This document introduces the topic of studying social media in political communication. It discusses how social media may contribute to selective exposure, fragmentation, and polarization due to people selectively exposing themselves to ideologically congruent content. It also examines how politicians are using social media to directly communicate with citizens. The case study aims to leverage computational social science methods and large-scale data analysis to provide new insights into these topics. Students will work on a project analyzing political content on social media over the course of the case study.
1) Traditional assumptions about how people consume news through a fixed set of outlets are incorrect in today's fragmented media environment.
2) News consumption involves three layers - media type, individual outlets, and the gateway through which people access the outlet (e.g. website, app, social media).
3) Researchers need to account for this third layer when studying people's news repertoires to fully capture how news flows in the digital age.
This document summarizes a presentation on analyzing political communication data from Twitter. It discusses analyzing the structure of Twitter data by examining things like retweet networks and interaction patterns, versus analyzing the content of tweets by looking at topics, sentiments, and word frequencies. It provides examples of studies that take both structural and content-based approaches. Specifically, it examines studies that analyzed how Twitter discussions relate to televised political debates and who engages in uncivil language online. The presentation concludes that the most insightful approach is often to combine structural and content-based analyses.
This document proposes conceptualizing and measuring news exposure as a network of users and news items. It outlines some common assumptions about news consumption that are outdated, such as people using a fixed set of news outlets. The document then presents a model where news items and users are represented as nodes, and their connections as edges. For example, an edge between a user and news item would indicate the user has read that item. Implementing this model involves collecting data on users, news items, and their connections to build a graph database that can be used to analyze news diffusion and exposure. This network approach is presented as an improved way to measure individual-level exposure to specific news items in today's unbundled media environment.
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?
Big social data analytics - social network analysis Jari Jussila
This document discusses social network analysis and visualization of Twitter data using tools like Gephi. It provides steps to collect Twitter data using an API script, create a network file from the data, and calculate network metrics and visualize the network in Gephi. Key aspects covered include extracting tweet data, creating a network file with NetworkX, uploading files to PythonAnywhere to run the script, and analyzing and visualizing the resulting network in Gephi to understand information diffusion on Twitter.
This document discusses social networks and graph analysis. It provides an overview of social network analysis and how graphs can be used to model real-world networks. It also covers basic graph theory concepts like types of graphs, graph algorithms, and examples of how graphs are used in domains like social media analysis and transportation. Graphs are useful for abstracting relationships in data and enabling large-scale computations to derive insights.
10 More than a Pretty Picture: Visual Thinking in Network Studiesdnac
Visualization has been important in network science since its beginnings to make invisible structures visible. While metrics can describe networks, visualizations allow researchers to see relationships and patterns across multiple dimensions that numbers alone cannot reveal. Effective network visualizations communicate insights that would be difficult to understand otherwise, by depicting global patterns and local details simultaneously in a way that builds intuition about the network's structure and generating processes. However, challenges include lack of consistent display frameworks, integrating too much multidimensional information, and issues of scale for large and dynamic networks.
1) Traditional assumptions about how people consume news through a fixed set of outlets are incorrect in today's fragmented media environment.
2) News consumption involves three layers - media type, individual outlets, and the gateway through which people access the outlet (e.g. website, app, social media).
3) Researchers need to account for this third layer when studying people's news repertoires to fully capture how news flows in the digital age.
This document summarizes a presentation on analyzing political communication data from Twitter. It discusses analyzing the structure of Twitter data by examining things like retweet networks and interaction patterns, versus analyzing the content of tweets by looking at topics, sentiments, and word frequencies. It provides examples of studies that take both structural and content-based approaches. Specifically, it examines studies that analyzed how Twitter discussions relate to televised political debates and who engages in uncivil language online. The presentation concludes that the most insightful approach is often to combine structural and content-based analyses.
This document proposes conceptualizing and measuring news exposure as a network of users and news items. It outlines some common assumptions about news consumption that are outdated, such as people using a fixed set of news outlets. The document then presents a model where news items and users are represented as nodes, and their connections as edges. For example, an edge between a user and news item would indicate the user has read that item. Implementing this model involves collecting data on users, news items, and their connections to build a graph database that can be used to analyze news diffusion and exposure. This network approach is presented as an improved way to measure individual-level exposure to specific news items in today's unbundled media environment.
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?
Big social data analytics - social network analysis Jari Jussila
This document discusses social network analysis and visualization of Twitter data using tools like Gephi. It provides steps to collect Twitter data using an API script, create a network file from the data, and calculate network metrics and visualize the network in Gephi. Key aspects covered include extracting tweet data, creating a network file with NetworkX, uploading files to PythonAnywhere to run the script, and analyzing and visualizing the resulting network in Gephi to understand information diffusion on Twitter.
This document discusses social networks and graph analysis. It provides an overview of social network analysis and how graphs can be used to model real-world networks. It also covers basic graph theory concepts like types of graphs, graph algorithms, and examples of how graphs are used in domains like social media analysis and transportation. Graphs are useful for abstracting relationships in data and enabling large-scale computations to derive insights.
10 More than a Pretty Picture: Visual Thinking in Network Studiesdnac
Visualization has been important in network science since its beginnings to make invisible structures visible. While metrics can describe networks, visualizations allow researchers to see relationships and patterns across multiple dimensions that numbers alone cannot reveal. Effective network visualizations communicate insights that would be difficult to understand otherwise, by depicting global patterns and local details simultaneously in a way that builds intuition about the network's structure and generating processes. However, challenges include lack of consistent display frameworks, integrating too much multidimensional information, and issues of scale for large and dynamic networks.
