OpenRaiser/Intern-Atlas huggingface dataset:
Topic-based place semantics discovered from microblogging text messages
Authors: Eunyoung Kim, Hwon Ihm, Sung-Hyon Myaeng
Venue: WWW
Year: 2014

Location-based social network services (LBSNS) such as Foursquare are getting the highlight with the extensive spread of GPS-enabled mobile devices, and a large body of research has been conducted to devise methods for understanding and clustering places. However, in previous studies, the predefined set of semantic categories of places play a critical role in both discovery and evaluation of the results, despite its limited ability to represent the dynamics of the places. We explore beyond the predefined semantic categories of the places and discover topic-based place semantics through the use of Latent Dirichlet Allocation, by extracting topics from the text which people post on site. We also show the proposed method allows for understanding the temporal dynamics of the place semantics. The finding of this study is intended for, but not limited to, context aware services and place recommendation systems.
A computational analysis of agenda setting
Authors: Yeooul Kim, Suin Kim, Alejandro Jaimes, Alice Oh
Venue: WWW
Year: 2014

Agenda setting theory explains how media affects its audience. While traditional media studies have done extensive research on agenda setting, there are important limitations in those studies, including using a small set of issues, running costly surveys of public interest, and manually categorizing the articles into positive and negative frames. In this paper, we propose to tackle these limitations with a computational approach and a large dataset of online news. Overall, we demonstrate how to carry out a large-scale computational research of agenda setting with online news data using machine learning.
Investigating socio-cultural behavior of users reflected in different social channels on K-pop
Authors: Yonghwan Kim, Dahee Lee, Jung Eun Hahm, Namgi Han, Min Song
Venue: WWW
Year: 2014

In this paper we investigated the socio-cultural behavior of users reflected in the two different social media channels, YouTube and Twitter. We conducted the comparative analysis of the networks generated from the two channels. The relationship we set for each network is the relatedness on YouTube and the co-links on Twitter. From the results, we revealed that the social media influenced the distinct socio-cultural behaviors of their users. Specifically, Twitter network better showed the actual consumption of contents in the field of the k-pop culture than YouTube. From this study, we contributed to offer a novel approach for exploring the socio-cultural behavior of users on the social media.
Finding informative Q&As on twitter
Authors: Kanghak Kim, Sunho Lee, Jeonghoon Son, Meeyoung Cha
Venue: WWW
Year: 2014

Question & Answer (Q&A) behaviors on social media have huge potential as a rich source of information and knowledge online. However, little is known about how much diversity there exists in the topics covered in such Q&As and whether unstructured social media data can be made searchable. This paper seeks the feasibility of utilizing social media data for developing a Q&A service by examining the topic coverage in Twitter conversations. We propose a new framework to automatically extract informative Q&A content using machine learning techniques.
Trends of news diffusion in social media based on crowd phenomena
Authors: Minkyoung Kim, David Newth, Peter Christen
Venue: WWW
Year: 2014

Information spreads across social media, bringing heterogeneous social networks interconnected and diffusion patterns varied in different topics of information. Studying such cross-population diffusion in various context helps us understand trends of information diffusion in a more accurate and consistent way. In this study, we focus on real-world news diffusion across online social systems such as mainstream news (News), social networking sites (SNS), and blogs (Blog), and we analyze behavioral patterns of the systems in terms of activity, reactivity, and heterogeneity. We found that News is the most active, SNS is the most reactive, and Blog is the most persistent, which governs time-evolving heterogeneity of these systems. Finally, we interpret the discovered crowd phenomena from various angles using our previous model-free and model-driven approaches, showing that the strength and directionality of influence reflect the behavioral patterns of the systems in news diffusion.
Motives for mass interactions in online sports viewing
Authors: Minsam Ko, Seung-Woo Choi, Joonwon Lee, Subin Yang, Uichin Lee, Aviv Segev, Junehwa Song
Venue: WWW
Year: 2014

Recent advances of social TV services allow sports fans to watch sports games at any place and to lively interact with other fans via online chatting. Despite their popularity, however, so far little is known about the key properties of such mass interactions in online sports viewing. In this paper, we explore motives for mass interactions in online sports viewing and investigate how the motives are related to viewing and chatting behaviors, by studying Naver Sports, the largest online sports viewing service in Korea.
Semantically enhanced keyword search for smartphones
Authors: Jihoon Ko, Sangjin Shin, Sungkwang Eom, Minjae Song, Dong-Hoon Shin, Kyong-Ho Lee, Yongil Jang
Venue: WWW
Year: 2014

