Political astroturfing and organised trolling are online malicious behaviours with significant real-world effects. Common approaches examining these phenomena focus on broad campaigns rather than the small groups responsible. To reveal networks of cooperating accounts, we propose a novel temporal window approach that relies on account interactions and metadata alone. It detects groups of accounts engaging in behaviours that, in concert, execute different goal-based strategies, which we describe. Our approach is validated against two relevant datasets with ground truth data. See https://github.com/weberdc/find_hccs for code and data.
Presented at ASONAM'20 (2020 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining).
Co-authored with Frank Neumann (University of Adelaide)
A method to evaluate the reliability of social media data for social network ...Derek Weber
In order to study the effects of Online Social Network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In this multidisciplinary study, we consider the question of constructing traditional social networks from OSN data and then present a measurement case study showing how the reliability of OSN data affects social network analyses. To this end we developed a systematic comparison methodology, which we applied to two parallel datasets we collected from Twitter. We found considerable differences in datasets collected with different tools and that these variations significantly alter the results of subsequent analyses. Our results lead to a set of guidelines for researchers planning to collect online data streams to infer social networks.
Presented at ASONAM'20 (2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining)
Co-authors: Mehwish Nasim (Data61 / CSIRO), Lewis Mitchell (University of Adelaide), Lucia Falzon (University of Melbourne / DST Group)
Revealing social bot communities through coordinated behaviourDerek Weber
Presented at the 5th Australian Social Network Analysis Conference (ASNAC) on 26 November 2020. Co-authored with Mehwish Nasim (Data61, CSIRO), Lucia Falzon (DST Group, Uni Melbourne) and Lewis Mitchell (Uni Melbourne, DST Group).
Efforts to influence public opinion online, especially during times of political relevance, such as election campaigns, have grown since first observed in 2010, and are feared to be a particular threat to the upcoming US Presidential election. A significant component of such efforts has consisted of the use of social bots to quickly disseminate vast amounts of polarizing information, propaganda and biased opinion. As social bots are intended to mimic humans on social media, it is often difficult for other humans to identify them easily, but as there are also legitimate uses for online automation, the social media platforms also struggle to contain them, especially with the vast number of users they manage. Previous research has developed methods to detect influence campaigns in general, as well as specifically focusing on identifying social bots, including examining how they interact with other accounts and influence the broader political discussion.
In this talk, we discuss preliminary results from analysis of Twitter activity over the recent 2020 Democratic and Republican National Conventions, at which the parties formally nominated their candidates for President and Vice President. Each convention ran for four days, during which we collected 3m tweets. In particular, we apply techniques for discovering highly coordinating communities based on potentially coordinated behaviours: co-retweeting, co-mentioning of hashtags, and URL sharing. In doing so, we reveal groups of accounts engaging in potentially inauthentic behaviour, and identify classes of participating accounts, including social bots, campaign accounts, news accounts, and regular Twitter users. A variety of analyses of content and temporal patterns exhibited by the communities provide qualitative and quantitative validation, along with discussion of different behaviour patterns observed between the conventions. The ultimate aim is to distinguish between legitimate use of online influence activities (e.g., by political parties and grass roots campaigns) from covert malicious ones.
Chung-Jui LAI - Polarization of Political Opinion by News MediaREVULN
In 2016 US election, social media played a vital role in shaping public opinions as expressed by the news media that have created the phenomenon of polarization in the United States. Because social media gave people the ability to follow, share, post, comment below everything, the phenomenon of political opinions being spread easily and quickly on social media by the news agencies is bringing out a significantly polarized populace.
Consequently, it’s very important to understand the language differences on Twitter and figure out how propaganda spread by different political parties that influence or perhaps mislead public opinion. This talk will introduce the relationship among the social media, public opinion, and news media, then suggests the method to collect the tweets from Twitter and conduct sentimental and logistic regression analysis on them. Furthermore, this talk points out the special aspect on the relationship between the polarization and the topic of this conference (fake news, disinformation and propaganda).
Main points:
- situation in Taiwan
- research on fake news
- methods for fighting fake news
Data Journalism and the Remaking of Data InfrastructuresLiliana Bounegru
Talk given at the “Evidence and the Politics of Policymaking” Conference, University of Bath, 14th September 2016, on the basis of my PhD research at the University of Groningen and University of Ghent.
http://www.bath.ac.uk/ipr/events/news-0230.html.
Keynote address by Anatoliy Gruzd at the 2017 Altmetrics Conference in Toronto, Canada (Sep 27, 2017)
Abstract
Arguably, even the most innovative ideas take time to catch on. Ideas that seem obvious today, at one point were obscure oddities known only to a select few. Washing your hands, airbags in cars, the internet - none of these ideas were accepted immediately. New ideas need time to incubate, the process of switching from old ideas to new is not seamless nor is it linear. In today’s social media-connected world, even though ideas can spread quickly and more efficiently than ever before, they are now competing for attention with a multitude of other ideas, memes, tweets, snaps, YouTube videos and news (fake and real). Conceptually, if social media is a network of highways on which ideas and people travel, altmetrics are the billboard or traffic signs on these highways that can help interested parties to discover new ideas or re-discover ideas left on the side of the road. While often neglected, the above metaphor is meant to illuminate the important role of altmetrics for researchers, innovators and funders seeking to track the impacts of new ideas, as well as for the many idea consumers looking for emerging and novel insights.
This talk will outline the current state of altmetrics research and how altmetrics are being commonly calculated and used by different stakeholders. It will also explore the social network properties of ideas and how these properties might be used to customize altmetrics for different audiences and uses. The keynote will conclude by calling for the development of training strategies to provide learning opportunities for researchers and administrators from various fields to acquire necessary digital literacy skills so that they better understand how altmetrics are measured and how they can be interpreted for decision making. The keynote will also call on altmetrics developers and researchers to create algorithms and data collection strategies that are less prone to manipulation by the rapid rise of social bots.
