Currently integrating this tool with many different credibility models- crawled 7 topic-specific data sets from twitter.Social credibility model, with many different components (eg: Retweet analysis)Content-based credibility mode (using NLP)Bayesian model built on ground truth collected data.
Map different network facets onto different components of the visualization
TopicNets: Previous WorkTopic modeled view of 10,000 research papers from UCSD / CALIT2Clustering of colored nodes reflects the clustering of topics from within the published research. Interestingly, it mirrors the departmental structure of the institution quite well.
We have tested this system on many data sets. Examples include:News Articles (New York Times), A collection of awarded NSF grant proposals. The US Health Bill, A PHD Thesis, A collection of three visualization conferences proceedings over three years. Twitter data (Libya, LondonRiots, and Obama data sets). Facebook preference data for 15,000 users.
Concept of this interface can be generalized. Any “credibility” metric can be applied.This example is from topic modeled data from TwitterNOTE: User 21 appears “credible” wrt the selected topic “wikileaks”. However, this “credibility” is not backed up by his friends and follower groups on TwitterUser 9 however, appears credible and has this credibility backed up by the underlying network.
User-centric or topic-centric views:Military relevant example: By switching to a user-centric view, we can perform interesting searches over the microblog network. Lets say a user shows interest in a “remote garage door opener”. This appears completely normal, until the system highlights that this user appears prevelant in the context of the topic “home made explosives”. At this point, an alarm is raised and an analyst uses the interface to examine other topics from the target user’s underlying network. This form of visual-interactive topic-based analysis is unique to TopicLens.
River view shows average preference for item I across the selected user’s friend group
Provides a quick and easy analysis of the distribution of topic popularity
TopicLens and More!
TopicLens, and More! John O’DonovanFour Eyes Lab, Department of Computer Science, University of California, Santa Barbara.
Our recent work with RS interfaces:System Type APISmallWorlds Music / Movies FacebookTasteWeights Musical Artists / Jobs Facebook, Twitter, DBPedia, LinkedInTopicLens Twitter users and topics / Static / Twitter API MoviesWigiPedia Semantic Labels DBPedia / MediaWikiTopicNets People, Documents, PDF Documents / Topics Structured RDF documents
Inspectability and Control Elements:System Inspectability ControlSmallWorlds Column Graph, Circular Graph, Node-repositioning List View Drop-down menusTasteWeights List Views, Slider positions, Item/user sliders, Locks, Background Opacity, On-hover domain sliders. edges, Provenance view for re- rankingTopicLens Graph and River View, 3D view, Side panel controls (buttons Many on-hover actions. Zoom and sliders). Graph “spinning”, node clicks, Sorting. ( UI only No data- level controls )WigiPedia Wiki Page, Node-link graph, Node selection (click). Button Pop-up list views, edge panel. highlighting, tabular view. Node dragging (interpolation technique)TopicNets Graph view: Zoom, Click, Drag, Huge amount of control. 10+ List views, Table views, Charts panels of functions. Full graph interaction, Layout algorithms etc.
Inspectability Elements:Inspectability Mechanism Advantage DisadvantageNode-Link Graph Good provenance. Easy to Scales badly, gets cluttered inspect paths, neighbor links quickly (abstraction / etc clustering can help)List Views Simple, can be reranked with Hard to display connectivity provenance annotations.Interactive (hover, click, zoom Can handle lots of information. Hidden functionality. Usuallyetc) Create a “game-like” feel. needs some training / learning Keep user interested. curve, or good annotation/help toolsTabular Views Easier to understand than a Hard to display complex graph. connectivity / provenanceText-based Simple, Lots of detail Boring? Does not scale well.
Control Elements:Control Mechanism Advantage DisadvantageNode-Link Graph (rating using Communicates impact of user Not initially intuitive, difficultnode-drags) input very well to rerank vertically (crossed edges)Node-Link Graph (for data Very useful for selecting a Edges cause clutter quicklyselection) subset from a general esp. for large graphs. overviewSlider List Views Clean look, Users are familiar Difficult to resize, less freedom with slider input, can be than node-link views. reranked easily with provenance data shownRight-click Useful for node-specific Hidden functionality. Usually functionality needs some training / learning curve, or good annotation/help toolsControl panels (buttons, Easier to understand than a Can get cluttered quicklysliders etc) graph, can be labeled more depending on system easily. complexity.
TopicLens: Exploring Content and Network Structure in Parallel (Devendorf, O’Donovan, Hollerer)Hybrid Network ViewsRiver and Graphrepresentations displayedin parallel.River shows statistical(topic modeled)information aboutnetwork selections.Graph view showsinformation about theunderlying network.
Visual Analysis of Dynamic Topics and Social Network in Parallel Network Data Sources (APIs etc) LDA over Topology Content Analysis LDA Topics Sub-Networks Sub-Networks Graph View River View
TopicNets: Exploring Topic Relations in Social Networks LDA “Topic Models” usefulfor understanding relations inlarge volumes of text. Visualization and Interactioncan help a user gain insightsinto topic modeled data. LDA can be iterativelyapplied to tailor theinformation space to a usersrequirement.Gretarsson, O’Donovan et al.2011 (ACM Trans. On the Web)
TopicLens is a General solution: New York Times Article Example
Showing Credibility in the Underlying Social Network