OpenRaiser/Intern-Atlas huggingface dataset:
Collaborative adaptive case management with linked data
Authors: Sebastian Heil, Stefan Wild, Martin Gaedke
Venue: WWW
Year: 2014

An increasing share of today's work is knowledge work. Adaptive Case Management (ACM) assists knowledge workers in handling this collaborative, emergent and unpredictable type of work. Finding suitable workers for specific functions still relies on manual assessment and assignment by persons in charge, which does not scale well. In this paper we discuss a tool for ACM to facilitate this expert finding leveraging existing Web technology. We propose a method to automatically recommend a set of eligible workers utilizing linked data, enriched user profile data from distributed social networks and information gathered from case descriptions. This semantic recommendation method detects similarities between case requirements and worker profiles. The algorithm traverses distributed social graphs to retrieve a ranked list of suitable contributors to a case according to adaptable metrics. For this purpose, we introduce a vocabulary to specify case requirements and a vocabulary to describe skill sets and personal attributes of workers. The semantic recommendation method is demonstrated by a prototypical implementation using a WebID-based distributed social network.
Bing-SF-IDF+: semantics-driven news recommendation
Authors: Frederik Hogenboom, Michel Capelle, Marnix Moerland, Flavius Frasincar
Venue: WWW
Year: 2014

Content-based news recommendation is traditionally performed using the cosine similarity and TF-IDF weighting scheme for terms occurring in news messages and user profiles. Semantics-driven variants such as SF-IDF additionally take into account term meaning by exploiting synsets from semantic lexicons. However, they ignore the various semantic relationships between synsets, providing only for a limited understanding of news semantics. Moreover, semantics-based weighting techniques are not able to handle - often crucial - named entities, which are usually not present in semantic lexicons. Hence, we extend SF-IDF by also considering the synset semantic relationships, and by employing named entity similarities using Bing page counts. Our proposed method, Bing-SF-IDF+, outperforms TF-IDF and SF-IDF in terms of F1 scores and kappa statistics.
Inferring social relationships from mobile sensor data
Authors: Hsun-Ping Hsieh, Cheng-Te Li
Venue: WWW
Year: 2014

While mobile sensors are ubiquitous nowadays, the geographical activities of human beings are feasible to be collected and the geo-spatial interactions between people can be derived. As we know there is an underlying social network between mobile users, such social relationships are hidden and hold by service providers. Acquiring the social network over mobile users would enable lots of applications, such as friend recommendation and energy-saving mobile DB management. In this paper, we propose to infer the social relationships using the sensor data, which contains the encounter records between individuals, without any knowledge about the real friendships in prior. We propose a two-phase prediction method for the social inference. Experiments conducted on the CRAWDAD data demonstrate the encouraging results with satisfying prediction scores of precision and recall.
Inferring visiting time distributions of locations from incomplete check-in data
Authors: Hsun-Ping Hsieh, Cheng-Te Li
Venue: WWW
Year: 2014

Online location-based services, such as Foursquare and Facebook, provide a great resource for location recommendation. As we know the time is one of the important factors on recommending places with proper time for users, since the pleasure of visiting a place could be diminished if arriving at wrong time, we propose to infer the visiting time distributions of locations. We assume the check-in data used is incomplete because in real-world scenarios it is hard or unavailable to collect all the temporal information of locations and the check-in behaviors might be abnormal. To tackle such problem, we devise a visiting time inference framework, VisTime-Miner, which considers the route-based visiting correlation of time labels to model the visiting behavior of a location. Experiments on a large-scaled Gowalla check-in data show a promising result.
Machine learning in an auction environment
Authors: Patrick Hummel, R. Preston McAfee
Venue: WWW
Year: 2014

We consider a model of repeated online auctions in which an ad with an uncertain click-through rate faces a random distribution of competing bids in each auction and there is discounting of payoffs. We formulate the optimal solution to this explore/exploit problem as a dynamic programming problem and show that efficiency is maximized by making a bid for each advertiser equal to the advertiser's expected value for the advertising opportunity plus a term proportional to the variance in this value divided by the number of impressions the advertiser has received thus far. We then use this result to illustrate that the value of incorporating active exploration into a machine learning system in an auction environment is exceedingly small.
Data imputation using a trust network for recommendation
Authors: Won-Seok Hwang, Shaoyu Li, Sang-Wook Kim, Kichun Lee
Venue: WWW
Year: 2014

Recommendation methods suffer from the data sparsity and cold-start user problems, often resulting in low accuracy. To address these problems, we propose a novel imputation method, which effectively densifies a rating matrix by filling unevaluated ratings with probable values. In our method, we use a trust network to estimate the unevaluated ratings accurately. We conduct experiments on the Epinions dataset and demonstrate that our method helps provide better recommendation accuracy than previous methods, especially for cold-start users.
Advertising in a stream
Authors: Samuel Ieong, Mohammad Mahdian, Sergei Vassilvitskii
Venue: WWW
Year: 2014

