Modeling and predicting the popularity of online news based on temporal and content-related features

As the market of globally available online news is large and still growing, there is a strong competition between online publishers in order to reach the largest possible audience. Therefore an intelligent online publishing strategy is of the highest importance to publishers. A prerequisite for being able to optimize any online strategy, is to have trustworthy predictions of how popular new online content may become. This paper presents a novel methodology to model and predict the popularity of online news. We first introduce a new strategy and mathematical model to capture view patterns of online news. After a thorough analysis of such view patterns, we show that well-chosen base functions lead to suitable models, and show how the influence of day versus night on the total view patterns can be taken into account to further increase the accuracy, without leading to more complex models. Second, we turn to the prediction of future popularity, given recently published content. By means of a new real-world dataset, we show that the combination of features related to content, meta-data, and the temporal behavior leads to significantly improved predictions, compared to existing approaches which only consider features based on the historical popularity of the considered articles. Whereas traditionally linear regression is used for the application under study, we show that the more expressive gradient tree boosting method proves beneficial for predicting news popularity

Enriching ontological user profiles with tagging history for multi-domain recommendations

Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can be applied to support individuals in the discovery of items according to explicit and implicit user preferences. Recently, the rapid adoption of Web2.0, and the proliferation of social networking sites, has resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, the unification of personal information with ontologies using the contemporary knowledge representation methods often associated with Web2.0 applications, such as community tagging, is a non-trivial task. In this paper, we propose a method for the unification of tags with ontologies by grounding tags to a shared representation in the form of Wordnet and Wikipedia. We incorporate individuals' tagging history into their ontological profiles by matching tags with ontology concepts. This approach is preliminary evaluated by extending an existing news recommendation system with user tagging histories harvested from popular social networking sites

Develop Guidelines for Pavement Preservation Treatments and for Building a Pavement Preservation Program Platform for Alaska

INE/AUTC 12.0

Predicting the Outcomes of Important Events based on Social Media and Social Network Analysis

Twitter is a famous social network website that lets users post their opinions about current affairs, share their social events, and interact with others. It has now become one of the largest sources of news, with over 200 million active users monthly. It is possible to predict the outcomes of events based on social networks using machine learning and big data analytics. Massive data available from social networks can be utilized to improve prediction efficacy and accuracy. It is a challenging problem to achieve high accuracy in predicting the outcomes of political events using Twitter data. The focus of this thesis is to investigate novel approaches to predicting the outcomes of political events from social media and social networks. The first proposed method is to predict election results based on Twitter data analysis. The method extracts and analyses sentimental information from microblogs to predict the popularity of candidates. Experimental results have shown its advantages over the existing method for predicting outcomes of politic events. The second proposed method is to predict election results based on Twitter data analysis that analyses sentimental information using term weighting and selection to predict the popularity of candidates. Scaling factors are used for different types of terms, which help to select informative terms more effectively and achieve better prediction results than the previous method. The third method proposed in this thesis represents the social network by using network connectivity constructed based on retweet data and social media contents as well, leading to a new approach to predicting the outcome of political events. Two approaches, whole-network and sub-network, have been developed and compared. Experimental results show that the sub-network approach, which constructs sub-networks based on different topics, outperformed the whole-network approach

Sensing Social Media Signals for Cryptocurrency News

The ability to track and monitor relevant and important news in real-time is of crucial interest in multiple industrial sectors. In this work, we focus on the set of cryptocurrency news, which recently became of emerging interest to the general and financial audience. In order to track relevant news in real-time, we (i) match news from the web with tweets from social media, (ii) track their intraday tweet activity and (iii) explore different machine learning models for predicting the number of the article mentions on Twitter within the first 24 hours after its publication. We compare several machine learning models, such as linear extrapolation, linear and random forest autoregressive models, and a sequence-to-sequence neural network. We find that the random forest autoregressive model behaves comparably to more complex models in the majority of tasks.Comment: full version of the paper, that is accepted at ACM WWW '19 Conference, MSM'19 Worksho

History of the National Survey of Sexual Attitudes and Lifestyles

Annotated and edited transcript of a Witness Seminar held on 14 December 2009. Introduction by Professor Clive Seale, QMUL. First published by Queen Mary, University of London, 2011. ©The Trustee of the Wellcome Trust, London, 2011. All volumes are freely available online at www.history.qmul.ac.uk/research/modbiomed/Annotated and edited transcript of a Witness Seminar held on 14 December 2009. Introduction by Professor Clive Seale, QMULAnnotated and edited transcript of a Witness Seminar held on 14 December 2009. Introduction by Professor Clive Seale, QMULAnnotated and edited transcript of a Witness Seminar held on 14 December 2009. Introduction by Professor Clive Seale, QMULAnnotated and edited transcript of a Witness Seminar held on 14 December 2009. Introduction by Professor Clive Seale, QMULAnnotated and edited transcript of a Witness Seminar held on 14 December 2009. Introduction by Professor Clive Seale, QMULAnnotated and edited transcript of a Witness Seminar held on 14 December 2009. Introduction by Professor Clive Seale, QMULA National Survey of Sexual Attitudes and Lifestyles (NATSAL) was proposed in the mid-1980s. This was to provide data to help predict and prevent the transmission and spread of HIV, in response to the critical need for information on the AIDS epidemic. Set up by biomedical and social scientists, NATSAL-1 was carried out in 1990, and the results used for AIDS projections and the national HIV and sexual health strategy. Subsequent surveys (NATSAL-2 and -3) have followed in 2000 and 2010 extending the objectives to include other sexually transmitted infections such as Chlamydia and Human Papillomavirus. Introduced by Professor Clive Seale, this volume focusses primarily on NATSAL-1 and addresses the background to the survey, the methodology, the results, and the funding: its initial support by the Department of Health, the dramatic withdrawal of government funds and subsequent funding by the Wellcome Trust. Contributors include many of the key people involved in setting up the survey, experts in public and sexual health, individuals from the Wellcome Trust, interviewers, and the Sunday Times journalist who, in September 1989, reported Margaret Thatcher’s veto of Government support.The History of Modern Biomedicine Research Group is funded by the Wellcome Trust, which is a registered charity, no. 210183