arXiv subjects

Aristides Gionis

Publications and source records attributed to Aristides Gionis.

At least 91 records · Page 5Linked to original sources

arXiv2017

Joint Non-negative Matrix Factorization for Learning Ideological Leaning on Twitter

People are shifting from traditional news sources to online news at an incredibly fast rate. However, the technology behind online news consumption promotes content that confirms the users' existing point of view. This phenomenon has led to polarization of opinions and intolerance towards opposing views. Thus, a key problem is to model information filter bubbles on social media and design methods to eliminate them. In this paper, we use a machine-learning approach to learn a liberal-conservative ideology space on Twitter, and show how we can use the learned latent space to tackle the filter bubble problem. We model the problem of learning the liberal-conservative ideology space of social media users and media sources as a constrained non-negative matrix-factorization problem. Our model incorporates the social-network structure and content-consumption information in a joint factorization problem with shared latent factors. We validate our model and solution on a real-world Twitter dataset consisting of controversial topics, and show that we are able to separate users by ideology with over 90% purity. When applied to media sources, our approach estimates ideology scores that are highly correlated (Pearson correlation 0.9) with ground-truth ideology scores. Finally, we demonstrate the utility of our model in real-world scenarios, by illustrating how the learned ideology latent space can be used to develop exploratory and interactive interfaces that can help users in diffusing their information filter bubble.

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arXiv2017

Quantifying Controversy in Social Media

Which topics spark the most heated debates on social media? Identifying those topics is not only interesting from a societal point of view, but also allows the filtering and aggregation of social media content for disseminating news stories. In this paper, we perform a systematic methodological study of controversy detection by using the content and the network structure of social media. Unlike previous work, rather than study controversy in a single hand-picked topic and use domain specific knowledge, we take a general approach to study topics in any domain. Our approach to quantifying controversy is based on a graph-based three-stage pipeline, which involves (i) building a conversation graph about a topic; (ii) partitioning the conversation graph to identify potential sides of the controversy; and (iii) measuring the amount of controversy from characteristics of the graph. We perform an extensive comparison of controversy measures, different graph-building approaches, and data sources. We use both controversial and non-controversial topics on Twitter, as well as other external datasets. We find that our new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy, and show that content features are vastly less helpful in this task.

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arXiv2017

Active Network Alignment: A Matching-Based Approach

Network alignment is the problem of matching the nodes of two graphs, maximizing the similarity of the matched nodes and the edges between them. This problem is encountered in a wide array of applications-from biological networks to social networks to ontologies-where multiple networked data sources need to be integrated. Due to the difficulty of the task, an accurate alignment can rarely be found without human assistance. Thus, it is of great practical importance to develop network alignment algorithms that can optimally leverage experts who are able to provide the correct alignment for a small number of nodes. Yet, only a handful of existing works address this active network alignment setting. The majority of the existing active methods focus on absolute queries ("are nodes $a$ and $b$ the same or not?"), whereas we argue that it is generally easier for a human expert to answer relative queries ("which node in the set $\{b_1, \ldots, b_n\}$ is the most similar to node $a$?"). This paper introduces two novel relative-query strategies, TopMatchings and GibbsMatchings, which can be applied on top of any network alignment method that constructs and solves a bipartite matching problem. Our methods identify the most informative nodes to query by sampling the matchings of the bipartite graph associated to the network-alignment instance. We compare the proposed approaches to several commonly-used query strategies and perform experiments on both synthetic and real-world datasets. Our sampling-based strategies yield the highest overall performance, outperforming all the baseline methods by more than 15 percentage points in some cases. In terms of accuracy, TopMatchings and GibbsMatchings perform comparably. However, GibbsMatchings is significantly more scalable, but it also requires hyperparameter tuning for a temperature parameter.

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arXiv2017

Fully Dynamic Algorithm for Top-$k$ Densest Subgraphs

Given a large graph, the densest-subgraph problem asks to find a subgraph with maximum average degree. When considering the top-$k$ version of this problem, a naïve solution is to iteratively find the densest subgraph and remove it in each iteration. However, such a solution is impractical due to high processing cost. The problem is further complicated when dealing with dynamic graphs, since adding or removing an edge requires re-running the algorithm. In this paper, we study the top-$k$ densest-subgraph problem in the sliding-window model and propose an efficient fully-dynamic algorithm. The input of our algorithm consists of an edge stream, and the goal is to find the node-disjoint subgraphs that maximize the sum of their densities. In contrast to existing state-of-the-art solutions that require iterating over the entire graph upon any update, our algorithm profits from the observation that updates only affect a limited region of the graph. Therefore, the top-$k$ densest subgraphs are maintained by only applying local updates. We provide a theoretical analysis of the proposed algorithm and show empirically that the algorithm often generates denser subgraphs than state-of-the-art competitors. Experiments show an improvement in efficiency of up to five orders of magnitude compared to state-of-the-art solutions.

