Network denoising in social media

Revisiting Information Cascades in Online Social Networks, 2024

It is widely believed that a user’s activity pattern in Online Social Networks (OSNs) is strongly influenced by their friends or the users they follow. Building on this intuition, numerous models have been proposed over the years to predict information propagation in OSNs. Many of these models drew inspiration from the process of infectious spread within a population. While this approach is definitely plausible, it relies on knowledge of users’ social connections, which can be challenging to obtain due to privacy concerns. Moreover, while a significant body of work has focused on predicting macro-level features, such as the total cascade size, relatively little attention has been given to the prediction of micro-level features, such as the activity of an individual user. In this study we aim to address this gap by proposing a method to predict the activity of individual users in an OSN, relying solely on their interactions rather than prior knowledge of their social network. We evaluated our results on four large datasets, each comprising over 14 million tweets, recorded on X social network across four different topics over several month. Our method achieved a mean 𝐹1 score of 0.86, with a best result of 0.983.

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011

We present Spine, an efficient algorithm for finding the "backbone" of an influence network. Given a social graph and a log of past propagations, we build an instance of the independent-cascade model that describes the propagations. We aim at reducing the complexity of that model, while preserving most of its accuracy in describing the data.

Community detection in online social networks is typically based on the analysis of the explicit connections between users, such as "friends" on Facebook and "followers" on Twitter. But online users often have hundreds or even thousands of such connections, and many of these connections do not correspond to real friendships or more generally to accounts that users interact with. We claim that community detection in online social networks should be question-oriented and rely on additional information beyond the simple structure of the network. The concept of 'community' is very general, and different questions such as "whom do we interact with?" and "with whom do we share similar interests?" can lead to the discovery of different social groups. In this paper we focus on three types of communities beyond structural communities: activity-based, topic-based, and interaction-based. We analyze a Twitter dataset using three different weightings of the structural network meant to highlight these three community types, and then infer the communities associated with these weightings. We show that the communities obtained in the three weighted cases are highly different from each other, and from the communities obtained by considering only the unweighted structural network. Our results confirm that asking a precise question is an unavoidable first step in community detection in online social networks, and that different questions can lead to different insights about the network under study.

Current network inference algorithms fail to generate graphs with edges that can explain whole sequences of node interactions in a given dataset or trace. To quantify how well an inferred graph can explain a trace, we introduce feasibility, a novel quality criterion, and suggest that it is linked to the result's accuracy. In addition, we propose CEM-*, a network inference method that guarantees 100% feasibility given online social media traces, which is a non-trivial extension of the Expectation-Maximization algorithm developed by Newman (2018). We propose a set of linear optimization updates that incorporate a set of auxiliary variables and a set of feasibility constraints; the latter takes into consideration all the hidden paths that are possible between users based on their timestamps of interaction and guides the inference toward feasibility. We provide two CEM-* variations, that assume either an Erdős–Rényi (ER) or a Stochastic Block Model (SBM) prior for the underlying gra...

Proceedings of the fourth ACM european conference on Computer systems - EuroSys '09, 2009

Social networks are popular platforms for interaction, communication and collaboration between friends. Researchers have recently proposed an emerging class of applications that leverage relationships from social networks to improve security and performance in applications such as email, web browsing and overlay routing. While these applications often cite social network connectivity statistics to support their designs, researchers in psychology and sociology have repeatedly cast doubt on the practice of inferring meaningful relationships from social network connections alone. This leads to the question: Are social links valid indicators of real user interaction? If not, then how can we quantify these factors to form a more accurate model for evaluating sociallyenhanced applications? In this paper, we address this question through a detailed study of user interactions in the Facebook social network. We propose the use of interaction graphs to impart meaning to online social links by quantifying user interactions. We analyze interaction graphs derived from Facebook user traces and show that they exhibit significantly lower levels of the "small-world" properties shown in their social graph counterparts. This means that these graphs have fewer "supernodes" with extremely high degree, and overall network diameter increases significantly as a result. To quantify the impact of our observations, we use both types of graphs to validate two well-known socialbased applications (RE ] and SybilGuard [Yu 2006]). The results reveal new insights into both systems, and confirm our hypothesis that studies of social applications should use real indicators of user interactions in lieu of social graphs.

2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2012

The advent of social media have allowed us to build massive networks of weak ties: acquaintances and nonintimate ties we use all the time to spread information and thoughts. Conversely, strong ties are the people we really trust, people whose social circles tightly overlap with our own and, often, they are also the people most like us. Unfortunately, the social media do not incorporate tie strength in the creation and management of relationships, and treat all users the same: friend or stranger, with little or nothing in between. In the current work, we address the challenging issue of detecting on online social networks the strong and intimate ties from the huge mass of such mere social contacts. In order to do so, we propose a novel multidimensional definition of tie strength which exploits the existence of multiple online social links between two individuals. We test our definition on a multidimensional network constructed over users in Foursquare, Twitter and Facebook, analyzing the structural role of strong e weak links, and the correlations with the most common similarity measures.

