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Get the most recent info and news about Polar on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

Polar Electro GmbH Deutschland High Tech Equipment manufactures health equipment. The Company offers heart rate monitors used for rehabilitation or weight management when exercising. Polar Electro serves customers throughout Germany.

Polar

This is Part 5 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. Table of Links Abstract and Introduction Human-machine interactions Collective outcomes Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Discussion Implications for research Implications for design Implications for policy Conclusion, Acknowledgments, References, and Competing interests Box 2: Contagion on Twitter Social bots on the micro-blogging platform Twitter (re-branded as X in 2022) are covert automated accounts designed to impersonate humans to boost followers, disseminate information, and promote products. Bots and bot detection methods have co-evolved, resulting in increasingly more sophisticated imitation or detection strategies [177,178,179,180,39,181,182], but detection is inherently limited due to the overlap between covert autonomous bots, managed user accounts, hacked accounts, cyborgs, sock-puppets, and coordinated botnets [24,183,22,184]. Estimates suggest that 9-15% of Twitter users are bots185,182, with bot activity typically increasing around controversial political events [186]. Twitter social bots, who do not follow social instincts but neither succumb to fatigue, engage less in social interactions via replies and mentions than humans but produce more content [187]. The bots mainly retweet – a passive strategy to indicate support and gain followers – but are less successful in attracting friends and followers than humans [182]. Overall, they are less connected and bot-bot (2%), bot-human (19%), and human-bot (3%) interactions are considerably less common than human-human interactions (76%) 1[86]. Despite their rudimentary social behavior and weak network integration, Twitter bots significantly influence political communication, public opinion, elections, and markets. They play an important role in misinformation dissemination in relation to political events [188,189,190,191,192,193,194], COVID-19[195,196], and stock market investment [197]. Bots can affect human interaction networks by encouraging followings and conversations [198] and amplify low-credibility content early on by targeting influential humans through replies and mentions [192]. Bots’ large numbers enhance their visibility and influence to trigger deep information cascades [199]. Bots equally link to true and false news from low-credibility sites, but people prefer false content, making humans ultimately responsible for the spread of false news [200]. Twitter bots significantly contribute to negative sentiment and conflict escalation. Acting from the periphery, they target central human users to exert indirect influence. They amplify existing negative sentiment and selectively promote inflammatory content, often targeting only one of the factions[186]. Their success stems from exploiting human tendencies to connect with similar others and engage with messages that reinforce their beliefs [201]. Consequently, bots increase ideological polarization and negatively affect democratic discourse on social media, as seen in the 2016 US presidential election[202], the 2016 UK Brexit Referendum[201], and the 2017 Catalan independence referendum[186]. In sum, Twitter’s covert social bots are considered harmful, prompting the platform to cull them[203,204]. Their strength lies in indirect action: they skew the platform’s recommendation system to bias content popularity [137] and exploit human behavioral weaknesses like attention seeking, confirmation bias, moral outrage, and ideological homophily. Authors:
(1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom;
(2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland;
(3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA;
(4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This paper is available on arxiv under CC BY 4.0 DEED license. This is Part 5 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. This is Part 5 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. “ Human-Machine Social Systems .” Human-Machine Social Systems Table of Links Abstract and Introduction Abstract and Introduction Human-machine interactions Human-machine interactions Collective outcomes Collective outcomes Box 1: Competition in high-frequency trading markets Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Box 4: Cooperation and contagion on Reddit Discussion Discussion Implications for research Implications for research Implications for design Implications for design Implications for policy Implications for policy Conclusion, Acknowledgments, References, and Competing interests Conclusion, Acknowledgments, References, and Competing interests Box 2: Contagion on Twitter Social bots on the micro-blogging platform Twitter (re-branded as X in 2022) are covert automated accounts designed to impersonate humans to boost followers, disseminate information, and promote products. Bots and bot detection methods have co-evolved, resulting in increasingly more sophisticated imitation or detection strategies [177,178,179,180,39,181,182], but detection is inherently limited due to the overlap between covert autonomous bots, managed user accounts, hacked accounts, cyborgs, sock-puppets, and coordinated botnets [24,183,22,184]. Estimates suggest that 9-15% of Twitter users are bots185,182, with bot activity typically increasing around controversial political events [186]. Twitter social bots, who do not follow social instincts but neither succumb to fatigue, engage less in social interactions via replies and mentions than humans but produce more content [187]. The bots mainly retweet – a passive strategy to indicate support and gain followers – but are less successful in attracting friends and followers than humans [182]. Overall, they are less connected and bot-bot (2%), bot-human (19%), and human-bot (3%) interactions are considerably less common than human-human interactions (76%) 1[86]. Despite their rudimentary social behavior and weak network integration, Twitter bots significantly influence political communication, public opinion, elections, and markets. They play an important role in misinformation dissemination in relation to political events [188,189,190,191,192,193,194], COVID-19[195,196], and stock market investment [197]. Bots can affect human interaction networks by encouraging followings and conversations [198] and amplify low-credibility content early on by targeting influential humans through replies and mentions [192]. Bots’ large numbers enhance their visibility and influence to trigger deep information cascades [199]. Bots equally link to true and false news from low-credibility sites, but people prefer false content, making humans ultimately responsible for the spread of false news [200]. Twitter bots significantly contribute to negative sentiment and conflict escalation. Acting from the periphery, they target central human users to exert indirect influence. They amplify existing negative sentiment and selectively promote inflammatory content, often targeting only one of the factions[186]. Their success stems from exploiting human tendencies to connect with similar others and engage with messages that reinforce their beliefs [201]. Consequently, bots increase ideological polarization and negatively affect democratic discourse on social media, as seen in the 2016 US presidential election[202], the 2016 UK Brexit Referendum[201], and the 2017 Catalan independence referendum[186]. In sum, Twitter’s covert social bots are considered harmful, prompting the platform to cull them[203,204]. Their strength lies in indirect action: they skew the platform’s recommendation system to bias content popularity [137] and exploit human behavioral weaknesses like attention seeking, confirmation bias, moral outrage, and ideological homophily. Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. Authors: Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Twitter Bots Spread Misinformation, Shape Opinions, & Amplify Polarization by Exploiting Human Bias

Exploring Data Operations with PySpark, Pandas, DuckDB, Polars, and DataFusion in a Python Notebook

Introduction The Data space is almost as volatile as the AI space this year, with many players consolidating. In the summer of 2022, I wrote about The Data Table Format Wars, and the ecosystem has only gotten more interesting since then. I won’t get deep into the weeds on Data Lakes vs. Data Warehouse, etc., but suffice it to say, we are seeing everything coalesce around decoupling data storage from computing. Table formats are a way to standardize lightly opinionated storage. As I opined a couple of years ago, Apache Iceberg seems to be where they are settling. These formats tend to use Apache Parquet on object storage and provide a method for tracking new data and data state changes. In 2023, Databricks announced the open-sourcing of their Unity Catalog, and Onehouse announced OneTable (now X-Table). These projects are meant to give you a single interface to the three popular table formats: Delta, Hudi, and Iceberg. Then, June 2024 happened… What Happened? Snowflake was having its annual Snowflake Summit conference, and on June 3, 2024, they announced Apache Polaris (incubating), a vendor-neutral, open catalog for Apache Iceberg. The next day, and a week before the Databricks conference, Databricks announced their acquisition of Tabular, a company founded by the creators of Iceberg. This still seems weird to everyone from the outside because Iceberg is a spec; it isn’t a product, and Tabular isn’t even the main contributor to Iceberg. In my opinion, Databricks will somehow roll their Deltalake into Iceberg. Behind the scenes, Snowflake had been trying to acquire Tabular. Something happened at the 11th hour, and Databricks came in and laid down a ton more money, and the Snowflake bid was discarded. Snowflake was already two years into their commitment to supporting Iceberg as external storage, which made a lot of sense for them regardless. Snowflake has a reputation concerning the release speed after an announcement, so there was some skepticism, and some even wondered if any code had been written. It turns out Snowflake had been working on this for a while, and it was made available in very short order, with vendors like Dremio, Starburst, CelerData, and Upsolver showing solutions using it in less than two months: a delightful surprise, and a positive sign of things to come. All of that is a long way to set up Apache Polaris, and hopefully, it will give you a sense of the importance that a good catalog plays in all of this. How will any of your compute tools work if you don’t have a unified system to describe all those parquet files in your data lake? Polaris Overview Apache Polaris is an open-source catalog implementation for Apache Iceberg tables. Its main features include: REST API implementation: Polaris implements Iceberg's REST API, enabling interoperability across multiple query engines.