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arXiv2017

The Effect of Collective Attention on Controversial Debates on Social Media

We study the evolution of long-lived controversial debates as manifested on Twitter from 2011 to 2016. Specifically, we explore how the structure of interactions and content of discussion varies with the level of collective attention, as evidenced by the number of users discussing a topic. Spikes in the volume of users typically correspond to external events that increase the public attention on the topic -- as, for instance, discussions about `gun control' often erupt after a mass shooting. This work is the first to study the dynamic evolution of polarized online debates at such scale. By employing a wide array of network and content analysis measures, we find consistent evidence that increased collective attention is associated with increased network polarization and network concentration within each side of the debate; and overall more uniform lexicon usage across all users.

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arXiv2017

Factors in Recommending Contrarian Content on Social Media

Polarization is a troubling phenomenon that can lead to societal divisions and hurt the democratic process. It is therefore important to develop methods to reduce it. We propose an algorithmic solution to the problem of reducing polarization. The core idea is to expose users to content that challenges their point of view, with the hope broadening their perspective, and thus reduce their polarity. Our method takes into account several aspects of the problem, such as the estimated polarity of the user, the probability of accepting the recommendation, the polarity of the content, and popularity of the content being recommended. We evaluate our recommendations via a large-scale user study on Twitter users that were actively involved in the discussion of the US elections results. Results shows that, in most cases, the factors taken into account in the recommendation affect the users as expected, and thus capture the essential features of the problem.

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arXiv2017

Exposing Twitter Users to Contrarian News

Polarized topics often spark discussion and debate on social media. Recent studies have shown that polarized debates have a specific clustered structure in the endorsement net- work, which indicates that users direct their endorsements mostly to ideas they already agree with. Understanding these polarized discussions and exposing social media users to content that broadens their views is of paramount importance. The contribution of this demonstration is two-fold. (i) A tool to visualize retweet networks about controversial issues on Twitter. By using our visualization, users can understand how polarized discussions are shaped on Twitter, and explore the positions of the various actors. (ii) A solution to reduce polarization of such discussions. We do so by exposing users to information which presents a contrarian point of view. Users can visually inspect our recommendations and understand why and how these would play out in terms of the retweet network. Our demo (https://users.ics.aalto.fi/kiran/reducingControversy/ homepage) provides one of the first steps in developing automated tools that help users explore, and possibly escape, their echo chambers. The ideas in the demo can also help content providers design tools to broaden their reach to people with different political and ideological backgrounds.

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arXiv2017

The Ebb and Flow of Controversial Debates on Social Media

We explore how the polarization around controversial topics evolves on Twitter - over a long period of time (2011 to 2016), and also as a response to major external events that lead to increased related activity. We find that increased activity is typically associated with increased polarization; however, we find no consistent long-term trend in polarization over time among the topics we study.

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arXiv2017

A Motif-based Approach for Identifying Controversy

Among the topics discussed in Social Media, some lead to controversy. A number of recent studies have focused on the problem of identifying controversy in social media mostly based on the analysis of textual content or rely on global network structure. Such approaches have strong limitations due to the difficulty of understanding natural language, and of investigating the global network structure. In this work we show that it is possible to detect controversy in social media by exploiting network motifs, i.e., local patterns of user interaction. The proposed approach allows for a language-independent and fine- grained and efficient-to-compute analysis of user discussions and their evolution over time. The supervised model exploiting motif patterns can achieve 85% accuracy, with an improvement of 7% compared to baseline structural, propagation-based and temporal network features.

