VOSON Lab Introduction

Transcription

1 VOSON Lab Introduction Big Data Analysis for Social Scientists Masterclass University of Edinburgh, 6 July 2017 Dr Robert Ackland School of Sociology Centre for Social Research & Methods Virtual Observatory for the Study of Online Networks (VOSON) Lab Research School of Social Sciences Australian National University E: robert.ackland@anu.edu.au W:

2 VOSON Lab ( Research, teaching and tool development in web (social) science, network science, computational social science, big data analytics... Formally commenced in 2005 Research via Australian Research Council grants: DP "New Methods for Researching the Existence and Impact of Political Networks on the WWW" Ackland and Gibson SR "Virtual Observatory for the Study of Online Networks (VOSON)" - Ackland, Gibson, O'Neil, Buchhorn, Bimber, Ward 2005 LP "The role of online social networks in successful ageing: benefitting from 'who you know' at older ages" - Booth, Ackland, Windsor DP "The institutional structure of e-government: a cross-policy, cross-country comparison" Henman, Ackland, Margetts DP Understanding online attention and user-generated content creation: An information consumption and production perspective - Ackland Australian Research Council Discovery Project proposal on measuring existence and impact of filter bubbles currently under assessment 2

3 Research tools VOSON software for hyperlink network construction & analysis (publicly available since 2006, over 2500 user accounts issued) Now available commercially via Uberlink ( R packages: SocialMediaLab (with Tim Graham) released on CRAN Nov 2015 collects (via free APIs) data from: Twitter, YouTube, Facebook creates various networks (actor networks, semantic networks) and datasets for text analysis Adaptive Sampling (with Kyosuke Tanaka) under development Implements adaptive sampling methodology of [Thompson (2006): Adaptive Web Sampling, Biometrics 62, , December 2006] to enable construction of samples from large-scale networks and unbiased estimates of population parameters. 3

4 Teaching & Training ANU undergraduate & masters courses: SOCY2166/SOCY6066 Social Science of the Internet SOCR8006 Online Research Methods Big Data Analysis for Social Scientists 5-day course available as part of the ACSPRI Social Science Methods Training Program 4

5 Part I Methods Ch 1 Introduction - Web Primer and Perspectives Ch 2 Online Research Methods Ch 3 Social Media Networks Ch 4 Hyperlink Networks Part II Examples Ch 5 Friendship Formation and Social Influence Ch 6 Organisational Collective Behaviour Ch 7 Politics and Participation Ch 8 Government and Public Policy Ch 9 Production and Collaboration Ch 10 Commerce and Marketing 5

6 Uberlink Corp Uberlink VOSON development team: Rob Ackland (ANU, Founder & CEO, Location: Canberra) Jamsheed Shorish (CTO, Location: Brussels) Francisca Borquez (Communication Officer & Research Assistant, Location: Canberra) VOSON 2.5 released 6 June 2017 Improved user interface/workflow More flexbility with database naming (e.g. special characters) Collect Twitter data from the real-time stream of tweets matching your search criteria (e.g. hashtag use) over a scheduled time period. 6

7 1. Scheduling a 1 hour collection on hashtag #ica17 2. As collection is run there is an update of the number of nodes (Twitter users) collected. 7

8 tie network and key SNA metrics 8

9 Twitter Analytics Service Access real-time streaming data from Twitter s Enterprise API platform, GNIP variety of networks (complete with statistics) based upon connections between Twitter users. Provides: GraphML network files, JSON tweet payload data files, rehydration scripts for unpackign the data structures Demo dataset and open source rehydration scripts: Also see: Example collections for clients: Australian federal politicians (9M activities) Twitter activities (tweets/retweets) authored by politicians or mention/reply to/retweet politician Collected for 1 year (Sep 2015 Sep 2016) 1st US presidential debate (6.5M activities) Twitter activities containing at least one of the following: #Debates, #Debates2016, #election2016, #HillaryClinton, #Hillary2016, #DonaldTrump, #Trump2016 Data collection started 15 min before debate and ended 15 min after debate 10 randomly chosen days during US presidential election (1.4M activities) Twitter activities containing at least one of the following: #HillaryClinton, #Hillary2016, #DonaldTrump, #Trump2016 Data collected between 26 Sep and 19 Oct, excluding debate days 9