One of the most important innovations of social networking websites is the notion of a "feed", a sequence of news items presented to the user as a stream that expands as the user scrolls down. The common method for monetizing such streams is to insert ads in between news items. In this paper, we model this setting, and observe that allocation and pricing of ad insertions in a stream poses interesting algorithmic and mechanism design challenges. In particular, we formulate an optimization problem that captures a typical stream ad placement setting. We give an approximation algorithm for this problem that provably achieves a value close to the optimal, and show how this algorithm can be turned into an incentive compatible mechanism. Finally, we conclude with a simple practical algorithm that makes the allocation decisions in an online fashion. We prove this algorithm to be approximately welfare-maximizing and show that it also has good incentive properties.
Tensor-based item recommendation using probabilistic ranking in social tagging systems
Authors: Noor Ifada, Richi Nayak
Venue: WWW
Year: 2014

A common problem with the use of tensor modeling in generating quality recommendations for large datasets is scalability. In this paper, we propose the Tensor-based Recommendation using Probabilistic Ranking method that generates the reconstructed tensor using block-striped parallel matrix multiplication and then probabilistically calculates the preferences of user to rank the recommended items. Empirical analysis on two real-world datasets shows that the proposed method is scalable for large tensor datasets and is able to outperform the benchmarking methods in terms of accuracy.
Volunteer-powered automatic classification of social media messages for public health in AIDR
Authors: Muhammad Imran, Carlos Castillo
Venue: WWW
Year: 2014

Microblogging platforms such as Twitter have become a valuable resource for disease surveillance and monitoring. Automatic classification can be used to detect disease-related messages and to sort them into meaningful categories. In this paper, we show how the AIDR (Artificial Intelligence for Disaster Response) platform can be used to harvest and perform analysis of tweets in real-time using supervised machine learning techniques. AIDR is a volunteer-powered online social media content classification platform that automatically learns from a set of human-annotated examples to classify tweets into user-defined categories. In addition, it automatically increases classification accuracy as new examples become available. AIDR can be operated through a web interface without the need to deal with the complexity of the machine learning methods used.
AIDR: artificial intelligence for disaster response
Authors: Muhammad Imran, Carlos Castillo, Ji Lucas, Patrick Meier, Sarah Vieweg
Venue: WWW
Year: 2014

We present AIDR (Artificial Intelligence for Disaster Response), a platform designed to perform automatic classification of crisis-related microblog communications. AIDR enables humans and machines to work together to apply human intelligence to large-scale data at high speed. The objective of AIDR is to classify messages that people post during disasters into a set of user-defined categories of information (e.g., "needs", "damage", etc.) For this purpose, the system continuously ingests data from Twitter, processes it (i.e., using machine learning classification techniques) and leverages human-participation (through crowdsourcing) in real-time. AIDR has been successfully tested to classify informative vs. non-informative tweets posted during the 2013 Pakistan Earthquake. Overall, we achieved a classification quality (measured using AUC) of 80%. AIDR is available at http://aidr.qcri.org/.
Alethiometer: a framework for assessing trustworthiness and content validity in social media
Authors: Eva Jaho, Efstratios Tzoannos, Aris Papadopoulos, Nikos Sarris
Venue: WWW
Year: 2014

There are both positive and negative aspects in the use of social media in news and information dissemination. To deal with the negative aspects, such as the spread of rumours and fake news, the flow of information should implicitly be filtered and marked to specific criteria such as credibility, trustworthiness, reputation, popularity, influence, and authenticity. This paper proposes an approach that can enhance trustworthiness and content validity in the presence of information overload. We introduce Alethiometer, a framework for assessing truthfulness in social media that can be used by professional and general news users alike. We present different measures that delve into the detailed analysis of the content, the contributors of the content and the underlying context. We further propose an approach for deriving a single metric that considers, in a unified manner, the quality of a contributor and of the content provided by that contributor. Finally, we present some preliminary statistical results from the examination of a set of 10 million twitter users, that provide useful insights on the characteristics of social media data.
Mal-netminer: malware classification based on social network analysis of call graph
Authors: Jae-wook Jang, Jiyoung Woo, Jaesung Yun, Huy Kang Kim
Venue: WWW
Year: 2014

