Exploring the information behaviour of users of Welsh Newspapers Online through web log analysis

Purpose – Webometric techniques have been applied to many websites and online resources, especially since the launch of Google Analytics (GA). To date, though, there has been little consideration of information behaviour in relation to digitised newspaper collections. The purpose of this paper is to address a perceived gap in the literature by providing an account of user behaviour in the newly launched Welsh Newspapers Online (WNO). Design/methodology/approach – The author collected webometric data for WNO using GA and web server content logs. These were analysed to identify patterns of engagement and user behaviour, which were then considered in relation to existing information behaviour. Findings – Use of WNO, while reminiscent of archival information seeking, can be understood as centring on the web interface rather than the digitised material. In comparison to general web browsing, users are much more deeply engaged with the resource. This engagement incorporates reading online, but users’ information seeking utilises website search and browsing functionality rather than filtering in newspaper material. Information seeking in digitised newspapers resembles the model of the “user” more closely than that of the “reader”, a value-laden distinction which needs further unpacking. Research limitations/implications – While the behaviour discussed in this paper is likely to be more widely representative, a larger longitudinal data set would increase the study’s significance. Additionally, the methodology of this paper can only tell us what users are doing, and further research is needed to identify the drivers for this behaviour. Originality/value – This study provides important insights into the underinvestigated area of digitised newspaper collections, and shows the importance of webometric methods in analysing online user behaviour

Traffic event detection framework using social media

This is an accepted manuscript of an article published by IEEE in 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC) on 18/09/2017, available online: https://ieeexplore.ieee.org/document/8038595 The accepted version of the publication may differ from the final published version.© 2017 IEEE. Traffic incidents are one of the leading causes of non-recurrent traffic congestions. By detecting these incidents on time, traffic management agencies can activate strategies to ease congestion and travelers can plan their trip by taking into consideration these factors. In recent years, there has been an increasing interest in Twitter because of the real-time nature of its data. Twitter has been used as a way of predicting revenues, accidents, natural disasters, and traffic. This paper proposes a framework for the real-time detection of traffic events using Twitter data. The methodology consists of a text classification algorithm to identify traffic related tweets. These traffic messages are then geolocated and further classified into positive, negative, or neutral class using sentiment analysis. In addition, stress and relaxation strength detection is performed, with the purpose of further analyzing user emotions within the tweet. Future work will be carried out to implement the proposed framework in the West Midlands area, United Kingdom.Published versio

The applications of social media in sports marketing

n the era of big data, sports consumer's activities in social media become valuable assets to sports marketers. In this paper, the authors review extant literature regarding how to effectively use social media to promote sports as well as how to effectively analyze social media data to support business decisions. Methods: The literature review method. Results: Our findings suggest that sports marketers can use social media to achieve the following goals, such as facilitating marketing communication campaigns, adding values to sports products and services, creating a two-way communication between sports brands and consumers, supporting sports sponsorship program, and forging brand communities. As to how to effectively analyze social media data to support business decisions, extent literature suggests that sports marketers to undertake traffic and engagement analysis on their social media sites as well as to conduct sentiment analysis to probe customer's opinions. These insights can support various aspects of business decisions, such as marketing communication management, consumer's voice probing, and sales predictions. Conclusion: Social media are ubiquitous in the sports marketing and consumption practices. In the era of big data, these ＂footprints＂ can now be effectively analyzed to generate insights to support business decisions. Recommendations to both the sports marketing practices and research are also addressed

Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting

Modern urbanization is demanding smarter technologies to improve a variety of applications in intelligent transportation systems to relieve the increasing amount of vehicular traffic congestion and incidents. Existing incident detection techniques are limited to the use of sensors in the transportation network and hang on human-inputs. Despite of its data abundance, social media is not well-exploited in such context. In this paper, we develop an automated traffic alert system based on Natural Language Processing (NLP) that filters this flood of information and extract important traffic-related bullets. To this end, we employ the fine-tuning Bidirectional Encoder Representations from Transformers (BERT) language embedding model to filter the related traffic information from social media. Then, we apply a question-answering model to extract necessary information characterizing the report event such as its exact location, occurrence time, and nature of the events. We demonstrate the adopted NLP approaches outperform other existing approach and, after effectively training them, we focus on real-world situation and show how the developed approach can, in real-time, extract traffic-related information and automatically convert them into alerts for navigation assistance applications such as navigation apps.Comment: This paper is accepted for publication in IEEE Technology Engineering Management Society International Conference (TEMSCON'20), Metro Detroit, Michigan (USA

Blindspot: Indistinguishable Anonymous Communications

Communication anonymity is a key requirement for individuals under targeted surveillance. Practical anonymous communications also require indistinguishability - an adversary should be unable to distinguish between anonymised and non-anonymised traffic for a given user. We propose Blindspot, a design for high-latency anonymous communications that offers indistinguishability and unobservability under a (qualified) global active adversary. Blindspot creates anonymous routes between sender-receiver pairs by subliminally encoding messages within the pre-existing communication behaviour of users within a social network. Specifically, the organic image sharing behaviour of users. Thus channel bandwidth depends on the intensity of image sharing behaviour of users along a route. A major challenge we successfully overcome is that routing must be accomplished in the face of significant restrictions - channel bandwidth is stochastic. We show that conventional social network routing strategies do not work. To solve this problem, we propose a novel routing algorithm. We evaluate Blindspot using a real-world dataset. We find that it delivers reasonable results for applications requiring low-volume unobservable communication.Comment: 13 Page