Doing Social and Political Research in a Digital Age: An Introduction to Digi...Liliana Bounegru
Lecture given at the National Center of Competence in Research: Challenges to Democracy in the 21st Century, 5 November 2015, Zürich University, Zürich, Switzerland
A method to evaluate the reliability of social media data for social network ...Derek Weber
In order to study the effects of Online Social Network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In this multidisciplinary study, we consider the question of constructing traditional social networks from OSN data and then present a measurement case study showing how the reliability of OSN data affects social network analyses. To this end we developed a systematic comparison methodology, which we applied to two parallel datasets we collected from Twitter. We found considerable differences in datasets collected with different tools and that these variations significantly alter the results of subsequent analyses. Our results lead to a set of guidelines for researchers planning to collect online data streams to infer social networks.
Presented at ASONAM'20 (2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining)
Co-authors: Mehwish Nasim (Data61 / CSIRO), Lewis Mitchell (University of Adelaide), Lucia Falzon (University of Melbourne / DST Group)
Revealing social bot communities through coordinated behaviourDerek Weber
Presented at the 5th Australian Social Network Analysis Conference (ASNAC) on 26 November 2020. Co-authored with Mehwish Nasim (Data61, CSIRO), Lucia Falzon (DST Group, Uni Melbourne) and Lewis Mitchell (Uni Melbourne, DST Group).
Efforts to influence public opinion online, especially during times of political relevance, such as election campaigns, have grown since first observed in 2010, and are feared to be a particular threat to the upcoming US Presidential election. A significant component of such efforts has consisted of the use of social bots to quickly disseminate vast amounts of polarizing information, propaganda and biased opinion. As social bots are intended to mimic humans on social media, it is often difficult for other humans to identify them easily, but as there are also legitimate uses for online automation, the social media platforms also struggle to contain them, especially with the vast number of users they manage. Previous research has developed methods to detect influence campaigns in general, as well as specifically focusing on identifying social bots, including examining how they interact with other accounts and influence the broader political discussion.
In this talk, we discuss preliminary results from analysis of Twitter activity over the recent 2020 Democratic and Republican National Conventions, at which the parties formally nominated their candidates for President and Vice President. Each convention ran for four days, during which we collected 3m tweets. In particular, we apply techniques for discovering highly coordinating communities based on potentially coordinated behaviours: co-retweeting, co-mentioning of hashtags, and URL sharing. In doing so, we reveal groups of accounts engaging in potentially inauthentic behaviour, and identify classes of participating accounts, including social bots, campaign accounts, news accounts, and regular Twitter users. A variety of analyses of content and temporal patterns exhibited by the communities provide qualitative and quantitative validation, along with discussion of different behaviour patterns observed between the conventions. The ultimate aim is to distinguish between legitimate use of online influence activities (e.g., by political parties and grass roots campaigns) from covert malicious ones.
Chung-Jui LAI - Polarization of Political Opinion by News MediaREVULN
In 2016 US election, social media played a vital role in shaping public opinions as expressed by the news media that have created the phenomenon of polarization in the United States. Because social media gave people the ability to follow, share, post, comment below everything, the phenomenon of political opinions being spread easily and quickly on social media by the news agencies is bringing out a significantly polarized populace.
Consequently, it’s very important to understand the language differences on Twitter and figure out how propaganda spread by different political parties that influence or perhaps mislead public opinion. This talk will introduce the relationship among the social media, public opinion, and news media, then suggests the method to collect the tweets from Twitter and conduct sentimental and logistic regression analysis on them. Furthermore, this talk points out the special aspect on the relationship between the polarization and the topic of this conference (fake news, disinformation and propaganda).
Main points:
- situation in Taiwan
- research on fake news
- methods for fighting fake news
Data Journalism and the Remaking of Data InfrastructuresLiliana Bounegru
Talk given at the “Evidence and the Politics of Policymaking” Conference, University of Bath, 14th September 2016, on the basis of my PhD research at the University of Groningen and University of Ghent.
http://www.bath.ac.uk/ipr/events/news-0230.html.
Keynote address by Anatoliy Gruzd at the 2017 Altmetrics Conference in Toronto, Canada (Sep 27, 2017)
Abstract
Arguably, even the most innovative ideas take time to catch on. Ideas that seem obvious today, at one point were obscure oddities known only to a select few. Washing your hands, airbags in cars, the internet - none of these ideas were accepted immediately. New ideas need time to incubate, the process of switching from old ideas to new is not seamless nor is it linear. In today’s social media-connected world, even though ideas can spread quickly and more efficiently than ever before, they are now competing for attention with a multitude of other ideas, memes, tweets, snaps, YouTube videos and news (fake and real). Conceptually, if social media is a network of highways on which ideas and people travel, altmetrics are the billboard or traffic signs on these highways that can help interested parties to discover new ideas or re-discover ideas left on the side of the road. While often neglected, the above metaphor is meant to illuminate the important role of altmetrics for researchers, innovators and funders seeking to track the impacts of new ideas, as well as for the many idea consumers looking for emerging and novel insights.
This talk will outline the current state of altmetrics research and how altmetrics are being commonly calculated and used by different stakeholders. It will also explore the social network properties of ideas and how these properties might be used to customize altmetrics for different audiences and uses. The keynote will conclude by calling for the development of training strategies to provide learning opportunities for researchers and administrators from various fields to acquire necessary digital literacy skills so that they better understand how altmetrics are measured and how they can be interpreted for decision making. The keynote will also call on altmetrics developers and researchers to create algorithms and data collection strategies that are less prone to manipulation by the rapid rise of social bots.