Jankowski & van selm, promise and practice of public debate, 2000Nick Jankowski
This document summarizes three studies that empirically investigated public debates in cyberspace to assess claims about digital democracy. The first study analyzed a year-long Usenet discussion on abortion, finding it was diverse and reciprocal but lacked equality and high-quality discourse. The second was an experiment with software to support an online debate about land use policy among 100 invited participants in the Netherlands. The third studied an online debate between senior citizens and political candidates before an election. The document reviews different perspectives on the promises of digital democracy around information, deliberation and decision-making, and suggests more research is needed to properly evaluate these initiatives.
This document discusses social network analysis and its applications. It defines a social network as a social structure made up of individuals or organizations connected by relationships. Social network analysis maps and measures these relationships to derive insights. The document provides an overview of key social network analysis concepts like nodes, edges, degrees of separation, centrality, and communities. It then discusses various business applications of social network analysis like churn reduction, cross-selling, marketing, and competitive analysis. Overall, the document promotes social network analysis as a technique for understanding relationship dynamics and improving business outcomes.
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
Researching Social Media – Big Data and Social Media AnalysisFarida Vis
Researching Social Media – Big Data and Social Media Analysis, presentation for the Social Media for Researchers: A Sheffield Universities Social Media Symposium, 23 September 2014
This document analyzes 653 tweets containing the words "public relations" or the acronym "PR" to understand how Twitter contributes to the development of public relations theory and practice. The tweets were categorized and the following key findings were reported:
1. The most common categories were announcements/events (28.6%) and discussions between users (18.7%), showing Twitter's role in networking and sharing information.
2. Press releases (4.3%) and jobs (15.2%) were also frequently discussed, demonstrating Twitter's use for professional purposes in PR.
3. Unexpectedly, few tweets discussed academic topics (2.3%), suggesting PR scholars do not often use Twitter, though it
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/
Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavi...Axel Bruns
Paper by Magdalena Wischnewski, Axel Bruns, and Tobias Keller, presented at the 2021 International Communication Association conference, 27-31 May 2021.
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).
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Echo Chamber? What Echo Chamber? Reviewing the EvidenceAxel Bruns
This document summarizes a study examining evidence for echo chambers and filter bubbles on Twitter in Australia. The study analyzed follower connections and engagement patterns between Twitter accounts with over 1,000 followers. It found limited evidence of highly exclusionary echo chambers or filter bubbles, except for some specialist clusters. Most accounts had more external than internal connections or balanced engagement. Retweets generally spread information more externally than @mentions. The study has limitations but provides little support for strong exclusionary information silos within the Australian Twittersphere.
Studying Online Food Consumption and Production Patterns: Recent Trends and C...Christoph Trattner
Food is a fundamental concept in our daily lives and is one of the most important factors that shape how healthy we are or how good we feel. Although research on the users’ food preferences has been a well-established research area over the last decades, only very little research was devoted yet to understand, how the World Wide Web influences the way we consume or produce food offline.
In this talk, I will therefore highlight recent research in online food communities and interesting findings in terms of online food recipe consumption and production patterns. I will show, how these studies might be useful when drawing conclusions about health related issues, such as obesity or diabetes of a large population, and how these insights might be used to tune current recommender approaches in this domain.
Last but not least, I will discuss the limitations of these studies and will highlight the need for a joined European taskforce, that studies food, health related issues and recommender systems on a much larger and more useful way, as proposed by current research in this area.
Social Media Analysis: Present and Futurematthewhurst
Current social media analysis separates content and structure analysis, missing opportunities. A unified theory is needed to better analyze social media data by considering both content and structure together. While current applications focus on marketing, a broader business intelligence approach could expand the field. Developing richer models of business domains and processes would increase the actionability and market for social media analysis tools.
Social network analysis & Big Data - Telecommunications and moreWael Elrifai
Social Network Analysis: Practical Uses and Implementation is a presentation that discusses social network analysis and its uses. It covers key topics such as defining social networks and social network analysis, why social network analysis is important, identifying influencers in social networks, roles in social networks, graph theory concepts used in social network analysis, calculating metrics from social networks, and recommended approaches to social network analysis. The presentation provides an overview of social network analysis concepts and their practical applications.
A presentation describing application of Node XL into analyzing social networks.
Made as part of project work for ITB course at VGSOM IIT Kharagpur.
By : Mayank Mohan
Anuradha Chakraborty
( Batch of 2012)
The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts, principles, and methods in various scenarios of social media mining.
Details at: http://dmml.asu.edu/smm/
Infotainment and the Impact of Connective Action: The Case of #MilkedDryAxel Bruns
This document discusses the #MilkedDry campaign, which aimed to raise awareness of issues facing Australian dairy farmers. It analyzes the campaign through the lens of "connective action" and social media data. The analysis found some evidence of the campaign spreading awareness but little active engagement on Twitter specifically. While not a clear example of "connective action", the segment may have still raised public awareness through other means. Overall, the document examines the role and impact of infotainment and social media in facilitating political discussion and action.
