OpenRaiser/Intern-Atlas huggingface dataset:
Predicting Pre-click Quality for Native Advertisements
Authors: Ke Zhou, Miriam Redi, Andrew Haines, Mounia Lalmas
Venue: WWW
Year: 2016

Native advertising is a specific form of online advertising where ads replicate the look-and-feel of their serving platform. In such context, providing a good user experience with the served ads is crucial to ensure long-term user engagement. In this work, we explore the notion of ad quality, namely the effectiveness of advertising from a user experience perspective. We design a learning framework to predict the pre-click quality of native ads. More specifically, we look at detecting offensive native ads, showing that, to quantify ad quality, ad offensive user feedback rates are more reliable than the commonly used click-through rate metrics. We then conduct a crowd-sourcing study to identify which criteria drive user preferences in native advertising. We translate these criteria into a set of ad quality features that we extract from the ad text, image and advertiser, and then use them to train a model able to identify offensive ads. We show that our model is very effective in detecting offensive ads, and provide in-depth insights on how different features affect ad quality. Finally, we deploy a preliminary version of such model and show its effectiveness in the reduction of the offensive ad feedback rate.
Tweet Properly: Analyzing Deleted Tweets to Understand and Identify Regrettable Ones
Authors: Lu Zhou, Wenbo Wang, Keke Chen
Venue: WWW
Year: 2016

Inappropriate tweets can cause severe damages on authors' reputation or privacy. However, many users do not realize the negative consequences until they publish these tweets. Published tweets have lasting effects that may not be eliminated by simple deletion because other users may have read them or third-party tweet analysis platforms have cached them. Regrettable tweets, i.e., tweets with identifiable regrettable contents, cause the most damage on their authors because other users can easily notice them. In this paper, we study how to identify the regrettable tweets published by \emph{normal individual users} via the contents and users' historical deletion patterns. We identify normal individual users based on their publishing, deleting, followers and friends statistics. We manually examine a set of randomly sampled deleted tweets from these users to identify regrettable tweets and understand the corresponding regrettable reasons. By applying content-based features and personalized history-based features, we develop classifiers that can effectively predict regrettable tweets.
Predicting the Link Strength of "Newborn" Links
Authors: Matteo Zignani, Sabrina Gaito, Gian Paolo Rossi
Venue: WWW
Year: 2016

Measurements of online social networks (OSNs) support the common fact that not all links carry the same social value, and that the strength of each link is strictly related to the frequency of interactions between the connected users. In this paper, we investigate the predictability of the interactions on OSN links by wondering if it is possible to categorize interactive or non-interactive links at their creation time. We turn the problem into a binary classification task and introduce a set of features which leverage the temporal and topological properties of the social and interaction networks, without requiring the knowledge of the interaction history of the link. The best classifier trained on a Facebook dataset obtained 0.72 as AUC. The above performance suggests that we can distinguish between interactive/non-interactive links at the time of link creation.
Research Collaborations in Multidisciplinary Institutions: a Case Study of iSchools
Authors: Zhiya Zuo, Xi Wang, David Eichmann, Kang Zhao
Venue: WWW
Year: 2016

Although closely related, multidisciplinarity and interdisciplinarity are different. The former indicates the co-existence of multiple disciplines while the latter is more about the integration among various areas. As collaboration between researchers from different areas is one of the major approaches for interdisciplinarity, this research investigated whether higher levels of multidisciplinarity in academic institutions are related to more collaborations, especially more interdisciplinary collaborations, among its faculty members. Using U.S. iSchools as a case study, we applied social network analysis and text mining techniques to faculty members' educational background and publication data, and proposed metrics for multidisciplinarity and collaboration interdisciplinarity. Our analysis results revealed that the multidisciplinarity of an iSchool is actually negatively correlated with the frequency and interdisciplinarity of research collaborations among its faculty members. This finding suggests that having a multidisciplinary environment alone is not sufficient to promote collaborations, nor interdisciplinary collaborations.
La Sécurité Ouverte How We Doin? So Far?
Authors: Mary Ellen Zurko
Venue: WWW
Year: 2016

Open has meant a lot of things in the web thus far. The openness of the web has had profound implications for web security, from the beginning through to today. Each time the underlying web technology changes, we do a reset on the security it provides. Patterns and differences emerge in each round of security responses and challenges. What has that brought us as web users, technologists, researchers, and as a global community? What can we expect going forward? And what should we work towards as web technologists and caretakers?
Caching with Dual Costs
Authors: Anirban Dasgupta, Ravi Kumar, Tamás Sarlós
Venue: WWW
Year: 2017