Doing Social and Political Research in a Digital Age: An Introduction to Digi...Liliana Bounegru
Lecture given at the National Center of Competence in Research: Challenges to Democracy in the 21st Century, 5 November 2015, Zürich University, Zürich, Switzerland
GitHub as Transparency Device in Data Journalism, Open Data and Data ActivismLiliana Bounegru
Slides from presentation of research agenda around uses of GitHub in journalism at the Digital Methods Summer School 2015. More details here: http://lilianabounegru.org/2015/07/08/github-as-transparency-device-in-data-journalism-open-data-and-data-activism/
Mapping Issues with the Web: An Introduction to Digital MethodsJonathan Gray
Slides from talk on "Mapping Issues with the Web: An Introduction to Digital Methods" at Tow Center for Digital Journalism, Columbia University, 23rd September 2014. Further details at: http://jonathangray.org/2014/09/10/mapping-issues-with-web-columbia/
Journalists today are faced with an overwhelming abundance of data – from large collections of leaked documents, to public databases about lobbying or government spending, to ‘big data’ from social networks such as Twitter and Facebook. To stay relevant to society journalists are learning to process this data and separate signal from noise in order to provide valuable insights to their readers. This talk will address questions like: What is the potential of data journalism? Why is it relevant to society? And how can you get started?
Doing Digital Methods: Some Recent Highlights from Winter and Summer SchoolsLiliana Bounegru
Talk given at the Digital Methods Winter School 2017 at the University of Amsterdam. It presents a selection of projects developed at the 2016 Digital Methods Winter and Summer Schools (www.digitalmethods.net).
Social Media in Australia: A ‘Big Data’ Perspective on TwitterAxel Bruns
Invited presentation at the University of Melbourne, 4 April 2017.
Twitter research to date has focussed mainly on the study of isolated events, as described for example by specific hashtags or keywords relating to elections, natural disasters, public events, and other moments of heightened activity in the network. This limited focus is determined in part by the limitations placed on large-scale access to Twitter data by Twitter, Inc. itself. This research presents the first ever comprehensive study of a national Twittersphere as an entity in its own right. It examines the structure of the follower network amongst some 4 million Australian Twitter accounts and the dynamics of their day-to-day activities, and explores the Australian Twittersphere’s engagement with specific recent events.
A glimpse into what social media is all about and how the researchers in the world are using social media. Social media is not a mere hype and not a platform to leverage word-of-the-mouth practices as is the common perception of it in Pakistan: it is much more than that and this is what this talk presented.
This is a presentation given at the ICWSM 2010 in Washington, DC (www.icwsm.org). You can watch a video of the presentation on videolectures.net
Twitter is a microblogging website where users read and write millions of short messages on a variety of topics every day. This study uses the context of the German federal election to investigate whether Twitter is used as a forum for political deliberation and whether online messages on Twitter validly mirror offline political sentiment. Using LIWC text analysis software, we conducted a content-analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is indeed used extensively for political deliberation. We find that the mere number of messages mentioning a party reflects the election result. Moreover, joint mentions of two parties are in line with real world political ties and coalitions. An analysis of the tweets’ political sentiment demonstrates close correspondence to the parties' and politicians’ political positions indicating that the content of Twitter messages plausibly reflects the offline political landscape. We discuss the use of microblogging message content as a valid indicator of political sentiment and derive suggestions for further research.
Finding Emerging Topics Using Chaos and Community Detection in Social Media G...Paragon_Science_Inc
In this talk, we describe our recent work in the analysis of Twitter-based network graphs, including the Ebola crisis in 2014 and the stock market in 2015.
Data augmented ethnography: using big data and ethnography to explore candi...Salla-Maaria Laaksonen
In this paper we propose data augmented ethnography as a novel mixed methods approach to combine ethnographic, qualitative, observations with social media data collection and computational analysis. Using two brief studies on online interaction as examples we discuss the benefits and challenges of the combination of these two perspectives. We posit that the observations made in the qualitative phase can be quantified and hypothesized together with the data collected later during the analysis stage. Through our case studies we aim to shed light to the differences apparent on the party level and seek to understand how candidates, based on their parties political standing, differ in terms of interactivity. We ask, what insights does a mixed-method approach combining ethnographic observations to computational social science offer to the study of interactivity and its many pregnant forms? To answer this question, we use a large data set collected from different social media platforms before and during the 2015 Parliament Election in Finland. This data consists of both textual data including all candidate updates and the conversations they elicited, as well as field notes written and collected during ethnographic field work period before the elections.
Twitter Based Outcome Predictions of 2019 Indian General Elections Using Deci...Ferdin Joe John Joseph PhD
Presented at the 4th International Conference on Information Technology InCIT 2019 organised by Thai-Nichi Institute of Technology and Council of IT Deans in Thailand (CITT)
2010 Catalyst Conference - Trends in Social Network AnalysisMarc Smith
Review of trends related to social network analysis in the enterprise. Presented at the 2010 Catalyst Conference in San Diego, CA july 29, 2010. Presented with Mike Gotta, Gartner Group.
§ Gruzd, A., Jacobson, J., Dubois, E. (2017). You’re Hired: Examining Acceptance of Social Media Screening of Job Applicants. In Proceedings of the 23rd Americas Conference on Information Systems (AMCIS), August 10-12, 2017, Boston, MA, USA.
Available at http://aisel.aisnet.org/amcis2017/DataScience/Presentations/28/
Abstract:
The paper examines attitudes towards employers using social media to screen job applicants. In an online survey of 454 participants, we compare the comfort level with this practice in relation to different types of information that can be gathered from publicly accessible social media. The results revealed a nuanced nature of people’s information privacy expectations in the context of hiring practices. People’s perceptions of employers using social media to screen job applicants depends on (1) whether or not they are currently seeking employment (or plan to), (2) the type of information that is being accessed by a prospective em-ployer (if there are on the job market), and (3) their cultural background, but not gender. The findings emphasize the need for employers and recruiters who are relying on social media to screen job applicants to be aware of the types of information that may be perceived to be more sensitive by applicants, such as social network-related information.