These slides are for my talk for the Somerville College Mathematics Reunion ("Somerville Maths Reunion", 6/24/17): http://www.some.ox.ac.uk/event/somerville-maths-reunion/
The Design of an Online Social Network Site for Emergency Management: A One-S...guest636475b
Web 2.0 is creating new opportunities for communication and collaboration. Part of this explosion is the increase in popularity and use of Social Network Sites (SNSs) for general and domain-specific use. In the emergency domain there are a number of websites, wikis, SNSs, etc. but they stand as silos in the field, unable to allow for cross-site collaboration. In this paper we describe ongoing design science research to develop and refine guiding principles for developing an SNS that will bring together emergency domain professionals in a “one-stop-shop.” We surveyed emergency professionals who study crisis information systems, to ascertain potential functionalities of such an SNS. Preliminary results suggest that there is a need for the envisioned SNS. Future research will continue to explore possible solutions to issues addressed in this paper.
Accessing and Using Big Data to Advance Social Science KnowledgeJosh Cowls
This document summarizes a project investigating the use of big data to advance social science knowledge. It introduces the project leaders and discusses data sources and scope. It then focuses on defining big data, discussing how digital data represents real-world objects and phenomena, and the opportunities and limits this presents. Challenges of using big data to gauge public opinion are also examined, such as issues of representativeness, reliability, and replicability. The document concludes by listing project papers on this topic.
Jankowski & van selm, promise and practice of public debate, 2000Nick Jankowski
This document summarizes three studies that empirically investigated public debates in cyberspace to assess claims about digital democracy. The first study analyzed a year-long Usenet discussion on abortion, finding it was diverse and reciprocal but lacked equality and high-quality discourse. The second was an experiment with software to support an online debate about land use policy among 100 invited participants in the Netherlands. The third studied an online debate between senior citizens and political candidates before an election. The document reviews different perspectives on the promises of digital democracy around information, deliberation and decision-making, and suggests more research is needed to properly evaluate these initiatives.
This document discusses social network analysis and its applications. It defines a social network as a social structure made up of individuals or organizations connected by relationships. Social network analysis maps and measures these relationships to derive insights. The document provides an overview of key social network analysis concepts like nodes, edges, degrees of separation, centrality, and communities. It then discusses various business applications of social network analysis like churn reduction, cross-selling, marketing, and competitive analysis. Overall, the document promotes social network analysis as a technique for understanding relationship dynamics and improving business outcomes.
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
Researching Social Media – Big Data and Social Media AnalysisFarida Vis
Researching Social Media – Big Data and Social Media Analysis, presentation for the Social Media for Researchers: A Sheffield Universities Social Media Symposium, 23 September 2014
This document analyzes 653 tweets containing the words "public relations" or the acronym "PR" to understand how Twitter contributes to the development of public relations theory and practice. The tweets were categorized and the following key findings were reported:
1. The most common categories were announcements/events (28.6%) and discussions between users (18.7%), showing Twitter's role in networking and sharing information.
2. Press releases (4.3%) and jobs (15.2%) were also frequently discussed, demonstrating Twitter's use for professional purposes in PR.
3. Unexpectedly, few tweets discussed academic topics (2.3%), suggesting PR scholars do not often use Twitter, though it
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/
Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavi...Axel Bruns
Paper by Magdalena Wischnewski, Axel Bruns, and Tobias Keller, presented at the 2021 International Communication Association conference, 27-31 May 2021.
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).
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Echo Chamber? What Echo Chamber? Reviewing the EvidenceAxel Bruns
This document summarizes a study examining evidence for echo chambers and filter bubbles on Twitter in Australia. The study analyzed follower connections and engagement patterns between Twitter accounts with over 1,000 followers. It found limited evidence of highly exclusionary echo chambers or filter bubbles, except for some specialist clusters. Most accounts had more external than internal connections or balanced engagement. Retweets generally spread information more externally than @mentions. The study has limitations but provides little support for strong exclusionary information silos within the Australian Twittersphere.
Studying Online Food Consumption and Production Patterns: Recent Trends and C...Christoph Trattner
Food is a fundamental concept in our daily lives and is one of the most important factors that shape how healthy we are or how good we feel. Although research on the users’ food preferences has been a well-established research area over the last decades, only very little research was devoted yet to understand, how the World Wide Web influences the way we consume or produce food offline.
In this talk, I will therefore highlight recent research in online food communities and interesting findings in terms of online food recipe consumption and production patterns. I will show, how these studies might be useful when drawing conclusions about health related issues, such as obesity or diabetes of a large population, and how these insights might be used to tune current recommender approaches in this domain.
Last but not least, I will discuss the limitations of these studies and will highlight the need for a joined European taskforce, that studies food, health related issues and recommender systems on a much larger and more useful way, as proposed by current research in this area.
Social Media Analysis: Present and Futurematthewhurst
Current social media analysis separates content and structure analysis, missing opportunities. A unified theory is needed to better analyze social media data by considering both content and structure together. While current applications focus on marketing, a broader business intelligence approach could expand the field. Developing richer models of business domains and processes would increase the actionability and market for social media analysis tools.
Social network analysis & Big Data - Telecommunications and moreWael Elrifai
Social Network Analysis: Practical Uses and Implementation is a presentation that discusses social network analysis and its uses. It covers key topics such as defining social networks and social network analysis, why social network analysis is important, identifying influencers in social networks, roles in social networks, graph theory concepts used in social network analysis, calculating metrics from social networks, and recommended approaches to social network analysis. The presentation provides an overview of social network analysis concepts and their practical applications.
A presentation describing application of Node XL into analyzing social networks.
Made as part of project work for ITB course at VGSOM IIT Kharagpur.
By : Mayank Mohan
Anuradha Chakraborty
( Batch of 2012)
The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts, principles, and methods in various scenarios of social media mining.