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arXiv2017

Community-aware network sparsification

Network sparsification aims to reduce the number of edges of a network while maintaining its structural properties; such properties include shortest paths, cuts, spectral measures, or network modularity. Sparsification has multiple applications, such as, speeding up graph-mining algorithms, graph visualization, as well as identifying the important network edges. In this paper we consider a novel formulation of the network-sparsification problem. In addition to the network, we also consider as input a set of communities. The goal is to sparsify the network so as to preserve the network structure with respect to the given communities. We introduce two variants of the community-aware sparsification problem, leading to sparsifiers that satisfy different connectedness community properties. From the technical point of view, we prove hardness results and devise effective approximation algorithms. Our experimental results on a large collection of datasets demonstrate the effectiveness of our algorithms.

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arXiv2017

Finding low-tension communities

Motivated by applications that arise in online social media and collaboration networks, there has been a lot of work on community-search and team-formation problems. In the former class of problems, the goal is to find a subgraph that satisfies a certain connectivity requirement and contains a given collection of seed nodes. In the latter class of problems, on the other hand, the goal is to find individuals who collectively have the skills required for a task and form a connected subgraph with certain properties. In this paper, we extend both the community-search and the team-formation problems by associating each individual with a profile. The profile is a numeric score that quantifies the position of an individual with respect to a topic. We adopt a model where each individual starts with a latent profile and arrives to a conformed profile through a dynamic conformation process, which takes into account the individual's social interaction and the tendency to conform with one's social environment. In this framework, social tension arises from the differences between the conformed profiles of neighboring individuals as well as from differences between individuals' conformed and latent profiles. Given a network of individuals, their latent profiles and this conformation process, we extend the community-search and the team-formation problems by requiring the output subgraphs to have low social tension. From the technical point of view, we study the complexity of these problems and propose algorithms for solving them effectively. Our experimental evaluation in a number of social networks reveals the efficacy and efficiency of our methods.

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arXiv2016

Semi-supervised Kernel Metric Learning Using Relative Comparisons

We consider the problem of metric learning subject to a set of constraints on relative-distance comparisons between the data items. Such constraints are meant to reflect side-information that is not expressed directly in the feature vectors of the data items. The relative-distance constraints used in this work are particularly effective in expressing structures at finer level of detail than must-link (ML) and cannot-link (CL) constraints, which are most commonly used for semi-supervised clustering. Relative-distance constraints are thus useful in settings where providing an ML or a CL constraint is difficult because the granularity of the true clustering is unknown. Our main contribution is an efficient algorithm for learning a kernel matrix using the log determinant divergence --- a variant of the Bregman divergence --- subject to a set of relative-distance constraints. The learned kernel matrix can then be employed by many different kernel methods in a wide range of applications. In our experimental evaluations, we consider a semi-supervised clustering setting and show empirically that kernels found by our algorithm yield clusterings of higher quality than existing approaches that either use ML/CL constraints or a different means to implement the supervision using relative comparisons.

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arXiv2016

Discovering topically- and temporally-coherent events in interaction networks

With the increasing use of online communication platforms, such as email, twitter, and messaging applications, we are faced with a growing amount of data that combine content (what is said), time (when), and user (by whom) information. An important computational challenge is to analyze these data, discover meaningful patterns, and understand what is happening. We consider the problem of mining online communication data and finding top-k temporal events. We define a temporal event to be a coherent topic that is discussed frequently, in a relatively short time span, while the information ow of the event respects the underlying network structure. We construct our model for detecting temporal events in two steps. We first introduce the notion of interaction meta-graph, which connects associated interactions. Using this notion, we define a temporal event to be a subset of interactions that (i) are topically and temporally close and (ii) correspond to a tree that captures the information ow. Finding the best temporal event leads to budget version of the prize-collecting Steiner-tree (PCST) problem, which we solve using three different methods: a greedy approach, a dynamic-programming algorithm, and an adaptation to an existing approximation algorithm. The problem of finding the top- k events among a set of candidate events maps to maximum set-cover problem, and thus, solved by greedy. We compare and analyze our algorithms in both synthetic and real datasets, such as twitter and email communication. The results show that our methods are able to detect meaningful temporal events.

