Information extraction from social media for route planning

Micro-blogging is an emerging form of communication and became very popular in recent years. Micro-blogging services allow users to publish updates as short text messages that are broadcast to the followers of users in real-time. Twitter is currently the most popular micro-blogging service. It is a rich and real-time information source and a good way to discover interesting content or to follow recent developments. Additionally, the updates published on Twitter public timeline can be retrieved through their API. A significant amount of traffic information exists on Twitter platform. Twitter users tweet when they are in traffic about accidents, road closures or road construction. With this in mind, this paper presents a system that extracts traffic information from Twitter to be used in route planning. Route planning is of increasing importance as societies try to reduce their energy consumption. Furthermore, route planning is concerned with two types of constraints: stable, such as distance between two points and temporary such as weather conditions, traffic jams or road construction. Our system attempt to extract these temporary constraints from Twitter. We train Naive bayes, Maxent and SVM classifiers to filter non relevant traffic. We then apply NER on traffic tweets to extract locations, highwaysand directions. These extracted locations are then geocoded and used in route planning to avoid routes with traffic jams

Towards trust-aware recommendations in social networks

Recommender systems have been strongly researched within the last decade. With the emergence and popularization of social networks a new fi eld has been opened for social recommendations. Introducing new concepts such as trust and considering the network topology are some of the new strategies that recommender systems have to take into account in order to adapt their techniques to these new scenarios. In this thesis a simple model for recommendations on twitter is developed to apply some of the known techniques and explore how well the state of the art does in a real scenario. The thesis can serve as a basis for further social recommender system research

What factors influence whether politicians’ tweets are retweeted? Using CHAID to build an explanatory model of the retweeting of politicians’ tweets during the 2015 UK General Election campaign

Twitter is ever-present in British political life and many politicians use it as part of their campaign strategies. However, little is known about whether their tweets engage people, for example by being retweeted. This research addresses that gap, examining tweets sent by MPs during the 2015 UK General Election campaign to identify which were retweeted and why. A conceptual model proposes three factors which are most likely to influence retweets: the characteristics of (1) the tweet’s sender, (2) the tweet and (3) its recipients. This research focuses on the first two of these. Content and sentiment analysis are used to develop a typology of the politicians’ tweets, followed by CHAID analysis to identify the factors that best predict which tweets are retweeted. The research shows that the characteristics of tweet and its sender do influence whether the tweet is retweeted. Of the sender’s characteristics, number of followers is the most important – more followers leads to more retweets. Of the tweet characteristics, the tweet’s sentiment is the most influential. Negative tweets are retweeted more than positive or neutral tweets. Tweets attacking opponents or using fear appeals are also highly likely to be retweeted. The research makes a methodological contribution by demonstrating how CHAID models can be used to accurately predict retweets. This method has not been used to predict retweets before and has broad application to other contexts. The research also contributes to our understanding of how politicians and the public interact on Twitter, an area little studied to date, and proposes some practical recommendations regarding how MPs can improve the effectiveness of their Twitter campaigning. The finding that negative tweets are more likely to be retweeted also contributes to the ongoing debate regarding whether people are more likely to pass on positive or negative information online

Computing Twitter Influence with a GPU

Masteroppgave i informatikkINF399MAMN-INFMAMN-PRO

Generic adaptation framework for unifying adaptive web-based systems

The Generic Adaptation Framework (GAF) research project first and foremost creates a common formal framework for describing current and future adaptive hypermedia (AHS) and adaptive webbased systems in general. It provides a commonly agreed upon taxonomy and a reference model that encompasses the most general architectures of the present and future, including conventional AHS, and different types of personalization-enabling systems and applications such as recommender systems (RS) personalized web search, semantic web enabled applications used in personalized information delivery, adaptive e-Learning applications and many more. At the same time GAF is trying to bring together two (seemingly not intersecting) views on the adaptation: a classical pre-authored type, with conventional domain and overlay user models and data-driven adaptation which includes a set of data mining, machine learning and information retrieval tools. To bring these research fields together we conducted a number GAF compliance studies including RS, AHS, and other applications combining adaptation, recommendation and search. We also performed a number of real systems’ case-studies to prove the point and perform a detailed analysis and evaluation of the framework. Secondly it introduces a number of new ideas in the field of AH, such as the Generic Adaptation Process (GAP) which aligns with a layered (data-oriented) architecture and serves as a reference adaptation process. This also helps to understand the compliance features mentioned earlier. Besides that GAF deals with important and novel aspects of adaptation enabling and leveraging technologies such as provenance and versioning. The existence of such a reference basis should stimulate AHS research and enable researchers to demonstrate ideas for new adaptation methods much more quickly than if they had to start from scratch. GAF will thus help bootstrap any adaptive web-based system research, design, analysis and evaluation

Artificial Intelligence for Multimedia Signal Processing

Artificial intelligence technologies are also actively applied to broadcasting and multimedia processing technologies. A lot of research has been conducted in a wide variety of fields, such as content creation, transmission, and security, and these attempts have been made in the past two to three years to improve image, video, speech, and other data compression efficiency in areas related to MPEG media processing technology. Additionally, technologies such as media creation, processing, editing, and creating scenarios are very important areas of research in multimedia processing and engineering. This book contains a collection of some topics broadly across advanced computational intelligence algorithms and technologies for emerging multimedia signal processing as: Computer vision field, speech/sound/text processing, and content analysis/information mining