Details at: http://dmml.asu.edu/smm/
Infotainment and the Impact of Connective Action: The Case of #MilkedDryAxel Bruns
This document discusses the #MilkedDry campaign, which aimed to raise awareness of issues facing Australian dairy farmers. It analyzes the campaign through the lens of "connective action" and social media data. The analysis found some evidence of the campaign spreading awareness but little active engagement on Twitter specifically. While not a clear example of "connective action", the segment may have still raised public awareness through other means. Overall, the document examines the role and impact of infotainment and social media in facilitating political discussion and action.
These slides are for my talk for the Somerville College Mathematics Reunion ("Somerville Maths Reunion", 6/24/17): http://www.some.ox.ac.uk/event/somerville-maths-reunion/
The Design of an Online Social Network Site for Emergency Management: A One-S...guest636475b
Web 2.0 is creating new opportunities for communication and collaboration. Part of this explosion is the increase in popularity and use of Social Network Sites (SNSs) for general and domain-specific use. In the emergency domain there are a number of websites, wikis, SNSs, etc. but they stand as silos in the field, unable to allow for cross-site collaboration. In this paper we describe ongoing design science research to develop and refine guiding principles for developing an SNS that will bring together emergency domain professionals in a “one-stop-shop.” We surveyed emergency professionals who study crisis information systems, to ascertain potential functionalities of such an SNS. Preliminary results suggest that there is a need for the envisioned SNS. Future research will continue to explore possible solutions to issues addressed in this paper.
Accessing and Using Big Data to Advance Social Science KnowledgeJosh Cowls
This document summarizes a project investigating the use of big data to advance social science knowledge. It introduces the project leaders and discusses data sources and scope. It then focuses on defining big data, discussing how digital data represents real-world objects and phenomena, and the opportunities and limits this presents. Challenges of using big data to gauge public opinion are also examined, such as issues of representativeness, reliability, and replicability. The document concludes by listing project papers on this topic.
Puffles the dragon fairy emerged on Twitter in 2010 while the author worked in the civil service with no social media guidance. Puffles grew a following of over 6,000 by engaging in conversations and sharing interesting content. This led to mainstream media coverage and invitations to events. The author used Puffles to advocate for social media guidance for civil servants and provide input to the Cabinet Office. Puffles' influence grew to include interactions with politicians and participation in parliamentary debates. The story illustrates how social media can expand access to knowledge and engagement in policy issues beyond traditional structures.
Digital Democracy by Katarzyna Anna Klimowicz and David Duenas-CidLuke Turkus Solarski
Presentation by Katarzyna Anna Klimowicz and David Duenas-Cid.
Until recently, digital democracy might have seemed like a scenario straight out of science fiction. Today, thanks to the innovative efforts of so-called „network” or „digital parties” (such as the International Pirate Party network, the Podemos party and the Barcelona en Comu movement) and other actors specializing in the design and implementation of online platforms for collective decision-making and other participatory digital tools, various forms of online participation are becoming an increasingly integral part of our hybrid reality.
Does it always really work that way? And what are the challenges involved?
My name is Katarzyna Anna Klimowicz and I invite you to the next meeting in the Teal Breakfast series, where my colleague David Duenas-Cid and I will try to answer some of these big questions and discuss with you how technology combines with politics.
The ethics of privacy in sharing culture 2016Zoetanya Sujon
1. The document summarizes a study on privacy and sharing culture among young people in London. It examines how they understand and manage privacy through diary entries and surveys.
2. It finds that respondents care deeply about social privacy and control over personal information. They see privacy as a way to protect their individuality and choose what to share.
3. Respondents also curate a "public persona" on social media, depicting a happier version of themselves. They employ strategies like private sharing on apps and depersonalizing content to balance privacy and sharing.
Digital Humanities and “Digital” Social SciencesChantal van Son
This document provides an overview of a meeting discussing digital humanities and digital social sciences. It begins with an introduction to the day's schedule, which includes presentations on projects in digital humanities focusing on data quality and representation of perspectives in text. Projects in digital social sciences are also discussed, including analyzing bias and engagement in political social media. The document then discusses similarities and differences between humanities and social sciences, as well as how data science relates to both fields. Key challenges and opportunities for using digital methods in each discipline are outlined. The document concludes with an introduction to a discussion on further collaborations between disciplines.
China 2016: My understanding of the history of quantitative science communic...John C. Besley
Presentation given at Nanjing Agriculture University, May 2016. Provides an initial overview of how I think about the field with a focus on the central role of agricultural university and increasing interest in the face of the challenge of issues such as climate change and various emerging technologies that the public sees as potentially risky.
This document discusses the challenges of using social media as both the object and instrument of research. It notes that social media research aims both to study social media as a phenomenon and to gain empirical insights into social life through social media data. However, distinguishing these aims can be difficult. The document then examines what constitutes the "social" in social media, noting that this depends on platform features, user-generated content, context, social metadata, methods, and combinations of these factors. It argues that the social cannot be taken as a given, but must be empirically detected. The document concludes that social media research requires flexible, adjustable methods that can align different components like platform, data, measures and context to address specific research questions.
This document summarizes a presentation given by Dr. Tracey P. Lauriault on data power. The presentation discusses how data is not objective and exists within social contexts. It also examines how data analytics reflect particular worldviews and epistemologies. Additionally, the presentation explores the concept of data assemblages and how data is part of larger socio-technical systems. Finally, it poses critical questions about data power and the role of data science in understanding and navigating issues of datafication.
