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检索条件"任意字段=5th ACM International Conference on Web Search and Data Mining, WSDM 2012"

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Fake News: Fundamental theories, Detection Strategies and Challenges 19

Fake News: Fundamental Theories, Detection Strategies and Ch...

引用

12th acm international conference on web search and data mining (wsdm)

作者： Zhou, Xinyi Zafarani, Reza Shu, Kai Liu, Huan Syracuse Univ EECS Dept Data Lab Syracuse NY 13244 USA Arizona State Univ Comp Sci & Engn Tempe AZ USA

ISBN: (纸本)9781450359405

the explosive growth of fake news and its erosion to democracy, justice, and public trust increased the demand for fake news detection. As an interdisciplinary topic, the study of fake news encourages a concerted effort of experts in computer and information science, political science, journalism, social science, psychology, and economics. A comprehensive framework to systematically understand and detect fake news is necessary to attract and unite researchers in related areas to conduct research on fake news. this tutorial aims to clearly present (1) fake news research, its challenges, and research directions;(2) a comparison between fake news and other related concepts (e.g., rumours);(3) the fundamental theories developed across various disciplines that facilitate interdisciplinary research;(4) various detection strategies unified under a comprehensive framework for fake news detection;and (5) the state-of-the-art datasets, patterns, and models. We present fake news detection from various perspectives, which involve news content and information in social networks, and broadly adopt techniques in data mining, machine learning, natural language processing, information retrieval and social search. Facing the upcoming 2020 U.S. presidential election, challenges for automatic, effective and efficient fake news detection are also clarified in this tutorial.

关键词： Fake news fake news detection news verification

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Adversarial immunization for Certifiable Robustness on Graphs 21

Adversarial immunization for Certifiable Robustness on Graph...

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14th acm international conference on web search and data mining (wsdm)

作者： Tao, Shuchang Shen, Huawei Cao, Qi Hou, Liang Cheng, Xueqi Chinese Acad Sci CAS Key Lab Network Data Sci & Technol Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781450382977

Despite achieving strong performance in semi-supervised node classification task, graph neural networks (GNNs) are vulnerable to adversarial attacks, similar to other deep learning models. Existing researches focus on developing either robust GNN models or attack detection methods against adversarial attacks on graphs. However, little research attention is paid to the potential and practice of immunization to adversarial attacks on graphs. In this paper, we propose and formulate the graph adversarial immunization problem, i.e., vaccinating an affordable fraction of node pairs, connected or unconnected, to improve the certifiable robustness of graph against any admissible adversarial attack. We further propose an effective algorithm, called AdvImmune, which optimizes with meta-gradient in a discrete way to circumvent the computationally expensive combinatorial optimization when solving the adversarial immunization problem. Experiments are conducted on two citation networks and one social network. Experimental results demonstrate that the proposed AdvImmune method remarkably improves the ratio of robust nodes by 12%, 42%, 65%, with an affordable immune budget of only 5% edges.

关键词： Adversarial immunization Graph neural networks Adversarial attack Node classification Certifiable robustness

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wsdm'15 Workshop Summary/Scalable data Analytics: theory and Applications 15

WSDM'15 Workshop Summary/Scalable Data Analytics: Theory and...

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8th acm international conference on web search and data mining (wsdm)

作者： Huang, Kaizhu Yang, Haiqin King, Irwin Lyu, Michael R. Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Renai Rd 111 Suzhou Jiangsu Peoples R China Chinese Univ Hong Kong SZRI Shenzhen Key Lab Rich Media Big Data Analyt & App Hong Kong Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China

ISBN: (纸本)9781450333177

the SDA workshop at wsdm 2015 is the fifth international Workshop on Scalable data Analytics, following the previous four workshops of SDA respectively held at IEEE Big data 2013, PAKDD 2014, IEEE Big data 2014, and IEEE ICDM 2014. this series of workshops aims to provide professionals, researchers, and technologists with a single forum where they can discuss and share the state-of-the-art theories and applications of scalable data analytics technologies. In particular, in the era of information explosion, the scientific, biomedical, and engineering research communities are undergoing a profound transformation where discoveries and innovations increasingly rely on massive amounts of data. the characteristics of volume, velocity, variety and veracity originated in the massive big data then bring challenges to current data analytics techniques. the focus of the fifth SDA is to discuss how we can scale up data analytics techniques for modeling and analyzing big data from various domains.