In this work, we aim to classify malware using automatic classifiers by employing graph metrics commonly used in social network analysis. First, we make a malicious system call dictionary that consists of system calls found in malware. To analyze the general structural information of malware and measure the influence of system calls found in malware, we adopt social network analysis. Thus, we use social network metrics such as the degree distribution, degree centrality, and average distance, which are implicitly equivalent to distinct behavioral characteristics. Our experiments demonstrate that the proposed system performs well in classifying malware families within each malware class with accuracy greater than 98%. As exploiting the social network properties of system calls found in malware, our proposed method can not only classify the malware with fewer features than previous methods adopting graph features but also enables us to build a quick and simple detection system against malware.
Detecting suspicious following behavior in multimillion-node social networks
Authors: Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, Shiqiang Yang
Venue: WWW
Year: 2014

In a multimillion-node network of who-follows-whom like Twitter, since a high count of followers leads to higher profits, users have the incentive to boost their in-degree. Can we spot the suspicious following behavior, which may indicate zombie followers and suspicious followees? To answer the above question, we propose CatchSync, which exploits two tell-tale signs of the suspicious behavior: (a) synchronized behavior: the zombie followers have extremely similar following behavior pattern, because, say, they are generated by a script; and (b) abnormal behavior: their behavior pattern is very different from the majority. Our CatchSync introduces novel measures to quantify both concepts and catches the suspicious behavior. Moreover, we show it is effective in a real-world social network.
Fast topic discovery from web search streams
Authors: Di Jiang, Kenneth Wai-Ting Leung, Wilfred Ng
Venue: WWW
Year: 2014

Web search involves voluminous data streams that record millions of users' interactions with the search engine. Recently latent topics in web search data have been found to be critical for a wide range of search engine applications such as search personalization and search history warehousing. However, the existing methods usually discover latent topics from web search data in an offline and retrospective fashion. Hence, they are increasingly ineffective in the face of the ever-increasing web search data that accumulate in the format of online streams. In this paper, we propose a novel probabilistic topic model, the Web Search Stream Model (WSSM), which is delicately calibrated for handling two salient features of the web search data: it is in the format of streams and in massive volume. We further propose an efficient parameter inference method, the Stream Parameter Inference (SPI) to efficiently train WSSM with massive web search streams. Based on a large-scale search engine query log, we conduct extensive experiments to verify the effectiveness and efficiency of WSSM and SPI. We observe that WSSM together with SPI discovers latent topics from web search streams faster than the state-of-the-art methods while retaining a comparable topic modeling accuracy.
NTCIR temporalia: a test collection for temporal information access research
Authors: Hideo Joho, Adam Jatowt, Roi Blanco
Venue: WWW
Year: 2014

Time is one of the key constructs of information quality. Following an upsurge of research in temporal aspects of information search, it has become clear that the community needs standardized evaluation benchmark for fostering research in Temporal Information Access. This paper introduces Temporalia (Temporal Information Access), a new pilot task run at NTCIR-11 to create re-usable datasets for those who are interested in temporal aspects of search technologies, and discusses its task design in detail.
Learning from unstructured multimedia data
Authors: Janani Kalyanam, Gert R. G. Lanckriet
Venue: WWW
Year: 2014

Information in today's world is highly heterogeneous and unstructured. Learning and inferring from such data is challenging and is an active research topic. In this paper, we present and investigate an approach to learning from heterogeneous and unstructured multimedia data. Inspired by approaches in many fields including computer vision, we investigate a histogram based approach to represent multimodal unstructured data. While existing works have predominantly focused on histogram based approaches for unimodal data, we present a methodology to represent unstructured multimodal data. We explain how to discover the prototypical features or codewords over which these histograms are built. We present experimental results on classification and retrieval tasks performed on the histogram based representation.
Dynamic communities formation through semantic tags
Authors: Saman Kamran
Venue: WWW
Year: 2014

Taggers in social tagging systems have the main role in giving identities to the objects. Tagged objects also represent perception of their taggers about them and can define identities of their taggers in return. Consequently, identities that are assigned to the objects and taggers have effect on the quality of their categorization and communities formation around them. Tags in social semantic tagging systems have formal definitions because they are mapped to the concepts that are defined in ontologies. Semantic tags are not only able to improve quality of tag assignments by solving some common tags ambiguity problems related to classic folksonomy systems (i.e., in particular polysemy and synonymy), but also to provide some meta data on top of the social relations based on contribution of taggers around semantic tags. Those meta data may be exploited to form dynamic communities which addresses the problems of lack of commonly agreed and evolving meaning of tags in social semantic tagging systems. This paper proposes an approach to form dynamic communities of related taggers around the tagged objects. Because our perceptions in each specific area of knowledge is evolving over time, the goal of our approach is also to evolve the represented knowledge in semantic tagging systems dynamically according to the latest perception of the related users.
Hierarchical interest graph from tweets
Authors: Pavan Kapanipathi, Prateek Jain, Chitra Venkatramani, Amit P. Sheth
Venue: WWW
Year: 2014

