OpenRaiser/Intern-Atlas huggingface dataset:
Semantics-Driven Implicit Aspect Detection in Consumer Reviews
Authors: Kim Schouten, Nienke de Boer, Tjian Lam, Marijtje van Leeuwen, Ruud van Luijk, Flavius Frasincar
Venue: WWW
Year: 2015

With consumer reviews becoming a mainstream part of e-commerce, a good method of detecting the product or service aspects that are discussed is desirable. This work focuses on detecting aspects that are not literally mentioned in the text, or implicit aspects. To this end, a co-occurrence matrix of synsets from WordNet and implicit aspects is constructed. The semantic relations that exist between synsets in WordNet are exploited to enrich the co-occurrence matrix with more contextual information. Comparing this method with a similar method which is not semantics-driven clearly shows the benefit of the proposed method. Especially corpora of limited size seem to benefit from the added semantic context.
Short-Text Clustering using Statistical Semantics
Authors: Sepideh Seifzadeh, Ahmed K. Farahat, Mohamed S. Kamel, Fakhri Karray
Venue: WWW
Year: 2015

Short documents are typically represented by very sparse vectors, in the space of terms. In this case, traditional techniques for calculating text similarity results in measures which are very close to zero, since documents even the very similar ones have a very few or mostly no terms in common. In order to alleviate this limitation, the representation of short-text segments should be enriched by incorporating information about correlation between terms. In other words, if two short segments do not have any common words, but terms from the first segment appear frequently with terms from the second segment in other documents, this means that these segments are semantically related, and their similarity measure should be high. Towards achieving this goal, we employ a method for enhancing document clustering using statistical semantics. However, the problem of high computation time arises when calculating correlation between all terms. In this work, we propose the selection of a few terms, and using these terms with the Nystr\"om method to approximate the term-term correlation matrix. The selection of the terms for the Nystr\"om method is performed by randomly sampling terms with probabilities proportional to the lengths of their vectors in the document space. This allows more important terms to have more influence on the approximation of the term-term correlation matrix and accordingly achieves better accuracy.
Bringing CUPID Indoor Positioning System to Practice
Authors: Souvik Sen, Dongho Kim, Stephane Laroche, Kyu-Han Kim, Jeongkeun Lee
Venue: WWW
Year: 2015

WiFi based indoor positioning has recently gained more attention due to the advent of the IEEE 802.11v standard, requirements by the FCC for E911 calls, and increased interest in location-based services. While there exist several indoor localization techniques, we find that these techniques tradeoff either accuracy, scalability, pervasiveness or cost -- all of which are important requirements for a truly deployable positioning solution. Wireless signal-strength based approaches suffer from location errors, whereas time-of-flight (ToF) based solutions provide good accuracy but are not scalable. Recent solutions address these issues by augmenting WiFi with either smartphone sensing or mobile crowdsourcing. However, they require tight coupling between WiFi infrastructure and a client device, or they can determine the client's location only if it is mobile. In this paper, we present CUPID2.0 which improved our previously proposed CUPID indoor positioning system to overcome these limitations. We achieve this by addressing the fundamental limitations in Time-of-Flight based localization and combining ToF with signal strength to address scalability. Experiments from $6$ cities using $40$ different mobile devices, comprising of more than $2.5$ million location fixes demonstrate feasibility. CUPID2.0 is currently under production, and we expect CUPID2.0 to ignite the wide adoption of WLAN-based positioning systems and their services.
Early Detection of Spam Mobile Apps
Authors: Suranga Seneviratne, Aruna Seneviratne, Mohamed Ali Kâafar, Anirban Mahanti, Prasant Mohapatra
Venue: WWW
Year: 2015

Increased popularity of smartphones has attracted a large number of developers to various smartphone platforms. As a result, app markets are also populated with spam apps, which reduce the users' quality of experience and increase the workload of app market operators. Apps can be "spammy" in multiple ways including not having a specific functionality, unrelated app description or unrelated keywords and publishing similar apps several times and across diverse categories. Market operators maintain anti-spam policies and apps are removed through continuous human intervention. Through a systematic crawl of a popular app market and by identifying a set of removed apps, we propose a method to detect spam apps solely using app metadata available at the time of publication. We first propose a methodology to manually label a sample of removed apps, according to a set of checkpoint heuristics that reveal the reasons behind removal. This analysis suggests that approximately 35% of the apps being removed are very likely to be spam apps. We then map the identified heuristics to several quantifiable features and show how distinguishing these features are for spam apps. Finally, we build an Adaptive Boost classifier for early identification of spam apps using only the metadata of the apps. Our classifier achieves an accuracy over 95% with precision varying between 85%-95% and recall varying between 38%-98%. By applying the classifier on a set of apps present at the app market during our crawl, we estimate that at least 2.7% of them are spam apps.
A Novel Agent-Based Rumor Spreading Model in Twitter
Authors: Emilio Serrano, Carlos Angel Iglesias, Mercedes Garijo
Venue: WWW
Year: 2015