Multi-engine compatibility: It allows various query engines, such as Apache Spark, Apache Flink, Trino, and others, to read and write Iceberg tables through a single catalog.
Centralized management: Polaris provides centralized and secure access to Iceberg tables across REST-compatible query engines.
Namespace and metadata management: It supports creating namespaces for logical grouping and manages metadata for Iceberg tables.
Storage configurations: Polaris offers robust storage configurations for cloud providers like S3, Azure, and Google Cloud Storage.
Security features: It implements role-based access control (RBAC) and uses credential vending to secure query execution.
Open-source nature: Polaris is fully open-source, allowing for community contributions and vendor neutrality.
Interoperability: It enables multiple engines to be used on a single copy of data, reducing storage duplication and ETL costs.
Consistent access control: Polaris allows you to manage role-based access control and storage layer security consistently across engines from one place.
Flexibility in hosting: It can be hosted on various infrastructures, providing flexibility and reducing vendor lock-in. And a lovely little graphic from the docs to put a picture on everything. Borrowing from the Polaris docs for a picture once again, let’s dive into what is happening here in this entity hierarchy graphic: We can have multiple catalogs in a single Polaris install; in this diagram, it is only Catalog1 that has any namespaces or tables defined. A single catalog can be either internal or external. The internal tables allow for read/write functionality, meaning you have full control. The external tables are currently read-only and are intended to mirror data that is in some external catalog, such as a Glue catalog, which Polaris will see as the source of truth once connected. The catalog organizes your iceberg tables; you’ll want to configure it with your storage configuration for the applicable cloud system that you are on. This forms the first architectural layer in the Iceberg table spec. A namespace can have an arbitrary level of nesting and is used to logically group Iceberg tables within a catalog. An Iceberg table will always belong to a namespace. With this configuration in place, you can then use the Polaris catalog to describe your Iceberg tables in such a way as to make them easily accessible and manageable using a large variety of tools. That’s a quick overview of what is happening and what it looks like. The implementation inside of Snowflake provides a really handy UI to manage the implementation; other vendors have their own, and you can just do it through Spark if you like. Summary I continue to believe that Apache Iceberg is going to “win” the table format wars. Apache Polaris focusing on Iceberg is not a bad thing. I want to dig into how RBAC has been implemented in Polaris another time and see if it is as robust as what the Tabular product implemented, which I was a big fan of. I believe Polaris is a big win for the Iceberg community and will help accelerate adoption even further. I’ve seen talk about the Dremio Nessie project getting rolled in, and I’m curious to see what that will look like. I haven’t looked at Nessie in a couple of years, and I thought their git-like semantics mainly had been covered by the Iceberg tagging and branching, which I wrote about last year—yet another story for another day. I’m a big fan of what is happening here. If you are in this space, this is the right train to jump on. Check out my other What the Heck is… articles at the links below: What The Heck Is DuckDB?
What the Heck Is Malloy?
What the Heck is PRQL?
What the Heck is GlareDB?
What the Heck is SeaTunnel?
What the Heck is LanceDB?
What the heck is SDF?
What the Heck is Paimon?
What the Heck is Proton?
What the Heck is PuppyGraph?
What the Heck is GPTScript?