Revealing Social Bots with Coordinated Networks during US Political ConventionsDerek Weber
Presented at Sunbelt in Cairns, Australia, 12-16 July 2022. Co-authors: Dr Mehwish Nasim (University of Western Australia), Dr Lucia Falzon (University of Melbourne) and Professor Lewis Mitchell (University of Adelaide). Presented by Derek Weber (PhD student, University of Adelaide, at the time).
Covert campaigns to influence public opinion online, especially during elections, have grown since first observed in 2010, and were feared to threaten the 2020 US Presidential election. Such efforts often include the use of social bots to disseminate propaganda, feeding polarisation. As social bots mimic humans, humans find them difficult to identify. Given there are legitimate uses for automated accounts, platforms also struggle to contain them, especially popular ones with many users. Previous research has focused on detecting entire influence campaigns, identifying botnets by common behaviours, and individual detection, including studying how they interact with human users to influence the broader discussion.
We discuss our analysis of 3m tweets collected over the 2020 Democratic and Republican National Conventions. We reveal highly coordinating groups based on co-retweeting, co-mentioning of hashtags, and URL sharing. These groups potentially engaged in coordinated inauthentic behaviour, and consisted of social bots, campaign accounts, news accounts, and regular Twitter users. Our content and temporal behaviour analyses provide qualitative and quantitative validation and expose behavioural differences between the conventions. Our ultimate aim is to distinguish between legitimate use of online influence activities (e.g., by political parties and grass roots campaigns) and malicious ones.
GitHub as Transparency Device in Data Journalism, Open Data and Data ActivismLiliana Bounegru
Slides from presentation of research agenda around uses of GitHub in journalism at the Digital Methods Summer School 2015. More details here: http://lilianabounegru.org/2015/07/08/github-as-transparency-device-in-data-journalism-open-data-and-data-activism/
Mapping Issues with the Web: An Introduction to Digital MethodsJonathan Gray
Slides from talk on "Mapping Issues with the Web: An Introduction to Digital Methods" at Tow Center for Digital Journalism, Columbia University, 23rd September 2014. Further details at: http://jonathangray.org/2014/09/10/mapping-issues-with-web-columbia/
Journalists today are faced with an overwhelming abundance of data – from large collections of leaked documents, to public databases about lobbying or government spending, to ‘big data’ from social networks such as Twitter and Facebook. To stay relevant to society journalists are learning to process this data and separate signal from noise in order to provide valuable insights to their readers. This talk will address questions like: What is the potential of data journalism? Why is it relevant to society? And how can you get started?
Doing Digital Methods: Some Recent Highlights from Winter and Summer SchoolsLiliana Bounegru
Talk given at the Digital Methods Winter School 2017 at the University of Amsterdam. It presents a selection of projects developed at the 2016 Digital Methods Winter and Summer Schools (www.digitalmethods.net).
Social Media in Australia: A ‘Big Data’ Perspective on TwitterAxel Bruns
Invited presentation at the University of Melbourne, 4 April 2017.
Twitter research to date has focussed mainly on the study of isolated events, as described for example by specific hashtags or keywords relating to elections, natural disasters, public events, and other moments of heightened activity in the network. This limited focus is determined in part by the limitations placed on large-scale access to Twitter data by Twitter, Inc. itself. This research presents the first ever comprehensive study of a national Twittersphere as an entity in its own right. It examines the structure of the follower network amongst some 4 million Australian Twitter accounts and the dynamics of their day-to-day activities, and explores the Australian Twittersphere’s engagement with specific recent events.
A glimpse into what social media is all about and how the researchers in the world are using social media. Social media is not a mere hype and not a platform to leverage word-of-the-mouth practices as is the common perception of it in Pakistan: it is much more than that and this is what this talk presented.
This is a presentation given at the ICWSM 2010 in Washington, DC (www.icwsm.org). You can watch a video of the presentation on videolectures.net
Twitter is a microblogging website where users read and write millions of short messages on a variety of topics every day. This study uses the context of the German federal election to investigate whether Twitter is used as a forum for political deliberation and whether online messages on Twitter validly mirror offline political sentiment. Using LIWC text analysis software, we conducted a content-analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is indeed used extensively for political deliberation. We find that the mere number of messages mentioning a party reflects the election result. Moreover, joint mentions of two parties are in line with real world political ties and coalitions. An analysis of the tweets’ political sentiment demonstrates close correspondence to the parties' and politicians’ political positions indicating that the content of Twitter messages plausibly reflects the offline political landscape. We discuss the use of microblogging message content as a valid indicator of political sentiment and derive suggestions for further research.
Finding Emerging Topics Using Chaos and Community Detection in Social Media G...Paragon_Science_Inc
In this talk, we describe our recent work in the analysis of Twitter-based network graphs, including the Ebola crisis in 2014 and the stock market in 2015.
Data augmented ethnography: using big data and ethnography to explore candi...Salla-Maaria Laaksonen
In this paper we propose data augmented ethnography as a novel mixed methods approach to combine ethnographic, qualitative, observations with social media data collection and computational analysis. Using two brief studies on online interaction as examples we discuss the benefits and challenges of the combination of these two perspectives. We posit that the observations made in the qualitative phase can be quantified and hypothesized together with the data collected later during the analysis stage. Through our case studies we aim to shed light to the differences apparent on the party level and seek to understand how candidates, based on their parties political standing, differ in terms of interactivity. We ask, what insights does a mixed-method approach combining ethnographic observations to computational social science offer to the study of interactivity and its many pregnant forms? To answer this question, we use a large data set collected from different social media platforms before and during the 2015 Parliament Election in Finland. This data consists of both textual data including all candidate updates and the conversations they elicited, as well as field notes written and collected during ethnographic field work period before the elections.