To apply semantic search to smartphones, we propose an efficient semantic search method based on a lightweight mobile ontology. Through a prototype implementation of a semantic search engine on an android smartphone, experimental results show that the proposed method provides more accurate search results and a better user experience compared to the conventional method.
How placing limitations on the size of personal networks changes the structural properties of complex networks
Authors: Somayeh Koohborfardhaghighi, Jörn Altmann
Venue: WWW
Year: 2014

People-to-people interactions in the real world and in virtual environments (e.g., Facebook) can be represented through complex networks. Changes of the structural properties of these complex networks are caused through a variety of dynamic processes. While accepting the fact that variability in individual patterns of behavior (i.e., establishment of random or FOAF-type potential links) in social environments might lead to an increase or decrease in the structural properties of a complex network, in this paper, we focus on another factor that may contribute to such changes, namely the size of personal networks. Any personal network comes with the cost of maintaining individual connections. Despite the fact that technology has shrunk our world, there is also a limit to how many close friends one can keep and count on. It is a relatively small number. In this paper, we develop a multi-agent based model to capture, compare, and explain the structural changes within a growing social network (e.g., expanding the social relations beyond one's social circles). We aim to show that, in addition to various dynamic processes of human interactions, limitations on the size of personal networks can also lead to changes in the structural properties of networks (i.e., the average shortest-path length). Our simulation result shows that the famous small world theory of interconnectivity holds true or even can be shrunk, if people manage to utilize all their existing connections to reach other parties. In addition to this, it can clearly be observed that the network's average path length has a significantly smaller value, if the size of personal networks is set to larger values in our network growth model. Therefore, limitations on the size of personal networks in network growth models lead to an increase in the network's average path length.
Integration and visualization public health dashboard: the medi+board pilot project
Authors: Patty Kostkova, Stephan J. Garbin, Justin Moser, Wendy Pan
Venue: WWW
Year: 2014

Traditional public health surveillance systems would benefit from integration with knowledge created by new situation-aware realtime signals from social media, online searches, mobile/sensor networks and citizens' participatory surveillance systems. However, the challenge of threat validation, cross-verification and information integration for risk assessment has so far been largely untackled. In this paper, we propose a new system, medi+board, monitoring epidemic intelligence sources and traditional case-based surveillance to better automate early warning, cross-validation of signals for outbreak detection and visualization of results on an interactive dashboard. This enables public health professionals to see all essential information at a glance. Modular and configurable to any 'event' defined by public health experts, medi+board scans multiple data sources, detects changing patterns and uses a configurable analysis module for signal detection to identify a threat. These can be validated by an analysis module and correlated with other sources to assess the reliability of the event classified as the reliability coefficient which is a real number between zero and one. Events are reported and visualized on the medi+board dashboard which integrates all information sources and can be navigated by a timescale widget. Simulation with three datasets from the swine flu 2009 pandemic (HPA surveillance, Google news, Twitter) demonstrates the potential of medi+board to automate data processing and visualization to assist public health experts in decision making on control and response measures.
The mobile semantic web
Authors: Shonali Krishnaswamy, Yuan-Fang Li
Venue: WWW
Year: 2014

The combination of the versatility of smart devices and the capabilities of semantic technologies forms a great foundation for a mobile Semantic Web that will contribute to further realising the true potential of both disciplines. Motivated by a service discovery and matchmaking example, this tutorial provides an overview of background knowledge in ontology languages, basic reasoning problems, and how they are applicable in the mobile environment. It aims at presenting a timely survey of state-of-the-art development and challenges on mobile ontology reasoning, focusing on the reasoning and optimization techniques developed in the mTableaux framework. Finally, the tutorial closes with a summary of important research problems and an outlook of future research directions in this area.
Enhancing media enrichment by semantic extraction
Authors: Michael Krug, Fabian Wiedemann, Martin Gaedke
Venue: WWW
Year: 2014

The opportunities of the Internet combined with new devices and technologies change the end users' habits in media consumption. While end users often search for related information to the currently watched TV show by themselves, we propose to improve this user experience by automatically enriching media using semantic extraction. In our recent work we focused on how to apply media enrichment to distributed screens. Based on the findings we made from our recent prototype we identify several problems and describe how we deal with them. We illustrate a way to achieve cross-platform real-time synchronization using several transport protocols. We propose the usage of sessions to handle multi-user, multi-screen scenarios and introduce techniques for new interaction and customization patterns. We extend our recent approach with the extraction of keywords from given subtitles by utilizing statistical algorithms and natural language processing technologies, which are then used to discover and display related content from the Web. The prototype presented in this paper reflects the improvements of our work. We discuss next research steps and define challenges for further research.
The market of internet sponsored links in the context of competition law: can modeling help?
Authors: Natalia Kudryashova
Venue: WWW
Year: 2014