With hundreds of millions of users worldwide, social networks provide incredible opportunities for social connection, learning, political and social change, and individual entertainment and enhancement in a wide variety of forms. In light of these notable outcomes, understanding information diffusion over online social networks is a critical research goal. Because many social interactions currently take place in online networks, we now have have access to unprecedented amounts of information about social interaction. Prior to the advent of such online networks, investigations about social behavior required resource-intensive activities such as random trials, surveys, and manual data collection to gather even small data sets. Now, massive amounts of information about social networks and social interactions are recorded. This wealth of data can allow us to study social interactions on a scale and at a level of detail that has never before been possible. We present an integrated approach to information diffusion in online social networks focusing on three key problems: (1) Querying and analysis of online social network datasets; (2) Modeling and analysis of social networks; and (3) Analysis of social media and social interactions in the contemporary media environment. The overarching goals are to generate a greater understanding of social interactions in online networks through data analysis, to develop reliable and scalable models that can predict outcomes of these social processes, and ultimately to create applications that can shape the outcome of these processes. We start by developing and refining models of information diffusion based on real-world data sets. We next address the problem of finding influential users in this data-driven framework. It is equally important to identify techniques that can slow or prevent the spread of misinformation, and hence algorithms are explored to address this question. A third interest is the process by which a social group forms opinions about an idea or product, and we therefore describe preliminary approaches to create models that accurately capture the opinion formation process in online social networks. While questions relating to the propagation of a single news item or idea are important, these information campaigns do not exist in isolation. Therefore, our proposed approach also addresses the interplay of the many information diffusion processes that take place simultaneously in a network and the relative importance of different topics or trends over multiple spatial and temporal resolutions.

Seventh International Aaai Conference on Weblogs and Social Media, 2013

In many complex social systems, the timing and frequency of interactions between individuals are observable but friendship ties are hidden. Recovering these hidden ties, particularly for casual users who are relatively less active, would enable a wide variety of friendship-aware applications in domains where labeled data are often unavailable, including online advertising and national security. Here, we investigate the accuracy of multiple statistical features, based either purely on temporal interaction patterns or on the cooperative nature of the interactions, for automatically extracting latent social ties. Using self-reported friendship and non-friendship labels derived from an anonymous online survey, we learn highly accurate predictors for recovering hidden friendships within a massive online data set encompassing 18 billion interactions among 17 million individuals of the popular online game Halo: Reach. We find that the accuracy of many features improves as more data accumulates, and cooperative features are generally reliable. However, periodicities in interaction time series are sufficient to correctly classify 95% of ties, even for casual users. These results clarify the nature of friendship in online social environments and suggest new opportunities and new privacy concerns for friendship-aware applications that do not require the disclosure of private friendship information.

Proceedings of the 1st International Conference on Advanced Research Methods and Analytics, 2016

Know your customer is a core element of any customer relationship management system for mass service organizations. The emergence of social networking services has provided a radically new dimension, creating a more personalized, deeper, ubiquitous and almost real time relation with customers. At the same time, some of the more widespread social network platforms seem to be evolving not only as social networks between individuals but also as mass information distribution media. When knowing your customer through social networking services, it may be of interest to disambiguate which part of the customer context in the network relates to his peers from other sources. In this paper we present an algorithmic approach to disambiguate one aspect of such relation, as expressed in the nature of the contacts established in the social network: with peers or with organizations, news media or influencers. We focus in the case of Twitter where a simple supervised linear regression can provide a ranking score, effectively discriminating and ordering by closeness peer and other types of contacts (mass media or influencers). Such discrimination can serve as a preliminary step for deeper analysis or privacy protection of customer interaction and is suitable for implementation in automated Big Data systems.

2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2012

The advent of social media have allowed us to build massive networks of weak ties: acquaintances and nonintimate ties we use all the time to spread information and thoughts. Conversely, strong ties are the people we really trust, people whose social circles tightly overlap with our own and, often, they are also the people most like us. Unfortunately, the social media do not incorporate tie strength in the creation and management of relationships, and treat all users the same: friend or stranger, with little or nothing in between. In the current work, we address the challenging issue of detecting on online social networks the strong and intimate ties from the huge mass of such mere social contacts. In order to do so, we propose a novel multidimensional definition of tie strength which exploits the existence of multiple online social links between two individuals. We test our definition on a multidimensional network constructed over users in Foursquare, Twitter and Facebook, analyzing the structural role of strong e weak links, and the correlations with the most common similarity measures.