Twitter Based Outcome Predictions of 2019 Indian General Elections Using Deci...Ferdin Joe John Joseph PhD
Presented at the 4th International Conference on Information Technology InCIT 2019 organised by Thai-Nichi Institute of Technology and Council of IT Deans in Thailand (CITT)
2010 Catalyst Conference - Trends in Social Network AnalysisMarc Smith
Review of trends related to social network analysis in the enterprise. Presented at the 2010 Catalyst Conference in San Diego, CA july 29, 2010. Presented with Mike Gotta, Gartner Group.
§ Gruzd, A., Jacobson, J., Dubois, E. (2017). You’re Hired: Examining Acceptance of Social Media Screening of Job Applicants. In Proceedings of the 23rd Americas Conference on Information Systems (AMCIS), August 10-12, 2017, Boston, MA, USA.
Available at http://aisel.aisnet.org/amcis2017/DataScience/Presentations/28/
Abstract:
The paper examines attitudes towards employers using social media to screen job applicants. In an online survey of 454 participants, we compare the comfort level with this practice in relation to different types of information that can be gathered from publicly accessible social media. The results revealed a nuanced nature of people’s information privacy expectations in the context of hiring practices. People’s perceptions of employers using social media to screen job applicants depends on (1) whether or not they are currently seeking employment (or plan to), (2) the type of information that is being accessed by a prospective em-ployer (if there are on the job market), and (3) their cultural background, but not gender. The findings emphasize the need for employers and recruiters who are relying on social media to screen job applicants to be aware of the types of information that may be perceived to be more sensitive by applicants, such as social network-related information.
Revealing Social Bots with Coordinated Networks during US Political ConventionsDerek Weber
Presented at Sunbelt in Cairns, Australia, 12-16 July 2022. Co-authors: Dr Mehwish Nasim (University of Western Australia), Dr Lucia Falzon (University of Melbourne) and Professor Lewis Mitchell (University of Adelaide). Presented by Derek Weber (PhD student, University of Adelaide, at the time).
Covert campaigns to influence public opinion online, especially during elections, have grown since first observed in 2010, and were feared to threaten the 2020 US Presidential election. Such efforts often include the use of social bots to disseminate propaganda, feeding polarisation. As social bots mimic humans, humans find them difficult to identify. Given there are legitimate uses for automated accounts, platforms also struggle to contain them, especially popular ones with many users. Previous research has focused on detecting entire influence campaigns, identifying botnets by common behaviours, and individual detection, including studying how they interact with human users to influence the broader discussion.
We discuss our analysis of 3m tweets collected over the 2020 Democratic and Republican National Conventions. We reveal highly coordinating groups based on co-retweeting, co-mentioning of hashtags, and URL sharing. These groups potentially engaged in coordinated inauthentic behaviour, and consisted of social bots, campaign accounts, news accounts, and regular Twitter users. Our content and temporal behaviour analyses provide qualitative and quantitative validation and expose behavioural differences between the conventions. Our ultimate aim is to distinguish between legitimate use of online influence activities (e.g., by political parties and grass roots campaigns) and malicious ones.
Cottbus Brandenburg University of Technology Lecture series on Smart RegionsCritically Assembling Data, Processes & Things: Toward and Open Smart CityJune 5, 2018
This lecture will critically focus on smart cities from a data based socio-technological assemblage approach. It is a theoretical and methodological framework that allows for an empirical examination of how smart cities are socially and technically constructed, and to study them as discursive regimes and as a large technological infrastructural systems.
The lecture will refer to the research outcomes of the ERC funded Programmable City Project led by Rob Kitchin at Maynooth University and will feature examples of empirical research conducted in Dublin and other Irish cities.
In addition, the lecture will discuss the research outcomes of the Canadian Open Smart Cities project funded by the Government of Canada GeoConnections Program. Examples will be drawn from five case studies namely about the cities of Edmonton, Guelph, Ottawa and Montreal, and the Ontario Smart Grid as well as number of international best practices. The recent Infrastructure Canada Canadian Smart City Challenge and the controversial Sidewalk Lab Waterfront Toronto project will also be discussed.
It will be argued that no two smart cities are alike although the technological solutionist and networked urbanist approaches dominate and it is suggested that these kind of smart cities may not live up to the promise of being better places to live.
In this lecture, the ideals of an Open Smart City are offered instead and in this kind of city residents, civil society, academics, and the private sector collaborate with public officials to mobilize data and technologies when warranted in an ethical, accountable and transparent way in order to govern the city as a fair, viable and livable commons that balances economic development, social progress and environmental responsibility. Although an Open Smart City does not yet exist, it will be argued that it is possible.
Twitter analytics: some thoughts on sampling, tools, data, ethics and user re...Farida Vis
Keynote delivered at the SRA Social Media in Social Research conference, London, 24 June, 2013. The presentation highlights some thoughts on sampling, tools, data, ethics and user requirements for Twitter analytics, including an overview of a series of recent tools.
Conference of Irish Geographies 2018
The Earth as Our Home
Automating Homelessness May 12, 2018
The research for these studies is funded by a European Research Council Advanced Investigator award ERC-2012-AdG-323636-SOFTCITY.
Presentation at the Workshop on "Small Data and Big Data Controversies and Alternatives: Perspectives from The Sage Handbook of Social Media Research Methods" with Anabel Quan-Haase, Luke Sloan, Diane Rasmussen Pennington, et al.
LINK: http://sched.co/7G5N
This week we will learn about user generated content (UGC), citizen science, crowdsourcing & volunteered geographic information (VGI). We will also discuss divergent views on data humanitarianism.