Internet search market of key words attracts much attention in conjunction with the legal proceedings against Google. It has been recognized that legal argumentation alone may not be sufficient to disentangle the complexity of the case. An approach that includes mathematical modeling is needed, to distinguish the effects of the factors intrinsic to the market and the consequences of anticompetitive practices. This pa- per proposes a modeling framework based on explicit treatment of users' switching between the search platforms in the environment set by the platforms' strategic decisions, and demonstrates some of its applications.
Photo recall: using the internet to label your photos
Authors: Neeraj Kumar, Steven M. Seitz
Venue: WWW
Year: 2014

We describe a system for searching your personal photos using an extremely wide range of text queries, including dates and holidays ("Halloween"), named and categorical places ("Empire State Building" or "park"), events and occasions ("Radiohead concert" or "wedding"), activities ("skiing"), object categories ("whales"), attributes ("outdoors"), and object instances ("Mona Lisa"), and any combination of these -- all with no manual labeling required. We accomplish this by correlating information in your photos -- the timestamps, GPS locations, and image pixels -- to information mined from the Internet. This includes matching dates to holidays listed on Wikipedia, GPS coordinates to places listed on Wikimapia, places and dates to find named events using Google, visual categories using classifiers either pre-trained on ImageNet or trained on-the-fly using results from Google Image Search, and object instances using interest point-based matching, again using results from Google Images. We tie all of these disparate sources of information together in a unified way, allowing for fast and accurate searches using whatever information you remember about a photo.
The multi agent based information diffusion model for false rumordiffusion analysis
Authors: Satoshi Kurihara
Venue: WWW
Year: 2014

Twitter is a famous social networking service and has received attention recently. Twitter user have increased rapidly, and many users exchange information. When the East Japan great earthquake disaster occurred on March 11, 2011, many people could obtain important information from social networking service. Although twitter also played the important role a false rumor diffusion was pointed out. So, in this talk, I would like to focus on the false rumor diffusion phenomena, and introduce about our multi agent information diffusion model based on SIR model. And I would like to discuss about more rapid correction-tweet diffusion methodology.
EVIN: building a knowledge base of events
Authors: Erdal Kuzey, Gerhard Weikum
Venue: WWW
Year: 2014

We present EVIN: a system that extracts named events from news articles, reconciles them into canonicalized events, and organizes them into semantic classes to populate a knowledge base. EVIN exploits different kinds of similarity measures among news, referring to textual contents, entity occurrences, and temporal ordering. These similarities are captured in a multi-view attributed graph. To distill canonicalized events, EVIN coarsens the graph by iterative merging based on a judiciously designed loss function. To infer semantic classes of events, EVIN uses statistical language models. EVIN provides a GUI that allows users to query the constructed knowledge base of events, and to explore it in a visual manner.
Has much potential but biased: exploring the scholarly landscape in twitter
Authors: Haewoon Kwak, Jong Gun Lee
Venue: WWW
Year: 2014

We explore how research papers are shared in Twitter to understand its potential and limitation of the current practice that measures or predicts the scientific impact of research papers from the web. We track 54 second-level domains offering the top 100 journals listed in Google Scholar and collect 403,165 tweets sharing 75,677 unique research papers by 142,743 users over the course of 135 days. Our findings show the great potential of Twitter as a platform for paper sharing, but at the same time, indicate the limitations of measuring scientific impact through the lens of social media mainly due to the highly skewed and limited attention to few number of top journals.
A demonstration of query-oriented distribution and replication techniques for dynamic graph data
Authors: Alan G. Labouseur, Paul W. Olsen Jr., Kyuseo Park, Jeong-Hyon Hwang
Venue: WWW
Year: 2014

Evolving networks can be modeled as series of graphs that represent those networks at different points in time. Our G* system enables efficient storage and querying of these graph snapshots by taking advantage of their commonalities. In extending G* for scalable and robust operation, we found the classic challenges of data distribution and replication to be imbued with renewed significance. If multiple graph snapshots are commonly queried together, traditional techniques that distribute data over all servers or create identical data replicas result in inefficient query execution. We propose to verify, using live demonstrations, the benefits of our graph snapshot distribution and replication techniques. Our distribution technique adjusts the set of servers storing each graph snapshot in a manner optimized for popular queries. Our replication technique maintains each snapshot replica on a different number of servers, making available the most efficient replica configurations for different types of queries.
Towards a scalable social recommender engine for online marketplaces: the case of apache solr
Authors: Emanuel Lacic, Dominik Kowald, Denis Parra, Martin Kahr, Christoph Trattner
Venue: WWW
Year: 2014