What the Heck is WarpStream? Introduction Introduction The Data space is almost as volatile as the AI space this year, with many players consolidating. In the summer of 2022, I wrote about The Data Table Format Wars , and the ecosystem has only gotten more interesting since then. I won’t get deep into the weeds on Data Lakes vs. Data Warehouse, etc., but suffice it to say, we are seeing everything coalesce around decoupling data storage from computing. The Data Table Format Wars Table formats are a way to standardize lightly opinionated storage. As I opined a couple of years ago, Apache Iceberg seems to be where they are settling. These formats tend to use Apache Parquet on object storage and provide a method for tracking new data and data state changes. In 2023, Databricks announced the open-sourcing of their Unity Catalog, and Onehouse announced OneTable (now X-Table). These projects are meant to give you a single interface to the three popular table formats: Delta, Hudi, and Iceberg. Then, June 2024 happened… announced announced What Happened? Snowflake was having its annual Snowflake Summit conference, and on June 3, 2024, they announced Apache Polaris (incubating), a vendor-neutral, open catalog for Apache Iceberg. The next day, and a week before the Databricks conference, Databricks announced their acquisition of Tabular, a company founded by the creators of Iceberg. This still seems weird to everyone from the outside because Iceberg is a spec; it isn’t a product, and Tabular isn’t even the main contributor to Iceberg. In my opinion, Databricks will somehow roll their Deltalake into Iceberg. announced announced Behind the scenes, Snowflake had been trying to acquire Tabular. Something happened at the 11th hour, and Databricks came in and laid down a ton more money, and the Snowflake bid was discarded. Snowflake was already two years into their commitment to supporting Iceberg as external storage, which made a lot of sense for them regardless. Snowflake has a reputation concerning the release speed after an announcement, so there was some skepticism, and some even wondered if any code had been written. It turns out Snowflake had been working on this for a while, and it was made available in very short order, with vendors like Dremio, Starburst, CelerData, and Upsolver showing solutions using it in less than two months: a delightful surprise, and a positive sign of things to come. All of that is a long way to set up Apache Polaris , and hopefully, it will give you a sense of the importance that a good catalog plays in all of this. How will any of your compute tools work if you don’t have a unified system to describe all those parquet files in your data lake? Apache Polaris Polaris Overview Apache Polaris is an open-source catalog implementation for Apache Iceberg tables. Its main features include: REST API implementation: Polaris implements Iceberg's REST API, enabling interoperability across multiple query engines. Multi-engine compatibility: It allows various query engines, such as Apache Spark, Apache Flink, Trino, and others, to read and write Iceberg tables through a single catalog. Centralized management: Polaris provides centralized and secure access to Iceberg tables across REST-compatible query engines. Namespace and metadata management: It supports creating namespaces for logical grouping and manages metadata for Iceberg tables. Storage configurations: Polaris offers robust storage configurations for cloud providers like S3, Azure, and Google Cloud Storage. Security features: It implements role-based access control (RBAC) and uses credential vending to secure query execution. Open-source nature: Polaris is fully open-source, allowing for community contributions and vendor neutrality. Interoperability: It enables multiple engines to be used on a single copy of data, reducing storage duplication and ETL costs. Consistent access control: Polaris allows you to manage role-based access control and storage layer security consistently across engines from one place. Flexibility in hosting: It can be hosted on various infrastructures, providing flexibility and reducing vendor lock-in. REST API implementation: Polaris implements Iceberg's REST API, enabling interoperability across multiple query engines. Multi-engine compatibility: It allows various query engines, such as Apache Spark, Apache Flink, Trino, and others, to read and write Iceberg tables through a single catalog. Centralized management: Polaris provides centralized and secure access to Iceberg tables across REST-compatible query engines. Namespace and metadata management: It supports creating namespaces for logical grouping and manages metadata for Iceberg tables. Storage configurations: Polaris offers robust storage configurations for cloud providers like S3, Azure, and Google Cloud Storage. Security features: It implements role-based access control (RBAC) and uses credential vending to secure query execution. Open-source nature: Polaris is fully open-source, allowing for community contributions and vendor neutrality. Interoperability: It enables multiple engines to be used on a single copy of data, reducing storage duplication and ETL costs. Consistent access control: Polaris allows you to manage role-based access control and storage layer security consistently across engines from one place. Flexibility in hosting: It can be hosted on various infrastructures, providing flexibility and reducing vendor lock-in. And a lovely little graphic from the docs to put a picture on everything. Borrowing from the Polaris docs for a picture once again, let’s dive into what is happening here in this entity hierarchy graphic: We can have multiple catalogs in a single Polaris install; in this diagram, it is only Catalog1 that has any namespaces or tables defined. A single catalog can be either internal or external. The internal tables allow for read/write functionality, meaning you have full control. The external tables are currently read-only and are intended to mirror data that is in some external catalog, such as a Glue catalog, which Polaris will see as the source of truth once connected. The catalog organizes your iceberg tables; you’ll want to configure it with your storage configuration for the applicable cloud system that you are on. This forms the first architectural layer in the Iceberg table spec. A namespace can have an arbitrary level of nesting and is used to logically group Iceberg tables within a catalog. An Iceberg table will always belong to a namespace. With this configuration in place, you can then use the Polaris catalog to describe your Iceberg tables in such a way as to make them easily accessible and manageable using a large variety of tools. That’s a quick overview of what is happening and what it looks like. The implementation inside of Snowflake provides a really handy UI to manage the implementation; other vendors have their own, and you can just do it through Spark if you like. Summary I continue to believe that Apache Iceberg is going to “win” the table format wars. Apache Polaris focusing on Iceberg is not a bad thing. I want to dig into how RBAC has been implemented in Polaris another time and see if it is as robust as what the Tabular product implemented, which I was a big fan of. I believe Polaris is a big win for the Iceberg community and will help accelerate adoption even further. I’ve seen talk about the Dremio Nessie project getting rolled in, and I’m curious to see what that will look like. I haven’t looked at Nessie in a couple of years, and I thought their git-like semantics mainly had been covered by the Iceberg tagging and branching, which I wrote about last year—yet another story for another day. wrote about I’m a big fan of what is happening here. If you are in this space, this is the right train to jump on. Check out my other What the Heck is… articles at the links below: What The Heck Is DuckDB? What the Heck Is Malloy? What the Heck is PRQL? What the Heck is GlareDB? What the Heck is SeaTunnel? What the Heck is LanceDB? What the heck is SDF? What the Heck is Paimon? What the Heck is Proton? What the Heck is PuppyGraph? What the Heck is GPTScript? What the Heck is WarpStream? What The Heck Is DuckDB? What The Heck Is DuckDB? What the Heck Is Malloy? What the Heck Is Malloy? What the Heck is PRQL? What the Heck is PRQL? What the Heck is GlareDB? What the Heck is GlareDB? What the Heck is SeaTunnel? What the Heck is SeaTunnel? What the Heck is LanceDB? What the Heck is LanceDB? What the heck is SDF? What the heck is SDF? What the Heck is Paimon? What the Heck is Paimon? What the Heck is Proton? What the Heck is Proton? What the Heck is PuppyGraph? What the Heck is PuppyGraph? What the Heck is GPTScript? What the Heck is GPTScript? What the Heck is WarpStream? What the Heck is WarpStream?

What The Heck is Apache Polaris?