关键词： Big data Scalable data Analytics

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Deep Bayesian data mining 20

Deep Bayesian Data Mining

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13th Annual acm international conference on web search and data mining (wsdm)

作者： Chien, Jen-Tzung Natl Chiao Tung Univ Hsinchu Taiwan

ISBN: (纸本)9781450368223

this tutorial addresses the fundamentals and advances in deep Bayesian mining and learning for natural language with ubiquitous applications ranging from speech recognition [7, 55] to document summarization [8], text classification [5, 75], text segmentation [18], information extraction [50], image caption generation [69, 72], sentence generation [25, 46], dialogue control [22, 76], sentiment classification, recommendation system, question answering [58] and machine translation [2], to name a few. Traditionally, "deep learning" is taken to be a learning process where the inference or optimization is based on the real-valued deterministic model. the "semantic structure" in words, sentences, entities, actions and documents drawn from a large vocabulary may not be well expressed or correctly optimized in mathematical logic or computer programs. the "distribution function" in discrete or continuous latent variable model for natural language may not be properly decomposed or estimated. this tutorial addresses the fundamentals of statistical models and neural networks, and focus on a series of advanced Bayesian models and deep models including hierarchical Dirichlet process [61], Chinese restaurant process [4], hierarchical Pitman-Yor process [60], Indian buffet process [35], recurrent neural network (RNN) [26, 41, 48, 65], long short-term memory, sequence-to-sequence model [59], variational auto-encoder (VAE) [44], generative adversarial network (GAN) [36], attention mechanism [27, 56], memory-augmented neural network [39, 58], skip neural network [6], temporal difference VAE [40], stochastic neural network [3, 47], stochastic temporal convolutional network [1], predictive state neural network [31], and policy neural network [49, 74]. Enhancing the prior/posterior representation is addressed [53, 62]. We present how these models are connected and why they work for a variety of applications on symbolic and complex patterns in natural language. the variational inference an

关键词： deep learning Bayesian learning data mining information retrieval natural language processing

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A Self-Adapting Latency/Power Tradeoff Model for Replicated search Engines 14

A Self-Adapting Latency/Power Tradeoff Model for Replicated ...

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7th acm international conference on web search and data mining (wsdm)

作者： Freire, Ana Macdonald, Craig Tonellotto, Nicola Ounis, Iadh Cacheda, Fidel Univ A Coruna Campus Elvina S-N La Coruna 15017 Spain Univ Glasgow Glasgow G12 8QQ Lanark Scotland Natl Res Council Italy I-56124 Pisa Italy

ISBN: (纸本)9781450323512

For many search settings, distributed/replicated search engines deploy a large number of machines to ensure efficient retrieval. this paper investigates how the power consumption of a replicated search engine can be automatically reduced when the system has low contention, without compromising its efficiency. We propose a novel self -adapting model to analyse the trade-off between latency and power consumption for distributed search engines. When query volumes are high and there is contention for the resources, the model automatically increases the necessary number of active machines in the system to maintain acceptable query response times. On the other hand, when the load of the system is low and the queries can be served easily, the model is able to reduce the number of active machines, leading to power savings. the model bases its decisions on examining the current and historical query loads of the search engine. Our proposal is formulated as a general dynamic decision problem, which can be quickly solved by dynamic programming in response to changing query loads. thorough experiments are conducted to validate the usefulness of the proposed adaptive model using historical web search traffic submitted to a commercial search engine. Our results show that our proposed self -adapting model can achieve an energy saving of 33% while only degrading mean query completion time by 10 ms compared to a baseline that provisions replicas based on a previous day's traffic.

关键词： search Engines Power Consumption

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ConsistSum: Unsupervised Opinion Summarization with the Consistency of Aspect, Sentiment and Semantic 22

ConsistSum: Unsupervised Opinion Summarization with the Cons...

引用

15th acm international conference on web search and data mining (wsdm)

作者： Ke, Wenjun Gao, Jinhua Shen, Huawei Cheng, Xueqi Chinese Acad Sci Inst Comp Technol Data Intelligence Syst Res Ctr Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781450391320

Unsupervised opinion summarization techniques are designed to condense the review data and summarize informative and salient opinions in the absence of golden references. Existing dominant methods generally follow a two-stage framework: first creating the synthetic "review-summary" paired datasets and then feeding them into the generative summary model for supervised training. However, these methods mainly focus on semantic similarity in synthetic dataset creation, ignoring the consistency of aspects and sentiments in synthetic pairs. Such inconsistency also brings a gap to the training and inference of the summarization model. To alleviate this problem, we propose ConsistSum, an unsupervised opinion summarization method devoting to capture the consistency of aspects and sentiment between reviews and summaries. Specifically, ConsistSum first extracts the preliminary "review-summary" pairs from the raw corpus by evaluating the distance of aspect distribution and sentiment distribution. then, we refine the preliminary summary with the constrained Metropolis-Hastings sampling to produce highly consistent synthetic datasets. In the summarization phase, we adopt the generative model T5 as the summarization model. T5 is fine-tuned for the opinion summarization task by incorporating the loss of predicting aspect and opinion distribution. Experimental results on two benchmark datasets, i.e.., Yelp and Amazon, demonstrate the superior performance of ConsistSum over the state-of-the-art baselines.