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arXiv2016

DopeLearning: A Computational Approach to Rap Lyrics Generation

Writing rap lyrics requires both creativity to construct a meaningful, interesting story and lyrical skills to produce complex rhyme patterns, which form the cornerstone of good flow. We present a rap lyrics generation method that captures both of these aspects. First, we develop a prediction model to identify the next line of existing lyrics from a set of candidate next lines. This model is based on two machine-learning techniques: the RankSVM algorithm and a deep neural network model with a novel structure. Results show that the prediction model can identify the true next line among 299 randomly selected lines with an accuracy of 17%, i.e., over 50 times more likely than by random. Second, we employ the prediction model to combine lines from existing songs, producing lyrics with rhyme and a meaning. An evaluation of the produced lyrics shows that in terms of quantitative rhyme density, the method outperforms the best human rappers by 21%. The rap lyrics generator has been deployed as an online tool called DeepBeat, and the performance of the tool has been assessed by analyzing its usage logs. This analysis shows that machine-learned rankings correlate with user preferences.

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arXiv2016

Streaming Similarity Self-Join

We introduce and study the problem of computing the similarity self-join in a streaming context (SSSJ), where the input is an unbounded stream of items arriving continuously. The goal is to find all pairs of items in the stream whose similarity is greater than a given threshold. The simplest formulation of the problem requires unbounded memory, and thus, it is intractable. To make the problem feasible, we introduce the notion of time-dependent similarity: the similarity of two items decreases with the difference in their arrival time. By leveraging the properties of this time-dependent similarity function, we design two algorithmic frameworks to solve the sssj problem. The first one, MiniBatch (MB), uses existing index-based filtering techniques for the static version of the problem, and combines them in a pipeline. The second framework, Streaming (STR), adds time filtering to the existing indexes, and integrates new time-based bounds deeply in the working of the algorithms. We also introduce a new indexing technique (L2), which is based on an existing state-of-the-art indexing technique (L2AP), but is optimized for the streaming case. Extensive experiments show that the STR algorithm, when instantiated with the L2 index, is the most scalable option across a wide array of datasets and parameters.

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arXiv2015

Exploring Controversy in Twitter

Among the topics discussed on social media, some spark more heated debate than others. For example, experience suggests that major political events, such as a vote for healthcare law in the US, would spark more debate between opposing sides than other events, such as a concert of a popular music band. Exploring the topics of discussion on Twitter and understanding which ones are controversial is extremely useful for a variety of purposes, such as for journalists to understand what issues divide the public, or for social scientists to understand how controversy is manifested in social interactions. The system we present processes the daily trending topics discussed on the platform, and assigns to each topic a controversy score, which is computed based on the interactions among Twitter users, and a visualization of these interactions, which provides an intuitive visual cue regarding the controversy of the topic. The system also allows users to explore the messages (tweets) associated with each topic, and sort and explore the topics by different criteria (e.g., by controversy score, time, or related keywords).

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arXiv2015

Absorbing random-walk centrality: Theory and algorithms

We study a new notion of graph centrality based on absorbing random walks. Given a graph $G=(V,E)$ and a set of query nodes $Q\subseteq V$, we aim to identify the $k$ most central nodes in $G$ with respect to $Q$. Specifically, we consider central nodes to be absorbing for random walks that start at the query nodes $Q$. The goal is to find the set of $k$ central nodes that minimizes the expected length of a random walk until absorption. The proposed measure, which we call $k$ absorbing random-walk centrality, favors diverse sets, as it is beneficial to place the $k$ absorbing nodes in different parts of the graph so as to "intercept" random walks that start from different query nodes. Although similar problem definitions have been considered in the literature, e.g., in information-retrieval settings where the goal is to diversify web-search results, in this paper we study the problem formally and prove some of its properties. We show that the problem is NP-hard, while the objective function is monotone and supermodular, implying that a greedy algorithm provides solutions with an approximation guarantee. On the other hand, the greedy algorithm involves expensive matrix operations that make it prohibitive to employ on large datasets. To confront this challenge, we develop more efficient algorithms based on spectral clustering and on personalized PageRank.

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arXiv2015

Discovering the Network Backbone from Traffic Activity Data

We introduce a new computational problem, the BackboneDiscovery problem, which encapsulates both functional and structural aspects of network analysis. While the topology of a typical road network has been available for a long time (e.g., through maps), it is only recently that fine-granularity functional (activity and usage) information about the network (like source-destination traffic information) is being collected and is readily available. The combination of functional and structural information provides an efficient way to explore and understand usage patterns of networks and aid in design and decision making. We propose efficient algorithms for the BackboneDiscovery problem including a novel use of edge centrality. We observe that for many real world networks, our algorithm produces a backbone with a small subset of the edges that support a large percentage of the network activity.

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