Social Network Analysis: From Graph Theory to Applications with Python

Social network analysis is the process of investigating social structures through the use of networks and graph theory. It combines a variety of techniques for analyzing the structure of social networks as well as theories that aim at explaining the underlying dynamics and patterns observed in these structures. It is an inherently interdisciplinary field which originally emerged from the fields of social psychology, statistics and graph theory. This talk will covers the theory of social network analysis, with a short introduction to graph theory and information spread. Then we will deep dive into Python code with NetworkX to get a better understanding of the network components, followed-up by constructing and implying social networks from real Pandas and textual datasets. Finally we will go over code examples of practical use-cases such as visualization with matplotlib, social-centrality analysis and influence maximization for information spread.Comment: Presented at PyCon'19 - Israeli Python Conference 201

Understanding Image Virality

Virality of online content on social networking websites is an important but esoteric phenomenon often studied in fields like marketing, psychology and data mining. In this paper we study viral images from a computer vision perspective. We introduce three new image datasets from Reddit, and define a virality score using Reddit metadata. We train classifiers with state-of-the-art image features to predict virality of individual images, relative virality in pairs of images, and the dominant topic of a viral image. We also compare machine performance to human performance on these tasks. We find that computers perform poorly with low level features, and high level information is critical for predicting virality. We encode semantic information through relative attributes. We identify the 5 key visual attributes that correlate with virality. We create an attribute-based characterization of images that can predict relative virality with 68.10% accuracy (SVM+Deep Relative Attributes) -- better than humans at 60.12%. Finally, we study how human prediction of image virality varies with different `contexts' in which the images are viewed, such as the influence of neighbouring images, images recently viewed, as well as the image title or caption. This work is a first step in understanding the complex but important phenomenon of image virality. Our datasets and annotations will be made publicly available.Comment: Pre-print, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201

An exploration of creative managers' perspectives on digital creativity: the impact of viral campaigns on creative processes, appeals and creative teams in UK advertising agencies

This research aims to develop conceptual insight into the practice and impact of a specific digital phenomenon – “viral marketing” – on marketing communications agencies. Specifically, it explores the UK, one of the most important hubs in global advertising looking at agenciesr campaign design planning, the roles of creative teams and the management processes through three research objectives: - To explicate, classify and explore the changes in advertising campaign planning processes and roles which digital phenomena such as virals have introduced - To capture and codify the working models which creative managers employ and the messaging strategies considered and implemented in viral campaigns - To develop theoretical models for understanding virals, agency campaign management, creative roles and develop extant frameworks Prior Work: Research into virals has grown rapidly over the last ten years but it is dominated by computing studies of online diffusion. The creative perspective has received little attention. Those studies which do address this viewpoint are principally focussed on the final advert. The voice of the producers of such campaigns – creative managers – is largely absent from the literature with a single study of campaign measurement. The roles of core teams/functions within the agencies, the criteria for viral creative concept evaluation, the campaign processes and working models are experiencing unprecedented change. Viral campaigns offer a bridge between the “old” and “new” worlds; it possesses the characteristics of TV and the Web. It is important because such viral campaigns have challenged the established models of advertising management and planning. Methods: The study undertakes the first comprehensive evaluation of the exisiting research into viral marketing, locating gaps in the creative design and management. Qualitative methodology through semi-structured in-depth interviews with creative managers in a range of UK advertising agencies is used to represent their views and responses to viral phenomena as they conceived, designed and reflected on campaigns. Contribution to Knowledge: This is the first study of the pre-launch/pre-production phase of campaign development. It has clarified a plethora of terms, in so doing developing the SPEED framework to understand the biological metaphor underpinning the phenomena, and finally producing a more accurate and comprehensive definition of the phenomenon. The paradigm funnel evaluation of prior research has tested and extended formal tools to arrive at a state of the art. The current research primarily consists of studies utilising secondary datasets, mainly quantitative – this study explores questions not just of what but of why, producing deeper insight than available before. Theoretical contributions: In the final model of viral creative management and design is an overarching conceptual contribution which for the first time represents creative roles, agency management and creative considerations at both pre and post-launch campaign phases. The thesis also develops theoretical constructs in specific areas – definition from practitioners, construct of campaign zones in viral design, a model of critical factors which facilitate virals, comparative theory of conventional and viral campaigns, characteristics of viral agencies, model of digital brand/agency relationships, a role construct for digital creatives among the main theoretical developments. These led into the final theoretical model