Linas Eriksonas, The Impact of Time Zone Difference on Social Networks of En...Linas Eriksonas
The presentation given at the 33rd Sunbelt Social Networks Conference of the International Network for Social Network Analysis, May 21-26 2013, University of Hamburg
Authors:
Tracey P. Lauriault, Programmable City Project, Maynooth University
Peter Mooney, Environmental Protection Agency Ireland and Department of Computer Science Maynooth University
Title:
Crowdsourcing: A Geographic Approach to Identifying Policy Opportunities and Challenges Toward Deeper Levels of Public Engagement
Presented:
The Internet, Policy and Politics Conference, Oxford Internet Institute, University of Oxford, September 25-26, 2014
See the abstract here:
http://ipp.oii.ox.ac.uk/2014/programme-2014/track-c-politics-of-engagement/community/tracey-p-lauriault-peter-mooney
Data Science Innovations is a guest lecture for the Advanced Data Analytics (an Introduction) course at the Advanced Analytics Institute at University of Technology Sydney
From Research to Applications: What Can We Extract with Social Media Sensing?Yiannis Kompatsiaris
SIGMAP22 Keynote Presentation:
Social media have transformed the Web into an interactive sharing platform where users upload data and media, comment on, and share this content within their social circles. The large-scale availability of user-generated content in social media platforms has opened up new possibilities for studying and understanding real-world phenomena, trends and events. Social media and websites provide an access to public opinions on certain aspects and therefore play an important role in getting insights on targeted audiences. The objective of this talk is to provide an overview of social media mining, including key aspects such as data collection, multimodal analysis and visualization. Challenges, such as fighting misinformation, will be presented together with applications, results and demonstrations from multiple areas including: news, environment, security, interior and urban design.
Paper presented at the International Conference on Using ICT, Social Media and Mobile Technologies to Foster Self-Organisation in Urban and Neighbourhood Governance. Delft, Netherlands. 16 May 2013
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Who’s in the Gang? Revealing Coordinating Communities in Social Media
1. OFFICIALASONAM 7-10 Dec 2020
Derek Weber1,2 & Frank Neumann1
Contact: derek.weber@adelaide.edu.au
1 School of Computer Science, University of Adelaide, Australia.
2 Defence Science and Technology Group, Department of Defence, Australia.
WHO’S IN THE GANG?
REVEALING COORDINATING COMMUNITIES IN SOCIALMEDIA
2. OFFICIALASONAM 7-10 Dec 2020 2
https://twitter.com/conspirator0/status/1328479128908132358 17 Nov 2020
3. OFFICIALASONAM 7-10 Dec 2020
Context
• Social media for political communication
• Targeted marketing → (Political) Spam & recruitment
• Anonymity → Trolls
• Automation → Bots, social bots & political bots
3
Targeted marketing + Anonymity + Automation = Interference
5. OFFICIALASONAM 7-10 Dec 2020
Information Campaigns & Coordination Strategies
5
Intent Strategy Planning Execution
Post Repost
time
time
Hostile
Friendly
Good Post Junk Post
time
Channel: e.g., #OurPartyRocks
time
t0 t1
t1 t2
t2 t3
Pollution
Woolley (2016)
Fisher (2018)
Nasim et al. (2018)
Boost
Cao et al. (2015)
Vo et al. (2017)
Graham et al. (2020)
Bully
Hine et al. (2017)
Kumar et al. (2018)
6. OFFICIALASONAM 7-10 Dec 2020
The Challenge
• Discovery
• RQ1 How can highly coordinating communities (HCCs) be found?
• Validation
• RQ2 How do the discovered communities differ?
• RQ3 How consistent is the HCC messaging?
• RQ4 Are the HCCs internally or externally focused?
6
To identify groups of accounts whose behaviour,
though typical in nature, is anomalous in degree.
9. OFFICIALASONAM 7-10 Dec 2020
Extract HCCs
9
Focal Structures Analysis1 – Variant (FSA_V)
https://github.com/weberdc/find_hccs
1 Şen et al., 2016
10. OFFICIALASONAM 7-10 Dec 2020
Extract HCCs
10
Focal Structures Analysis1 – Variant (FSA_V)
https://github.com/weberdc/find_hccs
1 Şen et al., 2016
11. OFFICIALASONAM 7-10 Dec 2020
Evaluation
• Window size, γ = {15, 60, 360, 1440} minutes
• Community extraction:
• FSA_V, θ = 0.3
• K Nearest Neighour (kNN), k = ln(|V|) (cf. Cao et al., 2015)
• Threshold
• Coordination strategy
• Boost (co-retweet)
• Pollute (co-hashtag)
• Bully (co-mention)
11
12. OFFICIALASONAM 7-10 Dec 2020
Data
DS1 – Australian regional election, 2018
• Including ground truth (GT, cf. Keller et al., 2017)
DS2 – Twitter’s election integrity dataset1
• Internet Research Agency, 2016 tweets
12
1 https://about.twitter.com/en us/values/elections-integrity.html
Tweets
(T)
Retweets (RT) Accts
(A)
Days T / A /
Day
RT / A /
Day
DS1 115.9k 64.2k 54.5% 20.6k 18 0.31 0.17
- GT 4.2k 2.5k 59.7% 134 18 1.74 1.04
DS2 1.57m 729.9k 56.6% 1.4k 365 3.12 1.45
Ethics
University of Adelaide
HREC H-2018-045https://github.com/weberdc/find_hccs
13. OFFICIALASONAM 7-10 Dec 2020
Finding HCCs
• Coordination Strategies
• HCCs found in all
• Many components (HCCs), incl. a very large one
• kNN – single HCC with internal structure
13
DS1 DS2
FSA_V kNN Threshold FSA_V kNN Threshold
GT
Networks: Gephi https://gephi.org
15. OFFICIALASONAM 7-10 Dec 2020
Hashtags
15
Retweeting the same tweet
Retweeting the same account
GT DS1
DS2
16. OFFICIALASONAM 7-10 Dec 2020
Consistency
Hypothesis
• Dissemination groups should have
highly similar content
• i.e., Int. similarity ≥ Ext. similarity
Approach
• For each group:
• For each member:
• Combine member tweets into a corpus
• Compare 5-char n-grams of corpus
against all other accounts
• Plot similiarities as a matrix
cf. a heatmap
16
GT DS1
DS2 RANDOM
23. OFFICIALASONAM 7-10 Dec 2020
Literature
Campaign detection
• Content (Lee et al., 2013)
• URL sharing (Cao et al., 2015)
• Temporal signatures (Hine et al., 2017)
• Cross-platform linking (Starbird & Wilson, 2020)
Social bots
• Agenda-oriented automated accounts pretending to be human (Ferrara et al., 2016)
• Hard to identify (Cresci et al., 2017; Nasim et al., 2018; Grimme et al., 2018)
Coordination as “orchestrated activities”
• Focus on detecting strategies (Fisher, 2018; Grimme et al., 2018; Starbird et al., 2019;
Weber, 2019)
• Co-retweet (Weber, 2019; Graham et al., 2020)
• Co-hashtag (Woolley, 2016; Fisher, 2018)
• Co-URL (Cao et al., 2015; Giglietto et al., 2020)
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24. OFFICIALASONAM 7-10 Dec 2020
• Brooking, E. T., and Singer, P. W. (2016). War Goes Viral: How social media is being weaponized across the world. The Atlantic. Retrieved
from https://www.theatlantic.com/magazine/archive/2016/11/war-goes-viral/501125/
• Cao, C., Caverlee, J., Lee, K., Ge, H. and Chung, J. 2015. Organic or Organized?: Exploring URL Sharing Behavior. CIKM’15, 513-522.