1. Warnings work best when they take into account basic human tendencies to think they are safe rather than at risk, and to confirm warnings through social interaction before acting.
2. Effective warning system design integrates monitoring, detection, management, public response, and linkages between them, and ensures subsystems and linkages function properly through planning, training and exercises.
3. Social media could help design warning systems by integrating all elements and actors to avoid failures, and linking subsystems that rarely interact.
This is an invited talk I presented at the University of Zurich, speakers' series 2.10.2017. The presentation is based on the following paper: Brandtzaeg, P. B., & Følstad, A. (2017). Trust and distrust in online fact-checking services. Communications of the ACM. 60(9): 65-71
The document discusses issues around 'We Media' and democracy. It provides questions to consider regarding how 'We Media' has emerged and both the positive and negative impacts on democracy. Theories from thinkers like Chomsky, Curtis, Gillmor, and Keen are referenced regarding how the contemporary media may be both more democratic through citizen participation but also less democratic through issues like surveillance, control and lack of experts. The document also briefly outlines the potential structure for an exam answer on these topics.
This document provides an overview of a study using an (n)ethnographic method to examine political participation on social media. It discusses three studies: 1) a case study of a politician, Nina Larsson, using social media for her campaign; 2) studying activists' use of social media; and 3) examining popular cultural participation on forums. The method involves both traditional ethnography (observation, interviews) and netnography of online spaces. Two problems are discussed: 1) issues analyzing Nina as a case study since she is an outlier in her social media use; and 2) ethical considerations around studying non-public online communities.
The document discusses how social media is changing how policy is developed and communicated within the UK government or Whitehall. It defines social media and how it differs from traditional media. It explores how individuals use social networks to find support from others, gain knowledge from crowdsourcing, and challenge those in authority. The document argues that knowledge is no longer concentrated within large organizations, but is dispersed among individuals online. It presents challenges this poses for how government ministers announce and discuss policies.
This document provides an overview of a presentation on big data in the social sciences given by Ralph Schroeder and Eric Meyer at the Oxford Internet Institute Summer Doctoral Programme. The presentation discusses how big data relates to advancing social science research, including opportunities for unprecedented insights but also challenges regarding data quality and replicability. Three case studies are summarized that demonstrate novel uses of big data: analyzing search engine queries, text analysis of digitized books, and identifying Twitter bots. The presentation concludes by considering debates around whether new technologies are outpacing social science and the threats and opportunities of new data sources.
The document discusses techniques for scaling up automated content analysis projects. It begins by looking back at the workflow and techniques covered in previous sessions, such as developing components separately, writing functions, and making the code robust. It then looks forward by discussing additional techniques that were not covered, such as using Selenium for dynamic web scraping, databases for storing large datasets, word embeddings, and more advanced natural language processing and machine learning models. The document also introduces the INCA project, which aims to scale up content analysis by collecting data in a way that allows for reuse across multiple projects, using a database backend and reusable preprocessing and analysis code. The goal is to make automated content analysis usable with minimal Python knowledge.
This document provides a summary of a meeting on machine learning. It recaps unsupervised and supervised machine learning techniques. Unsupervised techniques discussed include principal component analysis (PCA) and latent Dirichlet allocation (LDA). PCA is used to find how words co-occur in documents. LDA can be implemented in Python using gensim to infer topics in a collection of documents. Supervised machine learning techniques the audience has previously used are regression models. The document concludes by noting models will only use a portion of available data for training and validation.
This document provides an overview of using statistics in Python with Pandas. It discusses general considerations for using Python for statistics rather than exporting data to another program. Useful Python packages for statistics like NumPy, SciPy, statsmodels, and matplotlib are introduced. The document demonstrates how to work with Pandas dataframes, including descriptive statistics, plotting, and linear regression. An upcoming exercise will provide hands-on practice of these skills.
This document discusses last week's coding exercise on data harvesting and storage. It provides step-by-step explanations of code used to extract and analyze data from a JSON file. Examples include printing video titles, calculating average tags per video, determining the most commented on porn category, and finding the most frequently used words. The document also covers APIs, scrapers, file formats like JSON and CSV, and how to store extracted data.
This document provides an introduction to basic Python programming concepts like datatypes, functions, modifying lists and dictionaries, and indentation. It explains that Python uses indentation through spaces or tabs to structure code blocks that are executed repeatedly or under certain conditions. Examples are given for defining functions, appending and merging lists, adding keys to dictionaries, and using indentation with for, if/elif/else, and try/except blocks.
This document outlines an introductory course on big data and automated content analysis. It covers using the Linux command line, writing and running Python code, and announces upcoming meetings. The course will introduce tools like the Linux terminal and Python, explain why they are useful for big data tasks, and provide exercises for students to practice these skills, such as writing simple Python programs. Upcoming meetings are scheduled for weeks 2 and 3 to continue lectures and lab sessions on using Python.
This document provides an overview of a course on Big Data and Automated Content Analysis. It introduces the instructor, Damian Trilling, and a PhD student, Joanna Strycharz. It then discusses definitions of Big Data, implications and criticisms, and whether the techniques used in the course constitute Big Data research. Next, it outlines methods that will be covered, including data collection, analysis techniques, and the programming language Python. Finally, it discusses reasons for building one's own tools rather than using commercial software.