Caching mechanisms in distributed and social settings face the issue that the items can frequently change, requiring the cached versions to be updated to maintain coherence. There is thus a trade-off between incurring cache misses on read requests and cache hits on update requests. Motivated by this we consider the following dual cost variant of the classical caching problem: each request for an item can be either a read or a write. If the request is read and the item is not in the cache, then a read-miss cost is incurred and if the request is write and the item is in the cache, then a write-hit cost is incurred. The goal is to design a caching algorithm that minimizes the sum of read-miss and write-hit costs. We study online and offline algorithms for this problem.
Spatial Analysis of Social Media Response to Live Events: The Case of the Milano Fashion Week
Authors: Marco Brambilla, Stefano Ceri, Florian Daniel, Gianmarco Donetti
Venue: WWW
Year: 2017

Social media response to catastrophic events, such as natural disasters or terrorist attacks, has received a lot of attention. However, social media are also extremely important in the context of planned events, such as fairs, exhibits, festivals, as they play an essential role in communicating them to fans, interest groups, and the general population. These kinds of events are geo-localized within a city or territory and are scheduled within a public calendar. We consider a specific scenario, the Milano Fashion Week (MFW), which is an important event in our city.
Extracting Emerging Knowledge from Social Media
Authors: Marco Brambilla, Stefano Ceri, Emanuele Della Valle, Riccardo Volonterio, Felix Xavier Acero Salazar
Venue: WWW
Year: 2017

Massive data integration technologies have been recently used to produce very large ontologies. However, knowledge in the world continuously evolves, and ontologies are largely incomplete for what concerns low-frequency data, belonging to the so-called long tail. Socially produced content is an excellent source for discovering emerging knowledge: it is huge, and immediately reflects the relevant changes which hide emerging entities. Thus, we propose a method for discovering emerging entities by extracting them from social content.
Interplay between Social Influence and Network Centrality: A Comparative Study on Shapley Centrality and Single-Node-Influence Centrality
Authors: Wei Chen, Shang-Hua Teng
Venue: WWW
Year: 2017

We study network centrality based on dynamic influence propagation models in social networks. To illustrate our integrated mathematical-algorithmic approach for understanding the fundamental interplay between dynamic influence processes and static network structures, we focus on two basic centrality measures: (a) Single Node Influence (SNI) centrality, which measures each node's significance by its influence spread; and (b) Shapley Centrality, which uses the Shapley value of the influence spread function --- formulated based on a fundamental cooperative-game-theoretical concept --- to measure the significance of nodes. We present a comprehensive comparative study of these two centrality measures. Mathematically, we present axiomatic characterizations, which precisely capture the essence of these two centrality measures and their fundamental differences. Algorithmically, we provide scalable algorithms for approximating them for a large family of social-influence instances. Empirically, we demonstrate their similarity and differences in a number of real-world social networks, as well as the efficiency of our scalable algorithms. Our results shed light on their applicability: SNI centrality is suitable for assessing individual influence in isolation while Shapley centrality assesses individuals' performance in group influence settings.
Taming the Unpredictability of Cultural Markets with Social Influence
Authors: Andrés Abeliuk, Gerardo Berbeglia, Pascal Van Hentenryck, Tad Hogg, Kristina Lerman
Venue: WWW
Year: 2017

Unpredictability is often portrayed as an undesirable outcome of social influence in cultural markets. Unpredictability stems from the "rich get richer" effect, whereby small fluctuations in the market share or popularity of products are amplified over time by social influence. In this paper, we report results of an experimental study that shows that unpredictability is not an inherent property of social influence. We investigate strategies for creating markets in which the popularity of products is better-and more predictably-aligned with their underlying quality. For our study, we created a cultural market of science stories and conducted randomized experiments on different policies for presenting the stories to study participants. Specifically, we varied how the stories were ranked, and whether or not participants were shown the ratings these stories received from others. We present a policy that leverages social influence and product positioning to help distinguish the product's market share (popularity) from underlying quality. Highlighting products with the highest estimated quality reduces the "rich get richer" effect highlighting popular products. We show that this policy allows us to more robustly and predictably identify high quality products and promote blockbusters. The policy can be used to create more efficient online cultural markets with a better allocation of resources to products.
A Nonparametric Sequential Test for Online Randomized Experiments
Authors: Vineet Abhishek, Shie Mannor
Venue: WWW
Year: 2017

We propose a nonparametric sequential test that aims to address two practical problems pertinent to online randomized experiments: (i) how to do a hypothesis test for complex metrics; (ii) how to prevent type 1 error inflation under continuous monitoring. The proposed test does not require knowledge of the underlying probability distribution generating the data. We use the bootstrap to estimate the likelihood for blocks of data followed by mixture sequential probability ratio test. We validate this procedure on data from a major online e-commerce website. We show that the proposed test controls type 1 error at any time, has good power, is robust to misspecification in the distribution generating the data, and allows quick inference in online randomized experiments.
Automated Template Generation for Question Answering over Knowledge Graphs
Authors: Abdalghani Abujabal, Mohamed Yahya, Mirek Riedewald, Gerhard Weikum
Venue: WWW
Year: 2017