Viral marketing, marketing techniques that use pre-existing social networks, has experienced a significant encouragement in the last years. In this scope, Twitter is the most studied social network in viral marketing and the rumor spread is a widely researched problem. This paper contributes with a (1) novel agent-based social simulation model for rumors spread in Twitter. This model relies on the hypothesis that (2) when a user is recovered, this user will not influence his or her neighbors in the social network to recover. To support this hypothesis: (3) two Twitter rumor datasets are studied; (4) a baseline model which does not include the hypothesis is revised, reproduced, and implemented; (5) and a number of experiments are conducted comparing the real data with the two models results.
Disassortative Degree Mixing and Information Diffusion for Overlapping Community Detection in Social Networks (DMID)
Authors: Mohsen Shahriari, Sebastian Krott, Ralf Klamma
Venue: WWW
Year: 2015

In this paper we propose a new two-phase algorithm for overlapping community detection (OCD) in social networks. In the first phase, called disassortative degree mixing, we identify nodes with high degrees through a random walk process on the row-normalized disassortative matrix representation of the network. In the second phase, we calculate how closely each node of the network is bound to the leaders via a cascading process called network coordination game. We implemented the algorithm and four additional ones as a Web service on a federated peer-to-peer infrastructure. Comparative test results for small and big real world networks demonstrated the correct identification of leaders, high precision and good time complexity. The Web service is available as open source software.
Measuring and Characterizing Nutritional Information of Food and Ingestion Content in Instagram
Authors: Sanket S. Sharma, Munmun De Choudhury
Venue: WWW
Year: 2015

Social media sites like Instagram have emerged as popular platforms for sharing ingestion and dining experiences. However research on characterizing the nutritional information embedded in such content is limited. In this paper, we develop a computational method to extract nutritional information, specifically calorific content from Instagram food posts. Next, we explore how the community reacts specifically to healthy versus non-healthy food postings. Based on a crowdsourced approach, our method was found to detect calorific content in posts with 89% accuracy. We further show the use of Instagram as a platform where sharing of moderately healthy food content is common, and such content also receives the most support from the community.
N-gram IDF: A Global Term Weighting Scheme Based on Information Distance
Authors: Masumi Shirakawa, Takahiro Hara, Shojiro Nishio
Venue: WWW
Year: 2015

This paper first reveals the relationship between Inverse Document Frequency (IDF), a global term weighting scheme, and information distance, a universal metric defined by Kolmogorov complexity. We concretely give a theoretical explanation that the IDF of a term is equal to the distance between the term and the empty string in the space of information distance in which the Kolmogorov complexity is approximated using Web documents and the Shannon-Fano coding. Based on our findings, we propose N-gram IDF, a theoretical extension of IDF for handling words and phrases of any length. By comparing weights among N-grams of any N, N-gram IDF enables us to determine dominant N-grams among overlapping ones and extract key terms of any length from texts without using any NLP techniques. To efficiently compute the weight for all possible N-grams, we adopt two string processing techniques, i.e., maximal substring extraction using enhanced suffix array and document listing using wavelet tree. We conducted experiments on key term extraction and Web search query segmentation, and found that N-gram IDF was competitive with state-of-the-art methods that were designed for each application using additional resources and efforts. The results exemplified the potential of N-gram IDF.
Query Suggestion and Data Fusion in Contextual Disambiguation
Authors: Milad Shokouhi, Marc Sloan, Paul N. Bennett, Kevyn Collins-Thompson, Siranush Sarkizova
Venue: WWW
Year: 2015

Queries issued to a search engine are often under-specified or ambiguous. The user's search context or background may provide information that disambiguates their information need in order to automatically predict and issue a more effective query. The disambiguation can take place at different stages of the retrieval process. For instance, contextual query suggestions may be computed and recommended to users on the result page when appropriate, an approach that does not require modifying the original query's results. Alternatively, the search engine can attempt to provide efficient access to new relevant documents by injecting these documents directly into search results based on the user's context.
HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web
Authors: Philipp Singer, Denis Helic, Andreas Hotho, Markus Strohmaier
Venue: WWW
Year: 2015

