Retweets are an important mechanism for the propagation of information on the Twitter social media platform. However, many retweets do not use the offcial retweet mechanism, or even community established conventions, and these "dark retweets" are not accounted for in many existing analyses. In this thesis, a typology of 19 different tweet propagation types is presented, based on seven characteristics: whether it is proprietary, the mechanism used, whether it is created by followers or non-followers, whether it mentions other users, if it is explicitly propagating another tweet, if it links to an original tweet, and the audience that it is pushed to. Based on this typology and two retweetability confidence factors, the degrees of a retweet's "darkness" can be determined. This typology was evaluated over two datasets: a random sample of 27,146 tweets, and a URL drill-down dataset of 262,517 tweets. It was found that dark retweets amounted to 20.8% of the random sample, however the behaviour of dark retweets is not uniform. The existence of supervisible and superdark URLs skew the average proportion of dark retweets in a dataset. Dark retweet behaviour was explored further by examining the average reach of retweet actions and identifying content domains in which dark retweets seem more prevalent. It was found that 1) the average reach of a dark retweet action (3,614 users per retweet) was found to be just over double the average reach of a visible retweet action (1,675 users per retweet), and 2) dark retweets were more frequently used in spreading social media (41% of retweets) and spam (40.6%) URLs, whilst they were least prevalent in basic information domains such as music (8.5%), photos (5%) and videos (3.9%). It was also found that once the supervisible and superdark URLs were discarded from the analysis, the proportion of dark retweets decreased from 20.8% to 12%, whilst visible retweets increased from 79.2% to 88%. This research contributes a 19-type tweet propagation typology and the findings that dark retweets exist, but their behaviour varies depending on the retweeter and URL content domain.

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This research explores the potential of utilising social Web data as a source of contextual information for searching and information retrieval tasks. While using a semantic and ontological approach to do so, it works towards a support system for providing adaptive and personalised recommendations for Cultural Heritage Resources. Most knowledge systems nowadays support an impressive amount of information and in case of Web based systems the size is ever growing. Among other difficulties faced by these systems is the problem of overwhelming the user with a vast amount of unrequired data, often referred to as information overload. The problem is elevated with the ever increasing issues of time constraint and extensive use of handheld devices. Use of context is a possible way out of this situation. To provide a more robust approach to context gathering we propose the use of Social Web technologies alongside the Semantic Web. As the social Web is used the most amongst today’s Web users, it can provide better understanding about a user’s interests and intentions. The proposed system gathers information about users from their social Web identities and enriches it with ontological knowledge and interlinks this mapped data with LOD resources online e.g., DBpedia. Thus, designing an interest model for the user can serve as a good source of contextual knowledge. This work bridges the gap between the user and search by analysing the virtual existence of a user and making interesting recommendations accordingly. i This work will open a way for the vast amount of structured data on Cultural Heritage to be exposed to the users of social networks, according to their tastes and likings.

This thesis develops a theoretical framework for the generation of artificial societies. In particular it shows how sub-systems emerge when the agents are able to learn and have the ability to communicate. This novel theoretical framework integrates the autopoietic hypothesis of human societies, formulated originally by the German sociologist Luhmann, with concepts of Shannon's information theory applied to adaptive learning agents. Simulations were executed using Multi-Agent-Based Modelling (ABM), a relatively new computational modelling paradigm involving the modelling of phenomena as dynamical systems of interacting agents. The thesis in particular, investigates the functions and properties necessary to reproduce the paradigm of society by using the mentioned ABM approach. Luhmann has proposed that in society subsystems are formed to reduce uncertainty. Subsystems can then be composed by agents with a reduced behavioural complexity. For example in society there are people who produce goods and other who distribute them. Both the behaviour and communication is learned by the agent and not imposed. The simulated task is to collect food, keep it and eat it until sated. Every agent communicates its energy state to the neighbouring agents. This results in two subsystems whereas agents in the first collect food and in the latter steal food from others. The ratio between the number of agents that belongs to the first system and to the second system, depends on the number of food resources. Simulations are in accordance with Luhmann, who suggested that adaptive agents self-organise by reducing the amount of sensory information or, equivalently, reducing the complexity of the perceived environment from the agent's perspective. Shannon's information theorem is used to assess the performance of the simulated learning agents. A practical measure, based on the concept of Shannon's information ow, is developed and applied to adaptive controllers which use Hebbian learning, input correlation learning (ICO/ISO) and temporal difference learning. The behavioural complexity is measured with a novel information measure, called Predictive Performance, which is able to measure at a subjective level how good an agent is performing a task. This is then used to quantify the social division of tasks in a social group of honest, cooperative food foraging, communicating agents.