Bjorn Burscher
Biographic Data
| ID | 1534489 |
|---|---|
| NAME | Bjorn Burscher |
| GIVEN NAMES | Bjorn |
| FAMILY NAME | Burscher |
| SIGNATURE | BURSCHER B |
| AFFILIATIONS | University of Amsterdam |
| VERIFIED | No |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 103 |
| AUTHOR COUNT | 5 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2017 |
| H-INDEX | 4 |
From Newsworthiness to Shareworthiness: How to Predict News Sharing Based on Article Characteristics
People increasingly visit online news sites not directly, but by following links on social network sites. Drawing on news value theory and integrating theories about online identities and self-representation, we develop a concept of shareworthiness, with which we seek to understand how the number of shares an article receives on such sites can be predicted. Findings suggest that traditional criteria of newsworthiness indeed play a role in predict…
Frames Beyond Words: Applying Cluster and Sentiment Analysis to News Coverage of the Nuclear Power Issue
Methods to automatically analyze media content are advancing significantly. Among others, it has become increasingly popular to analyze the framing of news articles by means of statistical procedures. In this article, we investigate the conceptual validity of news frames that are inferred by a combination of k-means cluster analysis and automatic sentiment analysis. Furthermore, we test a way of improving statistical frame analysis such that reve…
Using Supervised Machine Learning to Code Policy Issues: Can Classifiers Generalize across Contexts
Content analysis of political communication usually covers large amounts of material and makes the study of dynamics in issue salience a costly enterprise. In this article, we present a supervised machine learning approach for the automatic coding of policy issues, which we apply to news articles and parliamentary questions. Comparing computer-based annotations with human annotations shows that our method approaches the performance of human coder…
Owning the issues of crime and immigration: The relation between immigration and crime news and anti-immigrant voting in 11 countries
Teaching the Computer to Code Frames in News: Comparing Two Supervised Machine Learning Approaches to Frame Analysis
We explore the application of supervised machine learning (SML) to frame coding. By automating the coding of frames in news, SML facilitates the incorporation of large-scale content analysis into framing research, even if financial resources are scarce. This furthers a more integrated investigation of framing processes conceptually as well as methodologically. We conduct several experiments in which we automate the coding of four generic frames t…
Owning the issues of crime and immigration: The relation between immigration and crime news and anti-immigrant voting in 11 countries
Frames Beyond Words: Applying Cluster and Sentiment Analysis to News Coverage of the Nuclear Power Issue
Methods to automatically analyze media content are advancing significantly. Among others, it has become increasingly popular to analyze the framing of news articles by means of statistical procedures. In this article, we investigate the conceptual validity of news frames that are inferred by a combination of k-means cluster analysis and automatic sentiment analysis. Furthermore, we test a way of improving statistical frame analysis such that reve…
Using Supervised Machine Learning to Code Policy Issues: Can Classifiers Generalize across Contexts
Content analysis of political communication usually covers large amounts of material and makes the study of dynamics in issue salience a costly enterprise. In this article, we present a supervised machine learning approach for the automatic coding of policy issues, which we apply to news articles and parliamentary questions. Comparing computer-based annotations with human annotations shows that our method approaches the performance of human coder…
Teaching the Computer to Code Frames in News: Comparing Two Supervised Machine Learning Approaches to Frame Analysis
We explore the application of supervised machine learning (SML) to frame coding. By automating the coding of frames in news, SML facilitates the incorporation of large-scale content analysis into framing research, even if financial resources are scarce. This furthers a more integrated investigation of framing processes conceptually as well as methodologically. We conduct several experiments in which we automate the coding of four generic frames t…
Teaching the Computer to Code Frames in News: Comparing Two Supervised Machine Learning Approaches to Frame Analysis
We explore the application of supervised machine learning (SML) to frame coding. By automating the coding of frames in news, SML facilitates the incorporation of large-scale content analysis into framing research, even if financial resources are scarce. This furthers a more integrated investigation of framing processes conceptually as well as methodologically. We conduct several experiments in which we automate the coding of four generic frames t…
Frames Beyond Words: Applying Cluster and Sentiment Analysis to News Coverage of the Nuclear Power Issue
Methods to automatically analyze media content are advancing significantly. Among others, it has become increasingly popular to analyze the framing of news articles by means of statistical procedures. In this article, we investigate the conceptual validity of news frames that are inferred by a combination of k-means cluster analysis and automatic sentiment analysis. Furthermore, we test a way of improving statistical frame analysis such that reve…
Using Supervised Machine Learning to Code Policy Issues: Can Classifiers Generalize across Contexts
Content analysis of political communication usually covers large amounts of material and makes the study of dynamics in issue salience a costly enterprise. In this article, we present a supervised machine learning approach for the automatic coding of policy issues, which we apply to news articles and parliamentary questions. Comparing computer-based annotations with human annotations shows that our method approaches the performance of human coder…
Owning the issues of crime and immigration: The relation between immigration and crime news and anti-immigrant voting in 11 countries
From Newsworthiness to Shareworthiness: How to Predict News Sharing Based on Article Characteristics
People increasingly visit online news sites not directly, but by following links on social network sites. Drawing on news value theory and integrating theories about online identities and self-representation, we develop a concept of shareworthiness, with which we seek to understand how the number of shares an article receives on such sites can be predicted. Findings suggest that traditional criteria of newsworthiness indeed play a role in predict…
Computer Science (4 works) · Sociology (4 works) · Artificial Intelligence (3 works) · Computational and Text Analysis Methods (3 works) · Content analysis (2 works) · Data science (2 works) · Electoral Systems and Political Participation (2 works) · Frame analysis (2 works) · Law (2 works) · Machine learning (2 works)