Recent research has unveiled the importance of online social networks for improving the quality of recommenders in several domains, what has encouraged the research community to investigate ways to better exploit the social information for recommendations. However, there is a lack of work that offers details of frameworks that allow an easy integration of social data with traditional recommendation algorithms in order to yield a straight-forward and scalable implementation of new and existing systems. Furthermore, it is rare to find details of performance evaluations of recommender systems such as hardware and software specifications or benchmarking results of server loading tests. In this paper we intend to bridge this gap by presenting the details of a social recommender engine for online marketplaces built upon the well-known search engine Apache Solr. We describe our architecture and also share implementation details to facilitate the re-use of our approach by people implementing recommender systems. In addition, we evaluate our framework from two perspectives: (a) recommendation algorithms and data sources, and (b) system performance under server stress tests. Using a dataset from the SecondLife virtual world that has both trading and social interactions, we contribute to research in social recommenders by showing how certain social features allow to improve recommendations in online marketplaces. On the platform implementation side, our evaluation results can serve as a baseline to people searching for performance references in terms of scalability, model training and testing trade-offs, real-time server performance and the impact of model updates in a production system.
Language technologies for enhancement of teaching and learning in writing
Authors: Tak Pang Lau, Shuai Wang, Yuanyuan Man, Chi Fai Yuen, Irwin King
Venue: WWW
Year: 2014

Writing is a vital issue for education as well as a fundamental skill in teaching and learning. With the development of information technologies, more and more professional writing tools emerge. As each of them mostly concentrates on addressing a specific issue, people need a one-stop platform, which could integrate multiply functions. In addition, with the supported concept of e-learning ecosystem for future education, a comprehensive platform will be more promising. Therefore, we introduce VeriGuide Platform, which provides a professional writing toolbox to promote the enhancement of teaching and learning in writing. It contains six vertical components, which could be split into two groups. The first group, Editing Assistance, facilitates students write papers and point out grammar and spelling errors. While the second group, Text Analysis, offers document analysis results, which enables students to achieve further writing improvement with explanatory feedbacks. Furthermore, we could do education data analytics to enhance the efficiency of teaching and learning. Specifically, the Editing Assistance contains well-organized writing and formatting, and grammar and spelling checking, while readability assessment, similarity detection, citation analysis, and sentiment analysis are included in the Text Analysis.
Finding k-highest betweenness centrality vertices in graphs
Authors: Min-Joong Lee, Chin-Wan Chung
Venue: WWW
Year: 2014

The betweenness centrality is a measure for the relative participation of the vertex in the shortest paths in the graph. In many cases, we are interested in the k-highest betweenness centrality vertices only rather than all the vertices in a graph. In this paper, we study an efficient algorithm for finding the exact k-highest betweenness centrality vertices.
A new correlation-based information diffusion prediction
Authors: Jong-Ryul Lee, Chin-Wan Chung
Venue: WWW
Year: 2014

For predicting the diffusion process of information, we introduce and analyze a new correlation between the information adoptions of users sharing a friend in online social networks. Based on the correlation, we propose a probabilistic model to estimate the probability of a user's adoption using the naive Bayes classifier. Next, we build a recommendation method using the probabilistic model. Finally, we demonstrate the effectiveness of the proposed method with the data from Flickr and Movielens which are well-known web services. For all cases in the experiments, the proposed method is more accurate than comparison methods.
A fast approximation for influence maximization in large social networks
Authors: Jong-Ryul Lee, Chin-Wan Chung
Venue: WWW
Year: 2014

This paper deals with a novel research work about a new efficient approximation algorithm for influence maximization, which was introduced to maximize the benefit of viral marketing. For efficiency, we devise two ways of exploiting the 2-hop influence spread which is the influence spread on nodes within 2-hops away from nodes in a seed set. Firstly, we propose a new greedy method for the influence maximization problem using the 2-hop influence spread. Secondly, to speed up the new greedy method, we devise an effective way of removing unnecessary nodes for influence maximization based on optimal seed's local influence heuristics. In our experiments, we evaluate our method with real-life datasets, and compare it with recent existing methods. From experimental results, the proposed method is at least an order of magnitude faster than the existing methods in all cases while achieving similar accuracy.
Social spam, campaigns, misinformation and crowdturfing
Authors: Kyumin Lee, James Caverlee, Calton Pu
Venue: WWW
Year: 2014

This tutorial will introduce peer-reviewed research work on information quality on social systems. Specifically, we will address new threats such as social spam, campaigns, misinformation and crowdturfing, and overview modern techniques to improve information quality by revealing and detecting malicious participants (e.g., social spammers, content polluters and crowdturfers) and low quality contents.