• Cresci, S., Pietro, R. D., Petrocchi, M., Spognardi, A. and Tesconi, M. 2017. The Paradigm-Shift of Social Spambots. WWW’17 (Companion
Volume), 963-972.
• Ferrara, E., Varol, O., Davis, C., Menczer, F. and Flammini, A. 2016. The rise of social bots. Communications of the ACM. 59(7) (Jun. 2016),
96–104. DOI:10.1145/2818717.
• Fisher, A. 2018. Netwar in Cyberia: Decoding the Media Mujahidin. USC Centre on Public Diplomacy, Figueroa Press.
• Giglietto, F., Righetti, N., Rossi, L. and Marino, G. 2020. Coordinated Link Sharing Behavior as a Signal to Surface Sources of Problematic
Information on Facebook. SMSociety, 85-91.
• Grimme, C., Assenmacher, D. and Adam, L. 2018. Changing Perspectives: Is It Sufficient to Detect Social Bots? HCI (13) 2018, 445–461.
• Graham, T., Bruns, A., Zhu, G., and Campbell, R. 2020. Like a virus: The coordinated spread of coronavirus disinformation. Centre for
Responsible Technology, The Australia Institute.
• Hine, G. E., Onaolapo, J., Cristofaro, E. D., Kourtellis, N., Leontiadis, I., Samaras, R., Stringhini, G. and Blackburn, J. 2017. Kek, Cucks, and
God Emperor Trump: A Measurement Study of 4chan’s Politically Incorrect Forum and Its Effects on the Web. ICWSM’17, 92–101.
• Keller, F.B., Schoch, D., Stier, S. and Yang, J.H. 2017. How to Manipulate Social Media: Analyzing Political Astroturfing Using Ground Truth
Data from South Korea. ICWSM’17, 564–567
• Kumar, S., Hamilton, W.L., Leskovec, J. and Jurafsky, D. 2018. Community Interaction and Conflict on the Web. Proceedings of the 2018
World Wide Web Conference, WWW’18, 933–943 .
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References (1)
25. OFFICIALASONAM 7-10 Dec 2020
References (2)
• Lee, K., Caverlee, J., Cheng, Z. and Sui, D. Z. 2013. Campaign extraction from social media. ACM Transactions on Intelligent Systems and
Technology. 5(1), 9:1–9:28. DOI:10.1145/2542182.2542191.
• Lim, K. H., Jayasekara, S., Karunasekera, S., Harwood, A., Falzon, L., Dunn, J. and Burgess, G. 2019. RAPID: Real-time Analytics Platform
for Interactive Data Mining. KCML/PKDD (3) 2018. 649–653.
• Nasim, M., Nguyen, A., Lothian, N., Cope, R. and Mitchell, L. 2018. Real-time Detection of Content Polluters in Partially Observable Twitter
Networks. WWW’18 (Companion Volume), 1331-1339.
• Pacheco, D., Hui, P.-M., Torres-Lugo, C., Truong, B. T., Flammini, A. and Menczer, F. 2020-01-16. Uncovering Coordinated Networks on
Social Media. ICWSM’21, to appear.
• Rizoiu, M.-A., Graham, T., Zhang, R., Zhang, Y., Ackland, R. and Xie, L. 2018. #DebateNight: The Role and Influence of Socialbots on
Twitter During the 1st 2016 U.S. Presidential Debate. ICWSM’18, 300–309.
• Saulwick, A., & Trentelman, K. (2014). Towards a formal semantics of social influence. Knowledge-Based Systems, 71, 52–60.
DOI:10.1016/j.knosys.2014.06.022
• Şen, F., Wigand, R., Agarwal, N., Tokdemir, S., and Kasprzyk, R. 2016. Focal structures analysis: identifying influential sets of individuals in
a social network. Social Network Analysis and Mining, 6(1). DOI:10.1007/s13278-016-0319-z
• Starbird, K. and Wilson, T. 2020. Cross-Platform Disinformation Campaigns: Lessons Learned and Next Steps. Harvard Kennedy School
Misinformation Review. (Jan. 2020). DOI:10.37016/mr-2020-002.
• Starbird, K., Arif, A. and Wilson, T. 2019. Disinformation as Collaborative Work:: Surfacing the Participatory Nature of Strategic Information
Operations . Proc. ACM on Human-Computer Interaction. 3 (CSCW), 127:1–127:26. DOI:10.1145/3359229.
• Vo, N., Lee, K., Cao, C., Tran, T. and Choi, H. 2017. Revealing and detecting malicious retweeter groups. ASONAM’17, 363-368.
• Weber, D. 2019. On Coordinated Online Behaviour. Poster presented at ASNAC’19, Adelaide, Australia.