Authors:
(1) Mehdi Naderi;
(2) Markos Papageorgiou;
(3) Dimitrios Troullinos;
(4) Iasson Karafyllis;
(5) Ioannis Papamichail. Table of Links Abstract and Introduction Vehicle Modeling The Nonlinear Feedback Control OD Corridors and Desired Orientations Boundary and Safety Controllers Simulation Results Conclusion Appendix A: Collision Detection Appendix B: Transformed ISO-Distance curves Appendix C: Local Density Appendix D: Safety Controller Details Appendix E: Controller Parameters References APPENDIX B: TRANSFORMED ISO-DISTANCE CURVES For the iso-distance curve transformation to align with the desired orientation, new polar coordinates are defined whose origin is found by crossing two lines perpendicular to the vehicles’ rear axles, while having the ego vehicle’s desired deviation from the circular angle. This implies that the coordinates are different for each pair of vehicles. The lines’ equations, according to Fig. 20, can be written as Note that each vehicle projects other vehicles on its own coordinates; therefore, in both equations of (42), the desired orientation of vehicle i is taken into account. Intersecting above equations gives the centre of new coordinates as Then, the radius and angle of each vehicle in the new coordinates is calculated by Furthermore, the distance in the new coordinates is This paper is available on arxiv under CC 4.0 license. Authors: (1) Mehdi Naderi; (2) Markos Papageorgiou; (3) Dimitrios Troullinos; (4) Iasson Karafyllis; (5) Ioannis Papamichail. Authors: Authors: (1) Mehdi Naderi; (2) Markos Papageorgiou; (3) Dimitrios Troullinos; (4) Iasson Karafyllis; (5) Ioannis Papamichail. Table of Links Abstract and Introduction Abstract and Introduction Vehicle Modeling Vehicle Modeling The Nonlinear Feedback Control The Nonlinear Feedback Control OD Corridors and Desired Orientations OD Corridors and Desired Orientations Boundary and Safety Controllers Boundary and Safety Controllers Simulation Results Simulation Results Conclusion Conclusion Appendix A: Collision Detection Appendix A: Collision Detection Appendix B: Transformed ISO-Distance curves Appendix B: Transformed ISO-Distance curves Appendix C: Local Density Appendix C: Local Density Appendix D: Safety Controller Details Appendix D: Safety Controller Details Appendix E: Controller Parameters Appendix E: Controller Parameters References References APPENDIX B: TRANSFORMED ISO-DISTANCE CURVES For the iso-distance curve transformation to align with the desired orientation, new polar coordinates are defined whose origin is found by crossing two lines perpendicular to the vehicles’ rear axles, while having the ego vehicle’s desired deviation from the circular angle. This implies that the coordinates are different for each pair of vehicles. The lines’ equations, according to Fig. 20, can be written as Note that each vehicle projects other vehicles on its own coordinates; therefore, in both equations of (42), the desired orientation of vehicle i is taken into account. Intersecting above i equations gives the centre of new coordinates as Then, the radius and angle of each vehicle in the new coordinates is calculated by Furthermore, the distance in the new coordinates is This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Polar Coordinates for Accurate Iso-Distance Curve Alignment in Vehicle Simulations

Authors:
(1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu);
(2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu);
(3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ). Table of Links Abstract and Introduction Related Work Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Discussion and Ethics Statement Appendix and References Appendix References Allen, J.; Howland, B.; Mobius, M.; Rothschild, D.; and Watts, D. J. 2020. Evaluating the fake news problem at the scale of the information ecosystem. Science Advances, 6(14): eaay3539. Barnard, S. R. 2018. Citizens at the Gates. Twitter, Networked Publics and The Transformation of American Journalism. Cham, Sveitsi: Palgrave Macmillan. Cheung, Y.-W.; and Lai, K. S. 1995. Lag Order and Critical Values of the Augmented Dickey–Fuller Test. Journal of Business & Economic Statistics, 13(3): 277–280. Chinn, S.; Hart, P. S.; and Soroka, S. 2020. Politicization and Polarization in Climate Change News Content, 1985-2017. Science Communication, 42(1): 112–129. Dash, S.; Mishra, D.; Shekhawat, G.; and Pal, J. 2022. Divided We Rule: Influencer Polarization on Twitter during Political Crises in India. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 16: 135–146. Demszky, D.; Movshovitz-Attias, D.; Ko, J.; Cowen, A.; Nemade, G.; and Ravi, S. 2020. GoEmotions: A dataset of fine-grained emotions. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 4040–4054. Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, 4171–4186. Dutta, S.; Ma, J.; and Choudhury, M. D. 2018. Measuring the Impact of Anxiety on Online Social Interactions. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 12(1): 584–587. Ebeling, R.; Saenz, C. A. C.; Nobre, J. C.; and Becker, K. 2022. Analysis of the Influence of Political Polarization in the Vaccination Stance: The Brazilian COVID-19 Scenario. Proceedings of the International AAAI Conference on Web and Social Media, 16: 159–170. Entman, R. M. 2003. Cascading activation: Contesting the White House’s frame after 9/11. Political Communication,, 20(4): 415– 432. Eveland, W. P.; Seo, M.; and Marton, K. 2002. Learning From the News in Campaign 2000: An Experimental Comparison of TV News, Newspapers, and Online News. Media Psychology, 4(4): 353–378. Garimella, K.; Smith, T.; Weiss, R.; and West, R. 2021. Political Polarization in Online News Consumption. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 15: 152–162. Gentzkow, M.; Shapiro, J. M.; and Taddy, M. 2019. Measuring Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech. Econometrica, 87(4): 1307–1340. Granger, C. W. J. 1980. Testing for causality: A personal viewpoint. Journal of Economic Dynamics and Control, 2: 329–352. Hamilton, W. L.; Leskovec, J.; and Jurafsky, D. 2016. Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change. Conference on Empirical Methods in Natural Language Processing., 2116–2121. Hoey, M. 2005. Lexical Priming: A new theory of words and language. London: Routledge. Horning, M. 2018. The pundit problem: A look at bias and negativity in cable news coverage as the 2016 election came to a close. The 2016 American Presidential Campaign and the News, 77–99. Jang, S. M.; and Hart, P. S. 2015. Polarized frames on “climate change” and “global warming” across countries and states: Evidence from Twitter big data. Global Environmental Change, 32: 11–17. Kim, E.; Lelkes, Y.; and McCrain, J. 2022. Measuring dynamic media bias. Proceedings of the National Academy of Sciences, 119(32): e2202197119. Kutuzov, A.; Øvrelid, L.; Szymanski, T.; and Velldal, E. 2018. Diachronic word embeddings and semantic shifts: a survey. In Proceedings of the 27th International Conference on Computational Linguistics, 1384–1397. McCombs, M. 1997. Building Consensus: The News Media’s Agenda-Setting Roles. Political Communication, 14(4): 433–443. McLeod, J. M.; Scheufele, D. A.; and Moy, P. 1999. Community, Communication, and Participation: The Role of Mass Media and Interpersonal Discussion in Local Political Participation. Political Communication, 16(3): 315–336. Muise, D.; Hosseinmardi, H.; Howland, B.; Mobius, M.; Rothschild, D.; and Watts, D. J. 2022. Quantifying partisan news diets in Web and TV audiences. Science Advances, 8(28): eabn0083. Papacharissi, Z. 2009. Journalism and citizenship. Routledge London. Polignano, M.; Basile, V.; Basile, P.; Gabrieli, G.; Vassallo, M.; and Bosco, C. 2022. A hybrid lexicon-based and neural approach for explainable polarity detection. Information Processing & Management, 59(5): 103058. Recuero, R.; Soares, F. B.; and Gruzd, A. 2020. Hyperpartisanship, Disinformation and Political Conversations on Twitter: The Brazilian Presidential Election of 2018. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 14: 569–578. Rogstad, I. 2016. Is Twitter just rehashing? Intermedia agenda setting between Twitter and mainstream media. Journal of Information Technology & Politics, 13(2): 142–158. Russell Neuman, W.; Guggenheim, L.; Mo Jang, S.; and Bae, S. Y. 2014. The Dynamics of Public Attention: Agenda-Setting Theory Meets Big Data. Journal of Communication, 64(2): 193–214. Sap, M.; Card, D.; Gabriel, S.; Choi, Y.; and Smith, N. A. 2019. The Risk of Racial Bias in Hate Speech Detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 1668–1678. Scheufele, D. A. 2000. Agenda-setting, priming, and framing revisited: Another look at cognitive effects of political communication. Mass communication & society, 3(2-3): 297–316. Sundararajan, M.; Taly, A.; and Yan, Q. 2017. Axiomatic Attribution for Deep Networks. In Proceedings of the 34th International Conference on Machine Learning, 3319–3328. Tuba, M.; Akashe, S.; and Joshi, A., eds. 2020. Information and Communication Technology for Sustainable Development, volume 933. Advances in Intelligent Systems and Computing. Westfall, J.; Van Boven, L.; Chambers, J. R.; and Judd, C. M. 2015. Perceiving Political Polarization in the United States: Party Identity Strength and Attitude Extremity Exacerbate the Perceived Partisan Divide. Perspectives on Psychological Science, 10(2): 145– 158. Yang, M.; Wen, X.; Lin, Y.-R.; and Deng, L. 2017. Quantifying Content Polarization on Twitter. In 2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC), 299– 308. Zhang, T.; Kishore, V.; Wu, F.; Weinberger, K. Q.; and Artzi, Y. 2020. BERTScore: Evaluating Text Generation with BERT. arXiv:1904.09675. This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu); (2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu); (3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ). Authors: Authors: (1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu); (2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu); (3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ). Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Discussion and Ethics Statement Discussion and Ethics Statement Appendix and References Appendix and References Appendix References Allen, J.; Howland, B.; Mobius, M.; Rothschild, D.; and Watts, D. J. 2020. Evaluating the fake news problem at the scale of the information ecosystem. Science Advances, 6(14): eaay3539. Barnard, S. R. 2018. Citizens at the Gates. Twitter, Networked Publics and The Transformation of American Journalism. Cham, Sveitsi: Palgrave Macmillan. Cheung, Y.-W.; and Lai, K. S. 1995. Lag Order and Critical Values of the Augmented Dickey–Fuller Test. Journal of Business & Economic Statistics, 13(3): 277–280. Chinn, S.; Hart, P. S.; and Soroka, S. 2020. Politicization and Polarization in Climate Change News Content, 1985-2017. Science Communication, 42(1): 112–129. Dash, S.; Mishra, D.; Shekhawat, G.; and Pal, J. 2022. Divided We Rule: Influencer Polarization on Twitter during Political Crises in India. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 16: 135–146. Demszky, D.; Movshovitz-Attias, D.; Ko, J.; Cowen, A.; Nemade, G.; and Ravi, S. 2020. GoEmotions: A dataset of fine-grained emotions. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 4040–4054. Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, 4171–4186. Dutta, S.; Ma, J.; and Choudhury, M. D. 2018. Measuring the Impact of Anxiety on Online Social Interactions. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 12(1): 584–587. Ebeling, R.; Saenz, C. A. C.; Nobre, J. C.; and Becker, K. 2022. Analysis of the Influence of Political Polarization in the Vaccination Stance: The Brazilian COVID-19 Scenario. Proceedings of the International AAAI Conference on Web and Social Media, 16: 159–170. Entman, R. M. 2003. Cascading activation: Contesting the White House’s frame after 9/11. Political Communication,, 20(4): 415– 432. Eveland, W. P.; Seo, M.; and Marton, K. 2002. Learning From the News in Campaign 2000: An Experimental Comparison of TV News, Newspapers, and Online News. Media Psychology, 4(4): 353–378. Garimella, K.; Smith, T.; Weiss, R.; and West, R. 2021. Political Polarization in Online News Consumption. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 15: 152–162. Gentzkow, M.; Shapiro, J. M.; and Taddy, M. 2019. Measuring Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech. Econometrica, 87(4): 1307–1340. Granger, C. W. J. 1980. Testing for causality: A personal viewpoint. Journal of Economic Dynamics and Control, 2: 329–352. Hamilton, W. L.; Leskovec, J.; and Jurafsky, D. 2016. Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change. Conference on Empirical Methods in Natural Language Processing., 2116–2121. Hoey, M. 2005. Lexical Priming: A new theory of words and language. London: Routledge. Horning, M. 2018. The pundit problem: A look at bias and negativity in cable news coverage as the 2016 election came to a close. The 2016 American Presidential Campaign and the News, 77–99. Jang, S. M.; and Hart, P. S. 2015. Polarized frames on “climate change” and “global warming” across countries and states: Evidence from Twitter big data. Global Environmental Change, 32: 11–17. Kim, E.; Lelkes, Y.; and McCrain, J. 2022. Measuring dynamic media bias. Proceedings of the National Academy of Sciences, 119(32): e2202197119. Kutuzov, A.; Øvrelid, L.; Szymanski, T.; and Velldal, E. 2018. Diachronic word embeddings and semantic shifts: a survey. In Proceedings of the 27th International Conference on Computational Linguistics, 1384–1397. McCombs, M. 1997. Building Consensus: The News Media’s Agenda-Setting Roles. Political Communication, 14(4): 433–443. McLeod, J. M.; Scheufele, D. A.; and Moy, P. 1999. Community, Communication, and Participation: The Role of Mass Media and Interpersonal Discussion in Local Political Participation. Political Communication, 16(3): 315–336. Muise, D.; Hosseinmardi, H.; Howland, B.; Mobius, M.; Rothschild, D.; and Watts, D. J. 2022. Quantifying partisan news diets in Web and TV audiences. Science Advances, 8(28): eabn0083. Papacharissi, Z. 2009. Journalism and citizenship. Routledge London. Polignano, M.; Basile, V.; Basile, P.; Gabrieli, G.; Vassallo, M.; and Bosco, C. 2022. A hybrid lexicon-based and neural approach for explainable polarity detection. Information Processing & Management, 59(5): 103058. Recuero, R.; Soares, F. B.; and Gruzd, A. 2020. Hyperpartisanship, Disinformation and Political Conversations on Twitter: The Brazilian Presidential Election of 2018. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 14: 569–578. Rogstad, I. 2016. Is Twitter just rehashing? Intermedia agenda setting between Twitter and mainstream media. Journal of Information Technology & Politics, 13(2): 142–158. Russell Neuman, W.; Guggenheim, L.; Mo Jang, S.; and Bae, S. Y. 2014. The Dynamics of Public Attention: Agenda-Setting Theory Meets Big Data. Journal of Communication, 64(2): 193–214. Sap, M.; Card, D.; Gabriel, S.; Choi, Y.; and Smith, N. A. 2019. The Risk of Racial Bias in Hate Speech Detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 1668–1678. Scheufele, D. A. 2000. Agenda-setting, priming, and framing revisited: Another look at cognitive effects of political communication. Mass communication & society, 3(2-3): 297–316. Sundararajan, M.; Taly, A.; and Yan, Q. 2017. Axiomatic Attribution for Deep Networks. In Proceedings of the 34th International Conference on Machine Learning, 3319–3328. Tuba, M.; Akashe, S.; and Joshi, A., eds. 2020. Information and Communication Technology for Sustainable Development, volume 933. Advances in Intelligent Systems and Computing. Westfall, J.; Van Boven, L.; Chambers, J. R.; and Judd, C. M. 2015. Perceiving Political Polarization in the United States: Party Identity Strength and Attitude Extremity Exacerbate the Perceived Partisan Divide. Perspectives on Psychological Science, 10(2): 145– 158. Yang, M.; Wen, X.; Lin, Y.-R.; and Deng, L. 2017. Quantifying Content Polarization on Twitter. In 2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC), 299– 308. Zhang, T.; Kishore, V.; Wu, F.; Weinberger, K. Q.; and Artzi, Y. 2020. BERTScore: Evaluating Text Generation with BERT. arXiv:1904.09675. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Methodological Considerations in Semantic Polarization Research