10 SocialMediaLab R Package Aims to be the Swiss army knife for collecting social media data via free APIs and constructing datasets for network and text analysis Tim Graham lead developer & maintainer Rob Ackland Chung-hong Chan (Univ. of Hong new UI using maggritr Download: SocialMediaLab/index.html Tutorials / help: 10

11 SocialMediaLab data typology 11

12 Workflow: code to data to network Collect 500 latest tweets from #ica17 and construct an actor network showing replies+mentions+retweets between users Try it yourself! 12

13 VOSON Lab Example Research 1 Large-scale Twitter collections (Sep 2015 Sep 2016) Australian advocacy groups approx. 700 Twitter accounts/handles (e.g. trade unions, industry groups, community organisations, clubs), Australian federal politicians approx 200 Twitter accounts Collected (via GNIP Twitter Firehose - Uberlink Twitter Analytics Service) all tweets/retweets authored by these accounts all tweets that mention/reply to these accounts all retweets of these accounts Types of data: networks (reply, mention, retweet) text - tweet 'payloads' dynamic Twitter profile data 13

14 Halpin, D., Fraussen, B. and R. Ackland Assessing the Role of Policy Identity in Explaining Interest Group Audiences on Twitter, paper presented at the 10th ECPR General Conference Panel Political Communication and Interest Groups Prague, 7-10 September, Political homophily during an election Ackland and Shorish (Computational Economics, 2009): Agent-based economic model of network formation. If blogger s political preference is in the minority of the population as a whole, they will exhibit a higher degree of political homophily. So is there a correlation between political homophily on Twitter and party polling data? With Haris Memic (ANU) tracking political homophily (measured via ERGM) over 1 year and looking at correlation with political events and polling data 14

15 Incivil/abusive behaviour on Twitter: Quantifying degree of incivil behaviour over time, across policy areas, party lines, gender of politician [Ackland] Analog visualisation by Jen Fullerton ( ) artist, paper sculpturer and ANU Vice-Chancellor s College Artist Fellow

16 VOSON Lab Example Research 2 Focus is on automated Twitter user accounts (example of so-called socialbots ) - what is their role and influence in political conversations on Twitter? Data collected during 1st US presidential debate 26 Sep 2016 Twitter activities containing at least one of the following: #Debates, #Debates2016, #election2016, #HillaryClinton, #Hillary2016, #DonaldTrump, #Trump hour collection period (15 min before debate and ended 15 min after debate) 6.5M activities collected Network and text datasets provided via Uberlink s Twitter Analytics Service Central question: to what extent were socialbots active and influential within the network as a whole (network level) but also at the level of theoreticallyvalidated conversations extracted from the network (sub-network level). Project funded via Universities Australia DAAD Australia-Germany Joint Research Cooperation Scheme ( ): Socialbots as political actors? Autonomous communication technologies as tools and objects for digital sociology : R. Ackland (ANU), T. Graham (ANU), F. Muhle (Bielefeld University) 16

17 'Poperbots' are designed to reduce or pop the ideological bubble that users within a given subnetwork are situated within, by exposing these users to alternate points of view that appear to come from a member of their own cohort Bridgerbots are conceptualised as socialbots tasked with the role of rerouting or bridging informational flows between otherwise ideologically segregated subnetworks. Graham, T. and R. Ackland (2017), "Do Socialbots Dream of Popping the Filter Bubble? The Role of Socialbots in Promoting Deliberative Democracy in Social Media", in M. Bakardjieva and R. W. Gehl (eds) Socialbots and Their Friends: Digital Media and the Automation of Sociality. London: Routledge. 17