a recently proposed method allowing to identify social capitalists, we tackle both points by studying how they are organized, and how their links spread across the Twitter follower–followee network. To that aim, we consider their position in the network w.r.t. its community structure. We use the concept of community role of a node, which describes its position in a network depending on its connectivity at the community level. However, the topological measures originally defined to characterize these roles consider only certain aspects of the community-related connectivity, and rely on a set of empirically fixed thresholds. We first show the limitations of these measures, before extending and generalizing them. Moreover, we use an unsupervised approach to identify the roles, in order to provide more flexibility relatively to the studied system. We then apply our method to the case of social capitalists and show they are highly visible on Twitter, due to the specific roles they hold.