This dissertation is an attempt to face the current cacophony in biblical scholarship by suggesting a multidimensional approach to biblical texts, as a reality-orientated, problem- solving and progressive-effective hypothesis. In chapter I the hermeneutical and exegetical dilemma is illustrated in the light of the history of research on 1 Peter. In section A an analysis of this cacophony identified a lack of theory and methodology as the dissonant elements in biblical interpretation. In section B a communication theory is proposed, to deal with the hermeneutical-exegetical dilemma. With the aid of insights from semiotics, linguistics, literary theory and reception theory, a multidimensional communication model lS outlined, to do justice to the static, dynamic and dialectic parameters of textual communication. In order to simplify this multidimensional (i e the intratextual dimension as the prelude, the historical dimension as the interlude and the metatextual dimension as the finale of text analysis) and plurimodal (i e syntactic, semantic and pragmatic modes of a text) model, the notions of static thrust, dynamic perspective and dialectic strategy are proposed as the basic parameters and constituents in textual communication. A theoretical outline of the implications of this model is also given in section B. In section C the presuppositions underlying this model are crosschecked in the light of the epistemologico- paradigmatic parameters of the philosophy of science. Chapters II, III and IV are an implementation of this communication model. In these chapters a methodology for the intratextual (chapter II A), historical (chapter III A) and metatextual (chapter IV A) analyses is proposed, whereafter in each of them, it is implemented, in order to determine the thrust, perspective and strategy of 1 Peter (i e in sections B and C of chapters II, III and IV respectively). The implementation of this multidimensional approach to 1 Peter has confirmed the hypothesis that a one -dimensional approach to ancient canonized texts is futile. The over - and underexposure of the text by either an absolutized text-immanent or historic al analysis is comparable to someone trying to solve Rubic's cube by turning only one level of squares. Therefore it is concluded that a multidimensional approach to textual communication which takes account of the basic relief-mapping function of the static thrust, the cosmologic-orientational function of the dynamic perspective and the persuasive function of the dialectic strategy, is required. Ultimately the analysis of the thrust, perspective and strategy of 1 Peter has illustrated new possibilities of experiencing the successful communication of an ancient canonized text as a cosmologic battle between perspectives. / Thesis (DD)--University of Pretoria, 2009. / New Testament Studies / unrestricted

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<div>Representation learning (RL) for social networks facilitates real-world tasks such as visualization, link prediction and friend recommendation. Many methods have been proposed in this area to learn continuous low-dimensional embedding of nodes, edges or relations in social and information networks. However, most previous network RL methods neglect social signals, such as textual communication between users (nodes). Unlike more typical binary features on edges, such as post likes and retweet actions, social signals are more varied and contain ambiguous information. This makes it more challenging to incorporate them into RL methods, but the ability to quantify social signals should allow RL methods to better capture the implicit relationships among real people in social networks. Second, most previous work in network RL has focused on learning from homogeneous networks (i.e., single type of node, edge, role, and direction) and thus, most existing RL methods cannot capture the heterogeneous nature of relationships in social networks. Based on these identified gaps, this thesis aims to study the feasibility of incorporating heterogeneous information, e.g., texts, attributes, multiple relations and edge types (directions), to learn more accurate, fine-grained network representations. </div><div> </div><div>In this dissertation, we discuss a preliminary study and outline three major works that aim to incorporate textual interactions to improve relational representation learning. The preliminary study learns a joint representation that captures the textual similarity in content between interacting nodes. The promising results motivate us to pursue broader research on using social signals for representation learning. The first major component aims to learn explicit node and relation embeddings in social networks. Traditional knowledge graph (KG) completion models learn latent representations of entities and relations by interpreting them as translations operating on the embedding of the entities. However, existing approaches do not consider textual communications between users, which contain valuable information to provide meaning and context for social relationships. We propose a novel approach that incorporates textual interactions between each pair of users to improve representation learning of both users and relationships. The second major component focuses on analyzing how users interact with each other via natural language content. Although the data is interconnected and dependent, previous research has primarily focused on modeling the social network behavior separately from the textual content. In this work, we model the data in a holistic way, taking into account the connections between the social behavior of users and the content generated when they interact, by learning a joint embedding over user characteristics and user language. In the third major component, we consider the task of learning edge representations in social networks. Edge representations are especially beneficial as we need to describe or explain the relationships, activities, and interactions among users. However, previous work in this area lack well-defined edge representations and ignore the relational signals over multiple views of social networks, which typically contain multi-view contexts (due to multiple edge types) that need to be considered when learning the representation. We propose a new methodology that captures asymmetry in multiple views by learning well-defined edge representations and incorporates textual communications to identify multiple sources of social signals that moderate the impact of different views between users.</div>

This essay describes a case study of textual communication on the online social mediaplatform Tumblr. The aim of the research was to analyse the communicative functions and thestylistic, sociolinguistic, and pragmatic characteristics of textual discourse on Tumblr. Usingdata gathered on Tumblr, this essay analyses discursive aspects such as function, style, andpragmatics, relating the findings to literature on topics including relevance theory and theoriesof computer-mediated communication and cyberpragmatics. The research found thatdiscourse on Tumblr is largely phatic in nature, with an overwhelming focus on the discussionof shared interests. Such discussion on Tumblr appears to heavily depend on sharedbackground knowledge, which works as a barrier of in-group discursive solidarity andprovides and furthers feelings of connectedness. This study further found that alternative useof style and grammar on Tumblr appears to be largely uniform, suggesting that divergencesfrom standard norms are a way of displaying membership of a group and obtaining covertprestige, rather than a display of linguistic innovation.