This document summarizes a presentation on unsupervised and supervised machine learning techniques for automated content analysis. It recaps types of automated content analysis, describes unsupervised techniques like principal component analysis (PCA) and latent Dirichlet allocation (LDA), and supervised machine learning techniques like regression. It provides examples of applying these techniques to cluster Facebook messages and predict newspaper reading. The document concludes by noting the presenter will use a portion of labeled data to estimate models and check predictions against the remaining labeled data.
The document discusses web scraping and outlines a step-by-step process for scraping comments from a Dutch website called GeenStijl. It begins with using regular expressions to scrape the comments, but notes that existing parsers can make the process more elegant, especially for complex websites. It then demonstrates using the lxml module and XPath to scrape reviews from another site in a more structured way. The document provides remarks on regular expressions and XPath, and encourages exploring different scraping techniques.
This document provides an overview of a presentation on automated content analysis using regular expressions and natural language processing. The presentation covers topics like bottom-up vs top-down analysis, what regular expressions are and how they can be used in Python, stemming, parsing sentences, and combining techniques like stemming and stopword removal. Examples are given on using regular expressions to count actors in articles and check the number of a document from LexisNexis. The takeaway message is about an upcoming take-home exam and future meetings.
The document discusses different types of analysis for automated content analysis, including sentiment analysis and stopword removal. It covers bag-of-words approaches to sentiment analysis, which involve comparing words in a text to lists of positive and negative words. More advanced approaches are mentioned that take the structure of text into account, such as identifying sentence structure using linguistic concepts.
This document discusses a lecture on data harvesting and storage. It covers APIs, RSS feeds, scraping and crawling as methods for collecting data from various sources. It also discusses storing data in formats like CSV, JSON, and XML. The document provides code examples for working with JSON data and discusses tools for long-term data collection like DMI-TCAT.
This document summarizes a presentation on the basics of Python programming. It introduces fundamental Python concepts like datatypes, functions, methods, and indentation-based code structuring. It also announces an exercise for the attendees to practice these basics and previews upcoming meetings that will involve working with structured datasets in Python.
This document summarizes a presentation on analyzing word co-occurrences in text data using network analysis techniques. It discusses counting the frequency of word combinations, representing the co-occurrence data as a network with nodes for words and edges for co-occurrences, and visualizing the network in Gephi. It also provides an example analysis of tweets about a political debate, examining which topics were emphasized by each candidate based on word associations on Twitter.
More from Department of Communication Science, University of Amsterdam (20)
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
MATATAG CURRICULUM: ASSESSING THE READINESS OF ELEM. PUBLIC SCHOOL TEACHERS I...NelTorrente
In this research, it concludes that while the readiness of teachers in Caloocan City to implement the MATATAG Curriculum is generally positive, targeted efforts in professional development, resource distribution, support networks, and comprehensive preparation can address the existing gaps and ensure successful curriculum implementation.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
1. Introducing. . . Studying social media in poltical communication Conclusion
Fundamentals of Data Science: Case “Political
Communication”
Damian Trilling
d.c.trilling@uva.nl
@damian0604
www.damiantrilling.net
Afdeling Communicatiewetenschap
Universiteit van Amsterdam
12-09-2016
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
2. Introducing. . . Studying social media in poltical communication Conclusion
Today
1 Introducing. . .
. . . the people
. . . the schedule
. . . the topic
2 Studying social media in poltical communication
Selective exposure and filter bubbles
Fragmentation
Polarization
Politicians on social media
Social media and public opinion
3 Conclusion
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
3. Introducing. . . Studying social media in poltical communication Conclusion
. . . the people
Introducing. . .
. . . the people
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
4. Introducing. . . Studying social media in poltical communication Conclusion
. . . the people
Introducing. . .
Stevan dr. Stevan Rudinac
Postdoctoral Researcher @ Intelligent Sensory
Information Systems // IvI // UvA
• received PhD degree in Computer Science @
TU Delft 2013
• graduated in Electrical Engineering @
University of Belgrade 2006
• worked @ NFI, TU Delft, TU Eindhoven and
University of Belgrade
• interested in multimedia information retrieval
with a focus on urban computing and security
applications.
s.rudinac@uva.nl Science Park 904 C3.253
https://staff.fnwi.uva.nl/s.rudinac/
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
5. Introducing. . . Studying social media in poltical communication Conclusion
. . . the people
Introducing. . .
Damian
dr. Damian Trilling
Assistant Professor Political Communication &
Journalism
• studied Communication Science in Münster
and at the VU 2003–2009
• PhD candidate @ UvA 2009–2012
• interested in political communication and
journalism in a changing media environment
and in innovative (digital, large-scale,
computational) research methods
@damian0604 d.c.trilling@uva.nl
REC-C 8th
floor www.damiantrilling.net
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
6. Introducing. . . Studying social media in poltical communication Conclusion
. . . the people
Introducing. . .
You
Your name?
Your background?
Your reason to follow this course?
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
7. Introducing. . . Studying social media in poltical communication Conclusion
. . . the schedule
Introducing. . .
. . . the schedule
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
8. Monday, 12 Sept., 11–13 (Damian)
Intro to our social science case:
Social media analysis in political communication
Tuesday, 13 Sept., 9-11 (Stevan)
The research pipeline
Working with the Twitter API
Data preprocessing and sentiment analysis
Tuesday, 13 Sept., 13-17 (you)
Project: It’s your turn!
Thursday, 15 Sept., 9-11 (Stevan & Damian)
Practical work: Helping you with the project
Thursday, 15 Sept., 11-17 (you)
Project: It’s your turn!