When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful for e.g., improving underlying network structures, predicting user clicks or enhancing recommendations. In this work, we present a general approach called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our approach utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to leverage the sensitivity of Bayes factors on the prior for comparing hypotheses with each other. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method. We demonstrate the general mechanics and applicability of HypTrails by performing experiments with (i) synthetic trails for which we control the mechanisms that have produced them and (ii) empirical trails stemming from different domains including website navigation, business reviews and online music played. Our work expands the repertoire of methods available for studying human trails on the Web.
Helping Users Understand Their Web Footprints
Authors: Lisa Singh, Grace Hui Yang, Micah Sherr, Yifang Wei, Andrew Hian-Cheong, Kevin Tian, Janet Zhu, Sicong Zhang, Tavish Vaidya, Elchin Asgarli
Venue: WWW
Year: 2015

To help users better understand the potential risks associated with publishing data publicly, and the types of data that can be inferred by combining data from multiple online sources, we introduce a novel information exposure detection framework that generates and analyzes the web footprints users leave across the social web. We propose to use probabilistic operators, free text attribute extraction, and a population-based inference engine to generate the web footprints. Evaluation over public profiles from multiple sites shows that our framework successfully detects and quantifies information exposure using a small amount of non-sensitive initial knowledge.
An Overview of Microsoft Academic Service (MAS) and Applications
Authors: Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June Paul Hsu, Kuansan Wang
Venue: WWW
Year: 2015

In this paper we describe a new release of a Web scale entity graph that serves as the backbone of Microsoft Academic Service (MAS), a major production effort with a broadened scope to the namesake vertical search engine that has been publicly available since 2008 as a research prototype. At the core of MAS is a heterogeneous entity graph comprised of six types of entities that model the scholarly activities: field of study, author, institution, paper, venue, and event. In addition to obtaining these entities from the publisher feeds as in the previous effort, we in this version include data mining results from the Web index and an in-house knowledge base from Bing, a major commercial search engine. As a result of the Bing integration, the new MAS graph sees significant increase in size, with fresh information streaming in automatically following their discoveries by the search engine. In addition, the rich entity relations included in the knowledge base provide additional signals to disambiguate and enrich the entities within and beyond the academic domain. The number of papers indexed by MAS, for instance, has grown from low tens of millions to 83 million while maintaining an above 95% accuracy based on test data sets derived from academic activities at Microsoft Research. Based on the data set, we demonstrate two scenarios in this work: a knowledge driven, highly interactive dialog that seamlessly combines reactive search and proactive suggestion experience, and a proactive heterogeneous entity recommendation.
Exploiting Collective Hidden Structures in Webpage Titles for Open Domain Entity Extraction
Authors: Wei Song, Shiqi Zhao, Chao Zhang, Hua Wu, Haifeng Wang, Lizhen Liu, Hanshi Wang
Venue: WWW
Year: 2015

We present a novel method for open domain named entity extraction by exploiting the collective hidden structures in webpage titles. Our method uncovers the hidden textual structures shared by sets of webpage titles based on generalized URL patterns and a multiple sequence alignment technique. The highlights of our method include: 1) The boundaries of entities can be identified automatically in a collective way without any manually designed pattern, seed or class name. 2) The connections between entities are also discovered naturally based on the hidden structures, which makes it easy to incorporate distant or weak supervision. The experiments show that our method can harvest large scale of open domain entities with high precision. A large ratio of the extracted entities are long-tailed and complex and cover diverse topics. Given the extracted entities and their connections, we further show the effectiveness of our method in a weakly supervised setting. Our method can produce better domain specific entities in both precision and recall compared with the state-of-the-art approaches.
Large Scale Network Analytics with SNAP: Tutorial at the World Wide Web 2015 Conference
Authors: Rok Sosic, Jure Leskovec
Venue: WWW
Year: 2015

Many techniques for the modeling, analysis and optimization of Web related datasets are based on studies of large scale networks, where a network can contain hundreds of millions of nodes and billions of edges. Network analysis tools must provide not only extensive functionality, but also high performance in processing these large networks. The tutorial will present Stanford Network Analysis Platform (SNAP), a general purpose, high performance system for analysis and manipulation of large networks. SNAP is being used widely in studies of the Web datasets. SNAP consists of open source software, which provides a rich set of functions for performing network analytics, and a popular repository of publicly available real world network datasets. SNAP software APIs are available in Python and C++.
Terms in Time and Times in Context: A Graph-based Term-Time Ranking Model
Authors: Andreas Spitz, Jannik Strötgen, Thomas Bögel, Michael Gertz
Venue: WWW
Year: 2015