How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity on Social Media

Authors:
(1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu);
(2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu);
(3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ). Table of Links Abstract and Introduction Related Work Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Discussion and Ethics Statement Appendix and References Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Method and Analysis Semantic polarity between Fox and CNN is predominantly high in 2020, echoing the series of highly publicized events in the media (e.g., COVID-19, George Floyd, and BLM demonstrations) that reified a widening ideological gap among the American public on a number of issues. Hence, in Study 2, we aim to go beyond providing a mere quantification of polarization by examining how the language surrounding the use of contentious keywords by CNN and Fox contributes to the 2020 peak in semantic polarity. In other words, how does CNN and Fox News contextually differ in their use of topical keywords on their news programs in 2020? To answer this question, we identified attributive words or contextual tokens that are most predictive of whether a speaker turn containing a topical keyword was from CNN or Fox in 2020. This task entailed a two-step process. First, we trained a BERT-based classifier using the 2020 corpus of closed captions from both stations to predict whether a speaker turn was from CNN or Fox. Then, we used a model interpretation technique called Integrated Gradients (Sundararajan, Taly, and Yan 2017) to identify which words (except for the topical keyword) in each speaker turn contributed most to the classification decision. Classifying 2020 Speaker Turns. To identify whether a speaker turn was broadcast from Fox or CNN in 2020, we built six BERT-based classifiers pertaining to each topic. First, we merged the 2020 corpus of speaker turns, each containing one of the nine topical keywords by topic (e.g., speaker turns containing “racism” or “racist” were merged into one topical corpus). For each of the six topical corpora, we trained BERT (BERT-Base-Uncased) by masking 15% of the words in each speaker turn and trained the model to fill in the masked words using a standard cross-entropy loss function. We then fine-tuned the adapted model to predict whether a speaker turn was from CNN or Fox News. For this step, we split the data into 80% training, 10% validation, and 10% test sets. We set batch sizes to 16, training epochs to 6 with 3-fold cross-validation, and used AdamW for optimization with a learning rate of 2e−05. We used PyTorch for all model implementations. Model Interpretation. To identify the contextual language or the attributive words that contributed most to the 2020 semantic polarity between CNN and Fox News, we used Integrated Gradients (IG) to interpret the classification results from our BERT-classifier in the previous step. IG is an attribution method that allows us to understand how much an input feature contributes to the output result of a deep neural network (Sundararajan, Taly, and Yan 2017). This post-hoc interpretability technique combines the input’s gradients by interpolating in short increments along a straight line between a baseline x (usually a vector with all zeros) and the input x 0 . Formally, if F : Rn → [0, 1] represents BERT, then the integrated gradient (IG) of the i th dimension of x is: Large pre-trained transformer-based language models, such as BERT may have strong predictive powers, but their multiple nonlinearities prevent users from directly attributing model outputs to various input features. To overcome this lack of interpretive insight, we leverage IG to identify words that are most attributable to each predicted class. Thus, we identify which contextual words within a speaker turn (input features of a classifier) contribute most to, or are most predictive of the output decision, namely, whether the speaker turn is from CNN or Fox News in 2020. Results Our BERT-classifier distinguishes 2020 speaker turns from CNN and Fox News with a mean accuracy of 75.3% across all six topics (Table 3). Model performance is highest for speaker turns topically focused on the police, corresponding to Study 1 findings demonstrating highest SP scores for “police” across all keywords in 2020. In Table 4, we show the top 10 words most predictive of how CNN and Fox News discuss the six topics on their news programs in 2020. To avoid noise caused by low-frequency words, we limited our token selection to words with a frequency above the 95th percentile. We categorized the tokens with the highest and lowest attribution scores ranging from positive (attributed to CNN) to negative (attributed to Fox News) values for each topic. As shown, across all topics, none of the top tokens overlap between the two stations, indicating the drastically distinct contexts in which the two channels discuss key topics in 2020. For example, words like “illegal”, “enforcement”, and “order” contextualize immigration discussions in a rather legalistic manner on Fox News whereas, “parents”, “family”, “children”, “daughter”, and “communities” - humanizing words connotative of family relations best predict how CNN discusses the same topic. Both stations may be discussing the same topic with identical keywords, but the contextual language surrounding their discussion is strikingly different. Such divergence sheds light on the underlying differences in the linguistic signature of televised media language that help explain the 2020 peak in semantic polarity between the two stations. This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu); (2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu); (3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ). Authors: Authors: (1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu); (2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu); (3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ). Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Discussion and Ethics Statement Discussion and Ethics Statement Appendix and References Appendix and References Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Method and Analysis Semantic polarity between Fox and CNN is predominantly high in 2020, echoing the series of highly publicized events in the media (e.g., COVID-19, George Floyd, and BLM demonstrations) that reified a widening ideological gap among the American public on a number of issues. Hence, in Study 2, we aim to go beyond providing a mere quantification of polarization by examining how the language surrounding the use of contentious keywords by CNN and Fox contributes to the 2020 peak in semantic polarity. In other words, how does CNN and Fox News contextually differ in their use of topical keywords on their news programs in 2020? To answer this question, we identified attributive words or contextual tokens that are most predictive of whether a speaker turn containing a topical keyword was from CNN or Fox in 2020. This task entailed a two-step process. First, we trained a BERT-based classifier using the 2020 corpus of closed captions from both stations to predict whether a speaker turn was from CNN or Fox. Then, we used a model interpretation technique called Integrated Gradients (Sundararajan, Taly, and Yan 2017) to identify which words (except for the topical keyword) in each speaker turn contributed most to the classification decision. Classifying 2020 Speaker Turns. To identify whether a speaker turn was broadcast from Fox or CNN in 2020, we built six