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Editor's Notes
In mid-November there was a sudden spate of almost identical tweets posted, starting with “My wife just told me that she voted for Joe Biden. As we speak we are getting divorced and I’m leaving for” somewhere, “Papers signed everything is done.. Absolutely disgusted with the 2020 elections, what a disgrace”.
This collection was posted by a data scientist with an interest in propaganda on social media.
As you can see, the language is almost, but not quite, identical, and based on other analyses, these are not bots. An earlier instance of this “copypasta” related to the sale of a football club, and a huge number of accounts (https://twitter.com/conspirator0/status/1299127612804075523) posted pretty much the same message, which, in the scheme of things, is relatively harmless. This example, however, could have effects such as reinforcing the idea of rejecting election results, which hurts democratic systems.
This kind of activity could be regarded as an information campaign, especially if it was seeded or supported by a foreign adversary, so it’s important to be able to identify such campaigns. As these accounts aren’t likely to be bots, bot detection systems won’t be as much help, unless they’re used for retweeting.
What is, however, of particular interest, is finding the groups of accounts who are working together to do this, and to see how they’re coordinating their activities.
Cf. “Swarmcast” p.10 from Fisher, A. 2018. “Netwar in Cyberia: Decoding the Media Mjahidin”, USC Center on Public Diplomacy, Paper 5.
Online marketing used to be just spam, now it’s political advertising
Anonymity is great for giving the disenfranchised a voice, but it also enables trolling, and I suspect Twitter’s latest ‘Fleets’ feature will make this worse.
Automation is great for news aggregators and sports announcement bots, but it allows accounts to post vast amounts of polarising, biased information, mis- and disinformation.
It’s now much easier for nation states to interfere with each other online discussion, particular political discussion.
When I refer to coordination, there’s a spectrum.
Fundamentally, as described by Malone and Crowston in the 90s, it’s the alignment of dependencies between tasks and the resources they use.
At higher levels are Starbird et al’s descriptions of information campaigns being orchestrated (run from on high), cultivated (e.g., infiltrating existing issue-motivated groups), or emergent (like in conspiracy communities).
In between is the space where specific communication actions occur. In our case, we’re interested in social media communications, which have many parallels, so methods to detecting reposting may apply to Twitter retweets, Facebook shares, or Tumblr Reposts.
Intent
Convince population to lose weight
Strategy
Ad blitz + word of mouth + tax incentives
(Planning)
Talk to ad company, design/disseminate flyers, craft legislation
Execution
TV ads, social media ads, flyers in fast food shops, pass laws & enforce
These are some coordination strategies observed in the literature, …
This is not an exhaustive list, of course, and coordination of activity may take many guises, but we’re focusing on co-actions, where a pair of accounts do the same thing to achieve their goal.
To find the highly coordinating communities, or HCCs, we first
[CLICK] Extract the abstracted common interaction behaviours from social media posts of a variety of platforms;
[CLICK] Then create a multi-digraph of interactions between users, hashtags and URLs.
[CLICK] This interaction graph is mined for evidence of coordination based on a search criterion, which is general and could be quite domain-specific. Examples of these include amplify-by-repost which is deliberate dissemination through content sharing, channel pollution through posting to particular hashtags or other communities, and coordinated attacks on a single user or community.
[CLICK] A latent coordination network is constructed from this evidence, being a weighted undirected network of users,
[CLICK] And this is then mined for the most highly coordinating communities.
That covers how to search for HCCs in social media data, but we need to consider its temporal aspect.
[CLICK] (shrink) [CLICK] (next slide)
Given a timeline of social media posts, we segment them into windows of gamma minutes.
We can vary the window depending on the nature of coordination sought.
Lots of variables, so I’ll mostly focus on 15m windows, FSA_V and Boost via co-retweet.
Threshold = retain heaviest normalised edges above 0.1
Clearly different groups are found.
The kNN results consist of a single large component, but I’ve used Gephi’s Force Atlas and then Fruchter Rheingold layouts to identify internal structures, and then applied the Louvain method to identify the clusters by colour. I’ve done this for visualisation purposes, but have not analysed the networks any more deeply yet.
[CLICK] The final HCCs are the ones I discovered in the ground truth – each component consists of accounts from a different political party.
How similar is the membership between the HCCs discovered in different window sizes? Quite a lot of variation.
Different HCCs have clearly different content – looking at their hashtags, we can get a feel for what their interests are.
Internal vs External focus
Internal retweet ratio – how often do they retweet themselves?
Internal mention ratio – how often do they mention themselves?
Remember that the members of HCCs need not be directly connected – all their connections may be inferred.
Looked at Twitter data over the recent 2020 US Democratic and Republican Conventions back in August.
Using co-retweet and a 10 second window, I identified a number of communities, all of which Botometer tells us are highly bot-like, and many of which present themselves as normal people, but with greatly inflated tweeting rates.
Named accounts are organisational or have been deleted.
Co-hashtag with 10 second window to find the HCCs, then added the hashtags they use back in, to see which HCCs are associated.
Structures tells us something about behaviour:
Clusters around a few hashtags says many groups are, in fact, one
Isolated stars tell us the accounts are pushing an agenda (the content of the hashtags) and no one else here is interested
Fans are pushing an agenda but have connected with other communities here via the linking hashtags
This tells me the community extraction method that I used (FSA_V) could do with a bit of tweaking and perhaps communities could be stitched back together.
Future:
Statistical measures
Evolution of HCCs
Simulation of coordination strategies
Campaign detection spawned out of spam detection but has relied on a number of features over the years.
Automation detection is particular important for social bots, accounts that masquerade as real humans, but are, in fact, automated.
It’s so hard to tell bots and humans apart that the real question is more about how they work together to achieve goals – how do they orchestrate their activities? For example, by disseminating content by retweeting the same tweets, or URLs, or using the same hashtags.