Templates are an important asset for question answering over knowledge graphs, simplifying the semantic parsing of input utterances and generating structured queries for interpretable answers. State-of-the-art methods rely on hand-crafted templates with limited coverage. This paper presents QUINT, a system that automatically learns utterance-query templates solely from user questions paired with their answers. Additionally, QUINT is able to harness language compositionality for answering complex questions without having any templates for the entire question. Experiments with different benchmarks demonstrate the high quality of QUINT.
Tiwiki: Searching Wikipedia with Temporal Constraints
Authors: Prabal Agarwal, Jannik Strötgen
Venue: WWW
Year: 2017

Temporal information retrieval received a lot of attention during the last years and it is, in the meantime, widely accepted in the IR community that temporal information needs are important to tackle. A particular type of temporal queries are those with explicit temporal constraints, which make almost 15% of today's Web search queries. Although several approaches to allow textual search combined with temporal constraints regarding the content of the documents have been suggested, there are no publicly available search engines allowing for a time-centric search experience.
Exploring Rated Datasets with Rating Maps
Authors: Sihem Amer-Yahia, Sofia Kleisarchaki, Naresh Kumar Kolloju, Laks V. S. Lakshmanan, Ruben H. Zamar
Venue: WWW
Year: 2017

Online rated datasets have become a source for large-scale population studies for analysts and a means for end-users to achieve routine tasks such as finding a book club. Existing systems however only provide limited insights into the opinions of different segments of the rater population. In this paper, we develop a framework for finding and exploring population segments and their opinions. We propose rating maps, a collection of (population segment, rating distribution) pairs, where a segment, e.g., {18-29 year old males in CA} has a rating distribution in the form of a histogram that aggregates its ratings for a set of items (e.g., movies starring Russel Crowe). We formalize the problem of building rating maps dynamically given desired input distributions. Our problem raises two challenges: (i) the choice of an appropriate measure for comparing rating distributions, and (ii) the design of efficient algorithms to find segments. We show that the Earth Mover's Distance (EMD) is well-adapted to comparing rating distributions and prove that finding segments whose rating distribution is close to input ones is NP-complete. We propose an efficient algorithm for building Partition Decision Trees and heuristics for combining the resulting partitions to further improve their quality. Our experiments on real and synthetic datasets validate the utility of rating maps for both analysts and end-users.
All Dates Lead to Rome: Extracting and Explaining Temporal References in Street Names
Authors: Rosita Andrade, Jannik Strötgen
Venue: WWW
Year: 2017

Street names say a lot about a country's or region's identity. So far, they have mostly been analyzed manually and for very limited regions (e.g., a city), and hardly any large-scale studies have been performed automatically. A phenomenon not yet studied are street names with date references. These are of special interest as they can be used to commemorate important events in a region's history. In this paper, we present our approach to automatically extract such street names across the world. We analyze the dates' temporal and geographic distribution, and automatically gather potential explanations why specific dates occur in particular regions. We further describe date-Rome, a tool to interactively explore the streets, their distribution, and possible explanations.
Is Interaction More Important than Individual Performance?: A Study of Motifs in Wikia
Authors: Thomas Arnold, Johannes Daxenberger, Iryna Gurevych, Karsten Weihe
Venue: WWW
Year: 2017

Recent research has discovered the importance of informal roles in peer online collaboration. These roles reflect prototypical activity patterns of contributors such as different editing activities in writing communities. While previous work has analyzed the dynamics of contributors within single communities, so far, the relationship between individuals' roles and interaction among contributors remains unclear. This is a severe drawback given that collaboration is one of the driving forces in online communities. In this study, we use a network-based approach to combine information about individuals' roles and their interaction over time. We measure the impact of recurring subgraphs in co-author networks, so called motifs, on the overall quality of the resulting collaborative product. Doing so allows us to measure the effect of collaboration over mere isolated contributions by individuals. Our findings indicate that indeed there are consistent positive implications of certain patterns that cannot be detected when looking at contributions in isolation, e.g. we found shared positive effects of contributors that specialize on content quality over of quantity. The empirical results presented in this work are based on a study of several online writing communities, namely wikis from Wikia and Wikipedia.
Secure Centrality Computation Over Multiple Networks
Authors: Gilad Asharov, Francesco Bonchi, David García-Soriano, Tamir Tassa
Venue: WWW
Year: 2017

