Philip Feldman | University of Maryland Baltimore County (original) (raw)
Papers by Philip Feldman
28th International Conference on Intelligent User Interfaces
We have developed a set of Python applications that use large language models to identify and ana... more We have developed a set of Python applications that use large language models to identify and analyze data from social media platforms relevant to a population of interest. Our pipeline begins with using OpenAI's GPT-3 to generate potential keywords for identifying relevant text content from the target population. The keywords are then validated, and the content downloaded and analyzed using GPT-3 embedding and manifold reduction. Corpora are then created to fine-tune GPT-2 models to explore latent information via prompt-based queries. These tools allow researchers and practitioners to gain valuable insights into population subgroups online. CCS CONCEPTS • Software and its engineering → Software libraries and repositories; • Information systems → Specialized information retrieval; • Human-centered computing → User interface toolkits; • Computer systems organization → Embedded systems.
![Research paper thumbnail of The Resilience of "Planes, Trains, and Automobiles": Comment on "Driverless Cars Will Make Passenger Rail Obsolete," by Yair Wiseman [Opinion]](https://attachments.academia-assets.com/110826672/thumbnails/1.jpg)
](IEEE Technology and Society Magazine, 2019
Proceedings of the 2018 Conference on Human Information Interaction&Retrieval - CHIIR '18
The detection of echo chambers and information bubbles is becoming increasingly relevant in this ... more The detection of echo chambers and information bubbles is becoming increasingly relevant in this era of polarized information. It may be possible to evaluate information trustworthiness by examining the behavior of individuals in belief space rather than evaluating the information itself, which is a harder problem. To explore this, I propose to research a model for information retrieval that integrates two levels of information interaction. On the individual level, I leverage Munson and Resnick»s diversity-seeker, confirmer, and avoider patterns. At a group level, I integrate individual behaviors according to Moskivici»s work on crowd polarization. These perspectives have been integrated in a simulation that employs insights from animal collective behavior to model agent groups, which enable the systematic exploration of belief navigation behaviors that can be detected algorithmically. Viewing information retrieval from the perspective of belief spaces may shed light on current practices and lay out consideration for future design work.
ACM Computing Surveys
Social media is a modern person’s digital voice to project and engage with new ideas and mobilise... more Social media is a modern person’s digital voice to project and engage with new ideas and mobilise communities—a power shared with extremists. Given the societal risks of unvetted content-moderating algorithms for Extremism, Radicalisation , and Hate speech (ERH) detection, responsible software engineering must understand the who, what, when, where, and why such models are necessary to protect user safety and free expression. Hence, we propose and examine the unique research field of ERH context mining to unify disjoint studies. Specifically, we evaluate the start-to-finish design process from socio-technical definition-building and dataset collection strategies to technical algorithm design and performance. Our 2015–2021 51-study Systematic Literature Review (SLR) provides the first cross-examination of textual, network, and visual approaches to detecting extremist affiliation, hateful content, and radicalisation towards groups and movements. We identify consensus-driven ERH definit...
Modern natural language models such as the GPT-2/GPT-3 contain tremendous amounts of information ... more Modern natural language models such as the GPT-2/GPT-3 contain tremendous amounts of information about human belief in a consistently testable form. If these models could be shown to accurately reflect the underlying beliefs of the human beings that produced the data used to train these models, then such models become a powerful sociological tool in ways that are distinct from traditional methods, such as interviews and surveys. In this study, We train a version of the GPT-2 on a corpora of historical chess games, and then "launch" clusters of synthetic agents into the model, using text strings to create context and orientation. We compare the trajectories contained in the text generated by the agents/model and compare that to the known ground truth of the chess board, move legality, and historical patterns of play. We find that the percentages of moves by piece using the model are substantially similar from human patterns. We further find that the model creates an accurate latent representation of the chessboard, and that it is possible to plot trajectories of legal moves across the board using this knowledge.
Text analysis of social media for sentiment, topic analysis, and other analysis depends initially... more Text analysis of social media for sentiment, topic analysis, and other analysis depends initially on the selection of keywords and phrases that will be used to create the research corpora. However, keywords that researchers choose may occur infrequently, leading to errors that arise from using small samples. In this paper, we use the capacity for memorization, interpolation, and extrapolation of Transformer Language Models such as the GPT series to learn the linguistic behaviors of a subgroup within larger corpora of Yelp reviews. We then use prompt-based queries to generate synthetic text that can be analyzed to produce insights into specific opinions held by the populations that the models were trained on. Once learned, more specific sentiment queries can be made of the model with high levels of accuracy when compared to traditional keyword searches. We show that even in cases where a specific keyphrase is limited or not present at all in the training corpora, the GPT is able to accurately generate large volumes of text that have the correct sentiment.