BERT-based classifiers pertaining to each topic. First, we merged the 2020 corpus of speaker turns, each containing one of the nine topical keywords by topic (e.g., speaker turns containing “racism” or “racist” were merged into one topical corpus). Classifying 2020 Speaker Turns. For each of the six topical corpora, we trained BERT (BERT-Base-Uncased) by masking 15% of the words in each speaker turn and trained the model to fill in the masked words using a standard cross-entropy loss function. We then fine-tuned the adapted model to predict whether a speaker turn was from CNN or Fox News. For this step, we split the data into 80% training, 10% validation, and 10% test sets. We set batch sizes to 16, training epochs to 6 with 3-fold cross-validation, and used AdamW for optimization with a learning rate of 2e−05. We used PyTorch for all model implementations. Model Interpretation. To identify the contextual language or the attributive words that contributed most to the 2020 semantic polarity between CNN and Fox News, we used Integrated Gradients (IG) to interpret the classification results from our BERT-classifier in the previous step. IG is an attribution method that allows us to understand how much an input feature contributes to the output result of a deep neural network (Sundararajan, Taly, and Yan 2017). This post-hoc interpretability technique combines the input’s gradients by interpolating in short increments along a straight line between a baseline x (usually a vector with all zeros) and the input x 0 . Formally, if F : Rn → [0, 1] represents BERT, then the integrated gradient (IG) of the i th dimension of x is: Model Interpretation. Large pre-trained transformer-based language models, such as BERT may have strong predictive powers, but their multiple nonlinearities prevent users from directly attributing model outputs to various input features. To overcome this lack of interpretive insight, we leverage IG to identify words that are most attributable to each predicted class. Thus, we identify which contextual words within a speaker turn (input features of a classifier) contribute most to, or are most predictive of the output decision, namely, whether the speaker turn is from CNN or Fox News in 2020. Results Our BERT-classifier distinguishes 2020 speaker turns from CNN and Fox News with a mean accuracy of 75.3% across all six topics (Table 3). Model performance is highest for speaker turns topically focused on the police, corresponding to Study 1 findings demonstrating highest SP scores for “police” across all keywords in 2020. In Table 4, we show the top 10 words most predictive of how CNN and Fox News discuss the six topics on their news programs in 2020. To avoid noise caused by low-frequency words, we limited our token selection to words with a frequency above the 95th percentile. We categorized the tokens with the highest and lowest attribution scores ranging from positive (attributed to CNN) to negative (attributed to Fox News) values for each topic. As shown, across all topics, none of the top tokens overlap between the two stations, indicating the drastically distinct contexts in which the two channels discuss key topics in 2020. For example, words like “illegal”, “enforcement”, and “order” contextualize immigration discussions in a rather legalistic manner on Fox News whereas, “parents”, “family”, “children”, “daughter”, and “communities” - humanizing words connotative of family relations best predict how CNN discusses the same topic. Both stations may be discussing the same topic with identical keywords, but the contextual language surrounding their discussion is strikingly different. Such divergence sheds light on the underlying differences in the linguistic signature of televised media language that help explain the 2020 peak in semantic polarity between the two stations. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Words that Characterize Semantic Polarity between Fox News & CNN in 2020

Authors:
(1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu);
(2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu);
(3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ). Table of Links Abstract and Introduction Related Work Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Discussion and Ethics Statement Appendix and References Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) NLP researchers studying diachronic shifts have been leveraging word embeddings from language models to understand how the meaning of a given word changes over time (Kutuzov et al. 2018). In Study 1, we adapt methodological intuitions from such prior work to calculate how semantic polarization — the semantic distance between how two entities contextually use an identical word — evolves over time. While scholars have examined online polarization in the context of understanding partisan framing and public discourse around current events, our work is the first to quantify and capture the evolution of semantic polarization in broadcast media language. Dataset Our data consists of transcribed closed captions from news shows that were broadcast 24-7 by CNN and Fox News from January 1, 2010 to December 31, 2020, which was provided by the Internet Archive and the Stanford Cable TV News Analyzer. We computationally extracted closed captions pertaining to each televised segment from a total of 181K SRT (SubRip Subtitle) files — consisting of 23.8 million speaker turns or 297 billion words spoken across the decade (Figure 1). To focus our analysis on how socially important yet politically controversial topics were discussed between the two major news networks, we narrowed down our sample of speaker turns to those that contained keywords associated with six politically contentious topics: racism, Black Lives Matter, police, immigration, climate change, and health care (Table 1). We extracted all sentences containing one of the nine topical keywords, giving us a final corpus of 2.1 million speaker turns or 19.4 billion words. All closed captions pertaining to commercials were removed using a matching search algorithm. (Tuba, Akashe, and Joshi 2020). Method and Analysis With more recent advances in deep learning, researchers have leveraged the ability of transformer-based language models to generate contextualized embeddings to detect the semantic change of words across time (Kutuzov et al. 2018). Similarly, we leverage contextual word representations from BERT (Bidirectional Encoder Representations from Transformers) (Devlin et al. 2019) to calculate the semantic polarity in how CNN and Fox News use topical keywords over time. First, we use BERT to learn a set of embeddings for each keyword from the hidden layers of the model for every year from 2010 to 2020, to quantify how much the embeddings for a given topical keyword from CNN semantically differ from those of Fox across an 11-year period. We extracted all contextual word representations from our corpus of 2.1 million speaker turns. Then, for every occurrence of the 9 topical keywords in the data, we calculated the semantic distance between each contextual word representation from CNN and Fox using cosine values. The use of cosine values of BERT word embeddings is standard in machine translation and other NLP domains for computing contextual similarities in how tokens are used in sentences (Zhang et al. 2020). Our calculation of semantic polarization between how CNN vs. Fox News uses a topical keyword for a given year is as follows. Given the set of word embeddings for a keyword in a given year t from CNN: and the set of word embeddings for a keyword in a given year t from Fox News: we define the semantic polarity (SP) between CNN and Fox News as: where: In Figure 2, we visualize our methodological framework to illustrate how we derive the semantic polarization (SP) score in how CNN vs. Fox News uses the keyword, “racist” in 2010. First, we extract all contextual word embeddings (each of which is a 768×1 tensor) associated with every occurrence