Thursday, 15 Sept., 17-19 (Stevan & Damian)
Presentations: Teams pitching the progress
9. Monday, 19 Sept., 11–13 (Damian)
Analyzing political content on social media: Examples of research so far
Tuesday, 20 Sept., 9-11 (Stevan)
Topic analysis, correlations, visualization
Guest presentation Joost Boonzajer Flaes, Twitter UK
Tuesday, 20 Sept., 13-17 (you)
Project: It’s your turn!
Thursday, 22 Sept., 9-11 (Stevan & Damian)
Practical work: Helping you with the project
Thursday, 22 Sept., 11-17 (you)
Project: It’s your turn!
Thursday, 22 Sept., 17-19 (Stevan & Damian)
Presentations: Teams pitching the final results
10. Introducing. . . Studying social media in poltical communication Conclusion
. . . the topic
Introducing. . .
. . . the topic
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
11. Introducing. . . Studying social media in poltical communication Conclusion
. . . the topic
This case is about
social sciences
⇒communication science
⇒political communication
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
12. Introducing. . . Studying social media in poltical communication Conclusion
. . . the topic
What is Communication Science?
• looks at communication between actors in society
• is one of the empirical social sciences
• (mainly) focuses on mediated communication rather than
interpersonal communication
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
13. Introducing. . . Studying social media in poltical communication Conclusion
. . . the topic
Political communication
journalists citizens
political actors
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
14. Introducing. . . Studying social media in poltical communication Conclusion
. . . the topic
Methods to study communication
qualitative
• discourse analysis
• interviews
• focus groups
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
15. Introducing. . . Studying social media in poltical communication Conclusion
. . . the topic
Methods to study communication
qualitative
• discourse analysis
• interviews
• focus groups
quantitative
• survey
• experiment
• content analysis
• network analysis
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
16. Introducing. . . Studying social media in poltical communication Conclusion
. . . the topic
Methods to study communication
quantitative
• survey
• experiment
• content analysis
• network analysis
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
17. Introducing. . . Studying social media in poltical communication Conclusion
. . . the topic
The link with data science
• “Computational social science”
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., . . . van Alstyne, M. (2009).
Computational social science. Science, 323, 721–723. doi:10.1126/science.1167742
Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 1–12.
doi:10.1177/2053951714528481
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
18. Introducing. . . Studying social media in poltical communication Conclusion
. . . the topic
The link with data science
• “Computational social science”
• “In short, a computational social science is emerging that
leverages the capacity to collect and analyze data with an
unprecedented breadth and depth and scale.’ (Lazer et al.)
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., . . . van Alstyne, M. (2009).
Computational social science. Science, 323, 721–723. doi:10.1126/science.1167742
Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 1–12.
doi:10.1177/2053951714528481
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
19. Introducing. . . Studying social media in poltical communication Conclusion
. . . the topic
The link with data science
• “Computational social science”
• “In short, a computational social science is emerging that
leverages the capacity to collect and analyze data with an
unprecedented breadth and depth and scale.’ (Lazer et al.)
• “the computational social sciences employ the scientific
method, complementing descriptive statistics with inferential
statistics that seek to identify associations and causality. In
other words, they are underpinned by an epistemology wherein
the aim is to produce sophisticated statistical models that
explain, simulate and predict human life.” (Kitchin)
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., . . . van Alstyne, M. (2009).
Computational social science. Science, 323, 721–723. doi:10.1126/science.1167742
Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 1–12.
doi:10.1177/2053951714528481
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
20. Studying social media in poltical communication
Selective exposure and filter bubbles
21.
22. Introducing. . . Studying social media in poltical communication Conclusion
Selective exposure and filter bubbles
Avoiding dissonant information is human.
Festinger, 1956
• People tend to avoid cognitive dissonance
• One effective way: avoiding information that conflicts
pre-existing beliefs
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
23. Introducing. . . Studying social media in poltical communication Conclusion
Selective exposure and filter bubbles
And it does happen in political communication.
Lazarsfeld, Berelson, & Gaudet, 1944
• Republicans are mainly exposed to the Republican campaign
• Democrats are mainly exposed to the Democratic campaign
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
26. Introducing. . . Studying social media in poltical communication Conclusion
Fragmentation
Fragmentation
Sunstein, 2001 (and many others)
• People will only use those news media that cater to their
interest
• “echo chambers”
• Loss of a common core of issues
• Loss of democratic discourse
⇒ news avoidance, entertainment preference as predictor of news
use in the new media ecosystem (Prior, 2015)
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
27. Introducing. . . Studying social media in poltical communication Conclusion
Polarization
Polarization
Selective exposure to ideologically congruent content
• If people don’t hear the other side any more, they become
more extreme
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
28. Introducing. . . Studying social media in poltical communication Conclusion
Polarization
US Presidential Elections: Vote share 1952, 1956
http://xenocrypt.blogspot.de/2013/02/presidential-results-by-1952-districts.html
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
29. Introducing. . . Studying social media in poltical communication Conclusion
Polarization
US Presidential Elections: Vote share 2008, 2012
http://xenocrypt.blogspot.de/2013/02/presidential-results-by-1952-districts.html
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
30. Introducing. . . Studying social media in poltical communication Conclusion
Polarization
Let’s conclude. . .