Industry and researchers have identified numerous ways to monetize microblogs for personalization and recommendation. A common challenge across these different works is the identification of user interests. Although techniques have been developed to address this challenge, a flexible approach that spans multiple levels of granularity in user interests has not been forthcoming. In this work, we focus on exploiting hierarchical semantics of concepts to infer richer user interests expressed as a Hierarchical Interest Graph. To create such graphs, we utilize users' tweets to first ground potential user interests to structured background knowledge such as Wikipedia Category Graph. We then adapt spreading activation theory to assign user interest score to each category in the hierarchy. The Hierarchical Interest Graph not only comprises of users' explicitly mentioned interests determined from Twitter, but also their implicit interest categories inferred from the background knowledge source.
Strengthening collaborative data analysis and decision making in web communities
Authors: Nikos I. Karacapilidis, Spyros Christodoulou, Manolis Tzagarakis, Georgia Tsiliki, Costas Pappis
Venue: WWW
Year: 2014

Generally speaking, modern research becomes increasingly interdisciplinary and collaborative in nature. Researchers need to collaborate and make decisions by meaningfully assembling, mining and analyzing available large-scale volumes of complex multi-faceted data residing in different sources. At the same time, they need to efficiently and effectively exploit services available over the Web. Arguing that dealing with data-intensive and cognitively complex settings is not a technical problem alone, this paper presents a novel collaboration support platform for Web communities. The proposed solution adopts a hybrid approach that builds on the synergy between machine and human intelligence to facilitate the underlying sense-making and decision making processes. User experience shows that the platform enables stakeholders to make better, more informed and quicker decisions. The functionalities of the proposed platform are described through a real-world case from a biomedical research community.
Cognitive search intents hidden behind queries: a user study on query formulations
Authors: Makoto P. Kato, Takehiro Yamamoto, Hiroaki Ohshima, Katsumi Tanaka
Venue: WWW
Year: 2014

This study investigated query formulations by users with Cognitive Search Intents (CSI), which are needs for the cognitive characteristics of documents to be retrieved, eg. comprehensibility, subjectivity, and concreteness. We proposed an example-based method of specifying search intents to observe unbiased query formulations. Our user study revealed that about half our subjects did not input any keywords representing CSIs, even though they were conscious of given CSIs.
Haters gonna hate: job-related offenses in twitter
Authors: Ricardo Kawase, Patrick Siehndel, Eelco Herder
Venue: WWW
Year: 2014

In this paper, we aim at finding out which users are likely to publicly demonstrate frustration towards their jobs on the microblogging platform Twitter - we will call these users haters. We show that the profiles of haters have specific characteristics in terms of vocabulary and connections. The implications of these findings may be used for the development of an early alert system that can help users to think twice before they post potentially self-harming content.
Identifying spreaders of malicious behaviors in online games
Authors: Youngjoon Ki, Jiyoung Woo, Huy Kang Kim
Venue: WWW
Year: 2014

Massively multiplayer online role-playing games (MMORPGs) simulate the real world and require highly complex user interaction. Many human behaviors can be observed in MMORPGs, such as social interactions, economic behaviors, and malicious behaviors. In this study, we primarily focus on malicious behavior, especially cheating using game bots. Bots can be diffused on the social network in an epidemic style. When bots diffuse on the social network, a user's influence on the diffusion process varies owing to different characteristics and network positions. We aim to identify the influentials in the game world and investigate how they differ from normal users. Identifying the influentials in the diffusion of malicious behaviors will enable the game company to act proactively and preventively towards malicious users such as game bot users.
SocRoutes: safe routes based on tweet sentiments
Authors: Jaewoo Kim, Meeyoung Cha, Thomas Sandholm
Venue: WWW
Year: 2014

Location-based services, and in particular personal navigation systems, have become increasingly popular with the widespread use of GPS technology in smart devices. Existing navigation systems are designed to suggest routes based on the shortest distance or the fastest time to a target. In this paper, we propose a new type of route navigation based on regional context---primarily sentiments. Our system, called SocRoutes, aims to find a safer, friendlier, and more enjoyable route based on sentiments inferred from real-time, geotagged messages from Twitter. SocRoutes tailors routes by avoiding places with extremely negative sentiments, thereby potentially finding a safer and more enjoyable route with marginal increase in total distance compared to the shortest path. The system supports three types of traveling modes: walking, bicycling, and driving. We validated the idea based on crime history data from the City of Chicago Portal in December 2012, and sentiments extracted from geotagged tweets during the same time. We discovered that there was a significant correlation between regional Twitter posting sentiments and crime rate, in particular for high-crime and highly negative sentiment areas. We also demonstrated that SocRoutes, by solely utilizing social media sentiments, can find routes that bypass crime hotspots.