of the word “racist” in our corpus of speaker turns from Fox and CNN in 2010. Next, we calculate the cosine distance between the tensors associated with the word representation “racist” in the CNN corpus and those from the Fox News corpus. Finally, we use Equation 3 to calculate the average of all cosine distance values to obtain the semantic polarization score or the semantic distance between how Fox News and CNN uses the word “racist” in 2010. Results Between 2010 and 2020, semantic polarity between America’s two largest TV news stations increased with a significant upward trend starting in 2015 and 2016 (Figure 3). Over the past decade, CNN and Fox News became increasingly semantically divergent in how they used the topical keywords “racist”, “racism”, “police”, “blackbivesmatter”, “immigrant”, “immigration”, “climate change”,“global warming”, and “health care” in their broadcasting language. Though there is a general dip in semantic polarity around 2013-14 across all keywords with the exception of “health care”, semantic polarity between the two stations starts to sharply increase every year on average by 112% across all keywords starting in 2016. These results corroborate findings from a recent study analyzing the airtime of partisan actors on CNN and Fox, which shows that the two stations became more polarized in terms of visibility bias particularly following the 2016 election (Kim, Lelkes, and McCrain 2022). Further, the highest peaks generally occur in 2020 for the terms “racism”, “racist”, “blacklivesmatter”, “police”, along with “immigrant”, and “global warming” (Table 2). Specifically, semantic polarity is highest for the keyword “police” in 2020, denoting that across the nine keywords, CNN and Fox were most polarized in how they semantically used the word “police” in their news programs in 2020. This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu); (2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu); (3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ). Authors: Authors: (1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu); (2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu); (3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ). Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Discussion and Ethics Statement Discussion and Ethics Statement Appendix and References Appendix and References Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) NLP researchers studying diachronic shifts have been leveraging word embeddings from language models to understand how the meaning of a given word changes over time (Kutuzov et al. 2018). In Study 1, we adapt methodological intuitions from such prior work to calculate how semantic polarization — the semantic distance between how two entities contextually use an identical word — evolves over time. While scholars have examined online polarization in the context of understanding partisan framing and public discourse around current events, our work is the first to quantify and capture the evolution of semantic polarization in broadcast media language. Dataset Our data consists of transcribed closed captions from news shows that were broadcast 24-7 by CNN and Fox News from January 1, 2010 to December 31, 2020, which was provided by the Internet Archive and the Stanford Cable TV News Analyzer. We computationally extracted closed captions pertaining to each televised segment from a total of 181K SRT (SubRip Subtitle) files — consisting of 23.8 million speaker turns or 297 billion words spoken across the decade (Figure 1). To focus our analysis on how socially important yet politically controversial topics were discussed between the two major news networks, we narrowed down our sample of speaker turns to those that contained keywords associated with six politically contentious topics: racism, Black Lives Matter, police, immigration, climate change, and health care (Table 1). We extracted all sentences containing one of the nine topical keywords, giving us a final corpus of 2.1 million speaker turns or 19.4 billion words. All closed captions pertaining to commercials were removed using a matching search algorithm. (Tuba, Akashe, and Joshi 2020). Method and Analysis With more recent advances in deep learning, researchers have leveraged the ability of transformer-based language models to generate contextualized embeddings to detect the semantic change of words across time (Kutuzov et al. 2018). Similarly, we leverage contextual word representations from BERT (Bidirectional Encoder Representations from Transformers) (Devlin et al. 2019) to calculate the semantic polarity in how CNN and Fox News use topical keywords over time. First, we use BERT to learn a set of embeddings for each keyword from the hidden layers of the model for every year from 2010 to 2020, to quantify how much the embeddings for a given topical keyword from CNN semantically differ from those of Fox across an 11-year period. We extracted all contextual word representations from our corpus of 2.1 million speaker turns. Then, for every occurrence of the 9 topical keywords in the data, we calculated the semantic distance between each contextual word representation from CNN and Fox using cosine values. The use of cosine values of BERT word embeddings is standard in machine translation and other NLP domains for computing contextual similarities in how tokens are used in sentences (Zhang et al. 2020). Our calculation of semantic polarization between how CNN vs. Fox News uses a topical keyword for a given year is as follows. Given the set of word embeddings for a keyword in a given year t from CNN: and the set of word embeddings for a keyword in a given year t from Fox News: we define the semantic polarity (SP) between CNN and Fox News as: where: In Figure 2, we visualize our methodological framework to illustrate how we derive the semantic polarization (SP) score in how CNN vs. Fox News uses the keyword, “racist” in 2010. First, we extract all contextual word embeddings (each of which is a 768×1 tensor) associated with every occurrence of the word “racist” in our corpus of speaker turns from Fox and CNN in 2010. Next, we calculate the cosine distance between the tensors associated with the word representation “racist” in the CNN corpus and those from the Fox News corpus. Finally, we use Equation 3 to calculate the average of all cosine distance values to obtain the semantic polarization score or the semantic distance between how Fox News and CNN uses the word “racist” in 2010. Results Between 2010 and 2020, semantic polarity between America’s two largest TV news stations increased with a significant upward trend starting in 2015 and 2016 (Figure 3). Over the past decade, CNN and Fox News became increasingly semantically divergent in how they used the topical keywords “racist”, “racism”, “police”, “blackbivesmatter”, “immigrant”, “immigration”, “climate change”,“global warming”, and “health care” in their broadcasting language. Though there is a general dip in semantic polarity around 2013-14 across all keywords with the exception of “health care”, semantic polarity between the two stations starts to sharply increase every year on average by 112% across all keywords starting in 2016. These results corroborate findings from a recent study analyzing the airtime of partisan actors on CNN and Fox, which shows that the two stations became more polarized in terms of visibility bias particularly following the 2016 election (Kim, Lelkes, and McCrain 2022). Further, the highest peaks generally occur in 2020 for the terms “racism”, “racist”, “blacklivesmatter”, “police”, along with “immigrant”, and “global warming” (Table 2). Specifically, semantic polarity is highest for the keyword “police” in 2020, denoting that across the nine keywords, CNN and Fox were most polarized in how they semantically used the word “police” in their news programs in 2020. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

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