• People are selective
• Nowadays, there is more media content to choose from
• content one politically agrees with ⇒ polarization
• entertainment over politics, only exposure to topics one is
interested in beforehand ⇒ fragmentation
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
31. Introducing. . . Studying social media in poltical communication Conclusion
Polarization
The filter bubble
Pariser, 2011
• Algorithms increasingly guess what we might like and choose
for us (FB, Google,. . . )
• Even if we do not avoid actively, we are living in a filter bubble
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
32. Conover, M. D., Gonçalves, B., Flammini, A., & Menczer, F. (2012). Partisan asymmetries in online political
activity. EPJ Data Science, 1(6), 1–19.
33. Introducing. . . Studying social media in poltical communication Conclusion
Polarization
How much of an echo chamber are social media?
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
34. Introducing. . . Studying social media in poltical communication Conclusion
Polarization
How much of an echo chamber are social media?
“We estimated ideological preferences of 3.8 million Twitter users
and, using a data set of nearly 150 million tweets concerning 12
political and nonpolitical issues. [...] Overall, we conclude that
previous work may have overestimated the degree of ideological
segregation in social-media usage”
Barbera, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting from left to right: Is online
political communication more than an echo chamber? Psychological Science, 26(10), 1531–1542.
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
35. Introducing. . . Studying social media in poltical communication Conclusion
Polarization
How much of an echo chamber are social media?
“We estimated ideological preferences of 3.8 million Twitter users
and, using a data set of nearly 150 million tweets concerning 12
political and nonpolitical issues. [...] Overall, we conclude that
previous work may have overestimated the degree of ideological
segregation in social-media usage”
Barbera, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting from left to right: Is online
political communication more than an echo chamber? Psychological Science, 26(10), 1531–1542.
“We find that users share news in similar ways regardless of outlet
or perceived ideology of outlet, and that as a user shares more
news content, they tend to quickly include outlets with opposing
viewpoints. [...] Specifically, users in our sample who sent multiple
tweets tended to increase the ideological diversity in news they
shared within two or three tweets”
Morgan, J. S., Shafiq, M. Z., & Lampe, C. (2013). Is news sharing on Twitter ideologically biased? Proceedings of
the 2013 conference on Computer supported cooperative work (pp. 887–897). ACM.
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
36. Introducing. . . Studying social media in poltical communication Conclusion
Polarization
A first answer to the question why we should study social
media:
They might change the way people
are exposed to news and political
messages – which could lead to
fragmentation and polarization.
But we don’t have conclusive answers yet. . .
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
38. Introducing. . . Studying social media in poltical communication Conclusion
Politicians on social media
The politician–citizen edge is now finally a viable way
. . . no need to take the detour through mass media any more.
journalists citizens
political actors
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
39. Introducing. . . Studying social media in poltical communication Conclusion
Politicians on social media
Consequences
• politicians use social media to be more in control, bypassing
the journalistic filter
• reach other target groups
• but also: from one-way to two-way communication
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
40. Introducing. . . Studying social media in poltical communication Conclusion
Politicians on social media
How politicians (should) communicate only
• interactivity
• personalization
can enhance political involvement
Kruikemeier, S., van Noort, G., Vliegenthart, R., & de Vreese, C. H. (2013). Getting closer: The effects of
personalized and interactive online political communication. European Journal of Communication, 28(1), 53–66.
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
41. Introducing. . . Studying social media in poltical communication Conclusion
Politicians on social media
Effects of politicians on social media
• Using social media impacts voting, especially
“voorkeurstemmen”
Jacobs, K., & Spierings, N. (2014). . . . Maar win je er stemmen mee ? De impact van Twittergebruik door politici
bij de Nederlandse Tweede Kamerverkiezingen. Tijdschrift Voor Communicatiewetenschap, 42(1), 22–38.
Kruikemeier, S. (2014). How political candidates use Twitter and the impact on votes.Computers in Human
Behavior, 34, 131–139.
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
44. Introducing. . . Studying social media in poltical communication Conclusion
Social media and public opinion
Things to keep in mind
• Be careful in generalizing!
• Often used because of easy to access API, but is it really the
right data source for your question? . . .
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
45. Introducing. . . Studying social media in poltical communication Conclusion
Social media and public opinion
Example: Twitter-based prediction of election results
Fundamental flaws
• heavily skewed user base
• better than random does not mean better then more sensible
baseline (last election results, . . . )
• published after results were known
• arbitrary choices on what to include
• overly simplistic assumptions (e.g., number of mentions =
support)
Gayo-Avello, D. (2013). A Meta-Analysis of State-of-the-Art Electoral Prediction From Twitter Data. Social
Science Computer Review, 31(6), 649–679.
Jungherr, A., Jürgens, P., & Schoen, H. (2011). Why the Pirate Party Won the German Election of 2009 or The
Trouble With Predictions: A Response to Tumasjan, A., Sprenger, T. O., Sander, P. G., & Welpe, I. M.
“Predicting Elections With Twitter: What 140 Characters Reveal About Political Sentiment.” Social Science
Computer Review, 30(2), 229–234.
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
47. Introducing. . . Studying social media in poltical communication Conclusion
Conclusion (1)
• The changing media environent can have impact on society at
large (fragmentation, polarization)
• It changes the communication triangle between politicians,
journalists, and the public
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
48. Introducing. . . Studying social media in poltical communication Conclusion
Conclusion (2)
• Social media data can be linked to political outcomes
• But be careful to generalize!
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
49. Introducing. . . Studying social media in poltical communication Conclusion
Questions?
d.c.trilling@uva.nl
@damian0604
www.damiantrilling.net
Fundamentals of Data Science: Case “Political Communication” Damian Trilling