Approaches in support of the extraction and exploration of temporal information in documents provide an important ingredient in many of today's frameworks for text analysis. Methods range from basic techniques, primarily the extraction of temporal expressions and events from documents, to more sophisticated approaches such as ranking of documents with respect to their temporal relevance to some query term or the construction of timelines. Almost all of these approaches operate on the document level, that is, for a collection of documents a timeline is extracted or a ranked list of documents is returned for a temporal query term. In this paper, we present an approach to characterize individual dates, which can be of different granularities, and terms. Given a query date, a ranked list of terms is determined that are highly relevant for that date and best summarize the date. Analogously, for a query term, a ranked list of dates is determined that best characterize the term. Focusing on just dates and single terms as they occur in documents provides a fine-grained query and exploration method for document collections. Our approach is based on a weighted bipartite graph representing the co-occurrences of time expressions and terms in a collection of documents. We present different measures to obtain a ranked list of dates and terms for a query term and date, respectively. Our experiments and evaluation using Wikipedia as a document collection show that our approach provides an effective means in support of date and temporal term summarization.
AdAlyze Redux: Post-Click and Post-Conversion Text Feature Attribution for Sponsored Search Ads
Authors: Thomas Steiner
Venue: WWW
Year: 2015

In this paper, we present our ongoing research on an ads quality testing tool that we call AdAlyze Redux. This tool allows advertisers to get individual best practice recommendations based on an expandable set of textual ads features, tailored to exactly the ads in an advertiser's set of accounts. This lets them optimize their ad copies against the common online advertising key performance indicators clickthrough rate and, if available, conversion rate. We choose the Web as the tool's platform and automatically generate the analyses as platform-independent HTML5 slides and full reports.
Random Walk TripleRush: Asynchronous Graph Querying and Sampling
Authors: Philip Stutz, Bibek Paudel, Mihaela Verman, Abraham Bernstein
Venue: WWW
Year: 2015

Most Semantic Web applications rely on querying graphs, typically by using SPARQL with a triple store. Increasingly, applications also analyze properties of the graph structure to compute statistical inferences. The current Semantic Web infrastructure, however, does not efficiently support such operations. This forces developers to extract the relevant data for external statistical post-processing. In this paper we propose to rethink query execution in a triple store as a highly parallelized asynchronous graph exploration on an active index data structure. This approach also allows to integrate SPARQL-querying with the sampling of graph properties.
Open Domain Question Answering via Semantic Enrichment
Authors: Huan Sun, Hao Ma, Wen-tau Yih, Chen-Tse Tsai, Jingjing Liu, Ming-Wei Chang
Venue: WWW
Year: 2015

Most recent question answering (QA) systems query large-scale knowledge bases (KBs) to answer a question, after parsing and transforming natural language questions to KBs-executable forms (e.g., logical forms). As a well-known fact, KBs are far from complete, so that information required to answer questions may not always exist in KBs. In this paper, we develop a new QA system that mines answers directly from the Web, and meanwhile employs KBs as a significant auxiliary to further boost the QA performance. Specifically, to the best of our knowledge, we make the first attempt to link answer candidates to entities in Freebase, during answer candidate generation. Several remarkable advantages follow: (1) Redundancy among answer candidates is automatically reduced. (2) The types of an answer candidate can be effortlessly determined by those of its corresponding entity in Freebase. (3) Capitalizing on the rich information about entities in Freebase, we can develop semantic features for each answer candidate after linking them to Freebase. Particularly, we construct answer-type related features with two novel probabilistic models, which directly evaluate the appropriateness of an answer candidate's types under a given question. Overall, such semantic features turn out to play significant roles in determining the true answers from the large answer candidate pool. The experimental results show that across two testing datasets, our QA system achieves an 18%~54% improvement under F_1 metric, compared with various existing QA systems.
Combining Automatic and Manual Approaches: Towards a Framework for Discovering Themes in Disaster-related Tweets
Authors: Leif Romeritch Syliongka, Alron Jan Lam, Cheryll Ruth Soriano, Ma. Divina Gracia Roldan, Francisco Magno, Charibeth Ko Cheng, Nathaniel Oco
Venue: WWW
Year: 2015

In this paper, we present a framework that combines automatic and manual approaches to discover themes in disaster-related tweets. As case study, we decided to focus on tweets related to typhoon Haiyan, which caused billions of dollars in damages. We collected tweets from November 2013 to March 2014 and used the local typhoon name "Yolanda" as the filter. Data association was used to expand the tweet set and k-means clustering was then applied. Clusters with high number of instances were subjected to open coding for labeling. The Silhouette indices ranged from 0.27 to 0.50. Analyses reveal that the use of automated Natural Language Processing (NLP) approach has the potential to deal with huge volumes of tweets by clustering frequently occurring words and phrases. This complements the manual approach to surface themes from a more manageable set of tweet pool, allowing for a more nuanced analysis of tweets from a human expert. As application, the themes identified during open coding were used as labels to train a classifier system. Future work could explore on using topic models and focusing on specific content or issues, such as natural calamities and citizen's participation in addressing these.