Visualizing Change in Political Influence Networks to Support
Total Page：16
File Type：pdf, Size：1020Kb
Visualizing change in political influence networks to support journalism SUBMITTED IN PARTIAL FULLFILLMENT FOR THE DEGREE OF MASTER OF SCIENCE Marieke van Kouwen 10822763 MASTER INFORMATION STUDIES Human Centered Multimedia FACULTY OF SCIENCE UNIVERSITY OF AMSTERDAM July 30, 2016 1st Supervisor 2nd Supervisor Dhr. Prof. Dr. M. Worring Dhr. Dr. M.J. Marx University of Amsterdam University of Amsterdam 1 Visualizing change in political influence networks to support journalism Marieke van Kouwen University of Amsterdam Abstract — Open data offers journalists a new possibility to investigate governmental information. However, even with this data available, information is hard to find, explore, and interpret. To support journalists in researching the changes in political influence networks, we developed Clique: an interactive tool, that uses three data visualizations to show the evolution of political egocentric networks. The first visualization allows the user to select a period during a politicians career in an interactive timeline, which makes use of the Triangular Model. Subsequently, the egocentric network changes during the selected period are displayed in a second visualization using the difference map technique, clustering them into five types of changes. By selecting an actor in the difference map, a third visualization is generated which explores the shared relationship between the two selected actors in the network. We found that Clique is especially considered useful during the initial phase of journalistic research, and supports journalists in making unexpected connections while exploring the network. Index Terms — Data visualization, Dynamic networks, Open data, Big data, Egocentric abstraction ö 1. INTRODUCTION about data. They amplify our cognition by enhancing our working memory, help us easily recognize patterns and Journalists have a supervisory role in modern society, irregularities, allow data reading and exploration, reduce often referred to as ’The Fourth Branch of Government’. search time, guide statistical analysis; check validity, and Their task is to provide citizens with a glimpse into help in formulating new hypotheses [23]. When depicting the political decision making process, which is often political influence networks we encounter two major influenced by organizations and associated lobbyists. More problems using the traditional node-link diagrams. transparent governmental information can potentially Firstly, due to clutter of the network elements, it becomes build trust, strengthen responsibility, and increase citizen a challenge to discover insights in these extensive complex satisfaction [13]. Open data offers journalists a new, unique networks. Secondly, the evolution of the network is opportunity to obtain and substantiate stories in order to essential for journalists to interpret historic and current provide citizens with up-to-date information on political political dynamics, though invisible in the node-link influence networks. The aim of this project is to develop diagram. These reasons lead us to conclude that other a research tool that supports journalists in visually visualization techniques than the traditional node-link exploring open data files describing the network of diagram are needed to depict political influence networks. politicians, organizations, and the relations between them. To find a solution better suitable to the needs of the The analysis and exploration of networks in general is data-journalist, we need to address the two challenges usually done with the aid of node-link diagrams (graphs). described above: complexity and temporal context. These traditional network visualizations consist of nodes, Firstly, our visualization needs to deal with the problem linked by edges. Generally, data visualizations form a of the size and complexity of the network, which causes powerful tool to assist us in summarizing and reasoning clutter of network elements. The common solution addressing this problem is filtering and aggregation: the clustering of nodes. While these solutions reduce First reader: M. Worring, University of Amsterdam clutter, they obscure information as well [20]. In the case Second reader: M. Marx, University of Amsterdam In cooperation with Waag Society, Amsterdam of researching political influence networks, even the Manuscript received July 30, 2016 smallest amount of information can be of significant 2 importance. To diminish the complexity of the network 2. RELATED WORK we need to search in another direction for a solution. Instead, this project explores a single politician’s history Most examples of dynamic network visualization and associated circle of influence. The complexity is concerning political connections disregard network reduced without obscuring information, by generating a evolution completely and use the traditional node-link subset of the network that only targets an individual node, diagram. An example is the website LittleSis [15] in the and its egocentric network: its neighbors and associated U.S., “a free database of who-knows-who at the heights interconnections. of business and government”. The database combines data about members of Congress, political contributions, The second challenge is the visualization of temporal corporate boards, lobbying data, and government contract political influence network dynamics. The two major data. The published visualizations on the platform are current visualization methods of network dynamics are mostly static node-link diagrams and only used for time-to-time mappings (animation) and time-to-space illustrative purposes and storytelling, instead of allowing mappings (small multiples1) [7, 24]. Both have their own data exploring and analysis, as Clique intends to support. (dis)advantages. The animation approach [17] has been proven ineffective for network analysis compared to the A more interactive example is the tool Lynksoft [8], small multiples technique as users need more time to an online network visualization tool. Lynksoft is currently understand the network dynamics, plus it is hard to track used to visualize the same political influence network data changes over longer periods of time [5, 9, 25]. Time-to-space source as we use for this thesis. It is a powerful tool, since mappings seem more promising for analytical purposes, users are given the possibility to filter and remove nodes, although it is challenging to find a balance between a few though it still does not address the problems of the node- visualizations that lack detail, and many visualizations link diagram: complexity and temporal context. that need large screens [21] and are hard to monitor simultaneously [24]. It appears that other visualization 2.1 Visualizing egocentric networks methods are appropriate when we combine the temporal Shi et al. [21] developed the 1.5D Egocentric Dynamic aspect of dynamic networks with the analytic journalistic Network Visualization, which focuses on the egocentric approach, and the extensive amount of links an egocentric dynamic network, and encodes time vertically in one network still contains. To track changes in the network dimension. This approach smoothly integrates multivariate over time, we propose to focus on the essential part, data next to the dynamic network structure by including i.e. the change. Using only the changes further reduces trend glyphs to reveal interesting patterns. Nodes are the amount of links, which makes it easier to research connected to moments on the timeline and to each other. extensive egocentric dynamic networks. The colors and thickness of the edges reveal information Summarized concisely: this project develops an innovative, on the node attributes. Although the timeline gives the interactive visual interface that makes it possible for user a clear overview of the network evolution, the display journalists to research the extensive amount of open of complex networks makes the visualization hard to data on politicians and their circle of influence. Instead interpret due to clutter of network elements. of visualizing the entire network, the focus is on the EgoNetCloud adds the event-based scenario to the visualization of change in the egocentric network. This 1.5D technique [21]. This event-based egocentric dynamic will emphasize the historical nature of the data and network visualization [16] displays events, i.e. temporal declutter large network visualizations. To put it in a network dynamics as well as the relationship between research question: How to enable journalists to research political the ego node and the first degree nodes. Events are time influence networks by visualizing change? points when an edge appears, i.e. the publication of The name that is chosen for the interactive tool is ’Clique’, a paper, a call, or befriending someone on social media. which means interconnected network in network theory as Although it is nice to have all aspects in one visualization, well as a secluded group of people who do not readily allow the addition of these events to the displayed timeline others to join them; a description often used for the group makes the visualization even harder to interpret than of governing politicians2. the 1.5D method, especially when the number of nodes and edges increases. 2.2 Visualizing changes in dynamic network evolution 1. Small multiples uses a series of several graphs or charts with equal scale Graphdiaries as introduced by Bach et al. [6] is an and axes to visualize different parts of the same dataset. interesting visualization