关键词： opinion summarization unsupervised method consistency enhancement

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AUTOMATIC GENERATION OF CONCEPT TAXONOMIES FROM web search data USING SUPPORT VECTOR MACHINE

AUTOMATIC GENERATION OF CONCEPT TAXONOMIES FROM WEB SEARCH D...

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5th international conference on web Information Systems and Technologies

作者： Damasevicius, Robertas Kaunas Univ Technol Software Engn Dept LT-51368 Kaunas Lithuania

ISBN: (纸本)9789898111814

Ontologies and concept taxonomies are essential parts of the Semantic web infrastructure. Since manual construction of taxonomies requires considerable efforts, automated methods for taxonomy construction should be considered. In this paper, an approach for automatic derivation of concept taxonomies from web search results is presented. the method is based on generating derivative features from web search data and applying the machine learning techniques. the Support Vector Machine (SVM) classifier is trained with known concept hyponym-hypernym pairs and the obtained classification model is used to predict new hyponymy (is-a) relations. Prediction results are used to generate concept taxonomies in OWL. the results of the application of the approach for constructing colour taxonomy are presented.

关键词： Taxonomy learning web mining data mining Machine learning Support Vector Machine

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Partitioning and Segment Organization Strategies for Real-Time Selective search on Document Streams 17

Partitioning and Segment Organization Strategies for Real-Ti...

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10th acm international conference on web search and data mining (wsdm)

作者： Wang, Yulu Lin, Jimmy Univ Maryland Dept Comp Sci College Pk MD 20742 USA Univ Waterloo David R Cheriton Sch Comp Sci Waterloo ON Canada

ISBN: (纸本)9781450346757

the basic idea behind selective search is to partition a collection into topical clusters, and for each query, consider only a subset of the clusters that are likely to contain relevant documents. Previous work on web collections has shown that it is possible to retain high-quality results while considering only a small fraction of the collection. these studies, however, assume static collections where it is feasible to run batch clustering algorithms for partitioning. In this work, we consider the novel formulation of selective search on document streams (specifically, tweets), where partitioning must be performed incrementally. In our approach, documents are partitioned into temporal segments and selective search is performed within each segment: these segments can either be clustered using batch or online algorithms, and at different temporal granularities. For efficiency, we take advantage of word embeddings to reduce the dimensionality of the document vectors. Experiments with test collections from the TREC Microblog Tracks show that we are able to achieve precision indistinguishable from exhaustive search while considering only around 5% of the collection. Interestingly, we observe no significant effectiveness differences between batch vs. online clustering and between hourly vs. daily temporal segments, despite them being very different index organizations. this suggests that architectural choices should be primarily guided by efficiency considerations.

关键词： tweets effectiveness-efficiency tradeoffs clustering

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IMPROVING web search BY EXPLOITING search LOGS

IMPROVING WEB SEARCH BY EXPLOITING SEARCH LOGS

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5th international conference on web Information Systems and Technologies

作者： Ma, Hongyan Florida State Univ Coll Informat Tallahassee FL 32306 USA

ISBN: (纸本)9789898111814

With the increased use of web search engines, acute needs evolve for more adaptive and more personalizable Information Retrieval (IR) systems. this study proposes an innovative probabilistic method exploiting search logs to gather useful data about contexts and users to support adaptive retrieval. Real users' search logs from an operational web search engine, Infocious, were processed to obtain past queries and click-through data for adaptive indexing and unified probabilistic retrieval. An empirical experiment of retrieval effectiveness was conducted. the results demonstrate that the log-based probabilistic system yields statistically superior performance over the baseline system.

关键词： web searching Relevance feedback Probabilistic model Log mining

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searchable web sites recommendation 11

Searchable web sites recommendation

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Proceedings of the fourth acm international conference on web search and data mining

作者： Song, Yang Nguyen, Nam He, Li-Wei Imig, Scott Rounthwaite, Robert Microsoft Research One Microsoft Way Redmond WA United States

ISBN: (纸本)9781450304931

In this paper, we propose a new framework for searchable web sites recommendation. Given a query, our system will recommend a list of searchable web sites ranked by relevance, which can be used to complement the web page results and ads from a search engine. We model the conditional probability of a searchable web site being relevant to a given query in term of three main components: the language model of the query, the language model of the content within the web site, and the reputation of the web site searching capability (static rank). the language models for queries and searchable sites are built using information mined from client-side browsing logs. the static rank for each searchable site leverages features extracted from these client-side logs such as number of queries that are submitted to this site, and features extracted from general search engines such as the number of web pages that indexed for this site, number of clicks per query, and the dwell-time that a user spends on the search result page and on the clicked result web pages. We also learn a weight for each kind of feature to optimize the ranking performance. In our experiment, we discover 10.5 thousand searchable sites and use 5 million unique queries, extracted from one week of log data to build and demonstrate the effectiveness of our searchable web site recommendation system. Copyright 2011 acm.

关键词： websites

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