Consider a multi-layered graph, where the different layers correspond to different proprietary social networks on the same ground set of users. Suppose that the owners of the different networks (called hosts) are mutually non-trusting parties: how can they compute a centrality score for each of the users using all the layers, but without disclosing information about their private graphs?
A Cyberphysical Learning Approach for Digital Smart Citizenship Competence Development
Authors: Yacine Atif, Stylianos Sergis, Demetrios G. Sampson, Gunnar Mathiason
Venue: WWW
Year: 2017

Smart Cities have emerged as a global concept that argues for the effective exploitation of digital technologies to drive sustainable innovation and well-being for citizens. Despite the large investments being placed on Smart City infrastructure, however, there is still very scarce attention on the new learning approaches that will be needed for cultivating Digital Smart Citizenship competences, namely the competences which will be needed by the citizens and workforce of such cities for exploiting the digital technologies in creative and innovative ways for driving financial and societal sustainability. In this context, this paper introduces cyberphysical learning as an overarching model of cultivating Digital Smart Citizenship competences by exploiting the potential of Internet of Things technologies and social media, in order to create authentic blended and augmented learning experiences.
Hybrid Crowdsensing: A Novel Paradigm to Combine the Strengths of Opportunistic and Participatory Crowdsensing
Authors: Marco Avvenuti, Salvatore Bellomo, Stefano Cresci, Mariantonietta Noemi La Polla, Maurizio Tesconi
Venue: WWW
Year: 2017

Crowdsensing systems can be either participatory or opportunistic, depending on whether the user intentionally contributes data, or she simply acts as the bearer of a sensing device from which data is transparently collected. In this paper, we propose hybrid crowdsensing, a social media-based paradigm which aims at combining the strengths of both participatory and opportunistic crowdsensing. With hybrid crowdsensing, possibly relevant data is collected via an opportunistic approach. Then, users that spontaneously contributed are directly contacted and asked to provide additional information following a participatory approach. To demonstrate its feasibility and usefulness, we experimented the proposed paradigm for involving Twitter users in an emergency relief scenario. For each of the two real-world experiments we analyze the answer ratio to our questions, their time distribution, and responders' willingness to collaborate. Results support the adoption of hybrid crowdsensing, especially in those practical scenarios where users are emotionally involved.
Evaluating the Impact of Articles with Geographical Distances between Institutions
Authors: Xiaomei Bai, Jie Hou, Hongzhuang Du, Xiangjie Kong, Feng Xia
Venue: WWW
Year: 2017

Evaluating the impact of scholarly papers plays an important role for addressing recruitment decision, funding allocation and promotion, etc. Yet little is known how actual geographic distance influences the impact of scholarly papers. In this paper, we leverage the law of geographic distance and citations between different institutions to weight quantum Pagerank algorithm for objectively measuring the impact of scholarly papers. The results indicate that the weighted quantum PageRank algorithm can better differentiate the impact of scholarly papers compared to PageRank algorithm.
Transparent Web Service Auditing via Network Provenance Functions
Authors: Adam Bates, Wajih Ul Hassan, Kevin R. B. Butler, Alin Dobra, Bradley Reaves, Patrick T. Cable II, Thomas Moyer, Nabil Schear
Venue: WWW
Year: 2017

Detecting and explaining the nature of attacks in distributed web services is often difficult -- determining the nature of suspicious activity requires following the trail of an attacker through a chain of heterogeneous software components including load balancers, proxies, worker nodes, and storage services. Unfortunately, existing forensic solutions cannot provide the necessary context to link events across complex workflows, particularly in instances where application layer semantics (e.g., SQL queries, RPCs) are needed to understand the attack. In this work, we present a transparent provenance-based approach for auditing web services through the introduction of Network Provenance Functions (NPFs). NPFs are a distributed architecture for capturing detailed data provenance for web service components, leveraging the key insight that mediation of an application's protocols can be used to infer its activities without requiring invasive instrumentation or developer cooperation. We design and implement NPF with consideration for the complexity of modern cloud-based web services, and evaluate our architecture against a variety of applications including DVDStore, RUBiS, and WikiBench to show that our system imposes as little as 9.3% average end-to-end overhead on connections for realistic workloads. Finally, we consider several scenarios in which our system can be used to concisely explain attacks. NPF thus enables the hassle-free deployment of semantically rich provenance-based auditing for complex applications workflows in the Cloud.
From Phoenix Wright to Atticus Finch: Legal Simulation Games as an Aid to Self-Represented Litigants
Authors: Scott Beattie, Stephen Colbran
Venue: WWW
Year: 2017

This paper examines the feasibility of a courtroom simulation being used to assist self-represented litigants in learning basic advocacy skills. A review of legal and forensic mechanics in games, particularly the Phoenix Wright games, provides design roadmap for a courtroom learning simulation.