ArXiv, 2019
Beliefs are not facts, but they are factive - they feel like facts. This property is what can mak... more Beliefs are not facts, but they are factive - they feel like facts. This property is what can make misinformation dangerous. Being able to deliberately navigate through a landscape of often conflicting factive statements is difficult when there is no way to show the relationships between them without incorporating the information in linear, narrative forms. In this paper, we present a mechanism to produce maps of belief places, where populations agree on salient features of fictional environments, and belief spaces, where subgroups have related but distinct perspectives. Using a model developed using agent-based simulation, we show that by observing the repeated behaviors of human participants in the same social context, it is possible to build maps that show the shared narrative environment overlaid with traces that show unique, individual or subgroup perspectives. Our contribution is a proof-of-concept system, based on the affordances of fantasy tabletop role-playing games, which ...
Our information environment can be viewed as a densely-connected socio-technical ecosystem. We co... more Our information environment can be viewed as a densely-connected socio-technical ecosystem. We communicate with each other through computers. Our networks include more than the obvious technologies like VOIP or social media. Whenever we run a search, algorithms monitor our behavior -- if enough of us pick the second or third link, then that link will rise, and a self-reinforcing pattern of query and result is created. These feedback loops crowd out the different or unusual, making the accessible information environment become less diverse. At scale, this can make our socio-technical ecosystem brittle and less resilient to shocks or manipulation. This dissertation explores these issues in two parts. The first is the development of Stampede Theory, which shows how animal behavior patterns in physical environments are similar to human belief-based behavior in online environments. There are nomadic explorers, individuals or small groups dispersed over large areas. Those who flock, engag...
ArXiv, 2018
In the parable of Simon's Ant, an ant follows a complex path along a beach on to reach its go... more In the parable of Simon's Ant, an ant follows a complex path along a beach on to reach its goal. The story shows how the interaction of simple rules and a complex environment result in complex behavior. But this relationship can be looked at in another way - given path and rules, we can infer the environment. With a large population of agents - human or animal - it should be possible to build a detailed map of a population's social and physical environment. In this abstract, we describe the development of a framework to create such maps of human belief space. These maps are built from the combined trajectories of a large number of agents. Currently, these maps are built using multidimensional agent-based simulation, but the framework is designed to work using data from computer-mediated human communication. Maps incorporating human data should support visualization and navigation of the "plains of research", "fashionable foothills" and "conspiracy cl...
The problem of determining if a military unit has correctly understood an order and is properly e... more The problem of determining if a military unit has correctly understood an order and is properly executing on it is one that has bedeviled military planners throughout history. The advent of advanced language models such as OpenAI's GPT-series offers new possibilities for addressing this problem. This paper presents a mechanism to harness the narrative output of large language models and produce diagrams or "maps" of the relationships that are latent in the weights of such models as the GPT-3. The resulting "Neural Narrative Maps" (NNMs), are intended to provide insight into the organization of information, opinion, and belief in the model, which in turn provide means to understand intent and response in the context of physical distance. This paper discusses the problem of mapping information spaces in general, and then presents a concrete implementation of this concept in the context of OpenAI's GPT-3 language model for determining if a subordinate is fol...
This paper describes a method for using Transformer-based Language Models (TLMs) to understand pu... more This paper describes a method for using Transformer-based Language Models (TLMs) to understand public opinion from social media posts. In this approach, we train a set of GPT models on several COVID-19 tweet corpora that reflect populations of users with distinctive views. We then use prompt-based queries to probe these models to reveal insights into the biases and opinions of the users. We demonstrate how this approach can be used to produce results which resemble polling the public on diverse social, political and public health issues. The results on the COVID-19 tweet data show that transformer language models are promising tools that can help us understand public opinions on social media at scale.
This paper describes the use of neural networks to enhance simulations for subsequent training of... more This paper describes the use of neural networks to enhance simulations for subsequent training of anomaly-detection systems. Simulations can provide edge conditions for anomaly detection which may be sparse or non-existent in real-world data. Simulations suffer, however, by producing data that is "too clean" resulting in anomaly detection systems that cannot transition from simulated data to actual conditions. Our approach enhances simulations using neural networks trained on real-world data to create outputs that are more realistic and variable than traditional simulations.
Modern natural language models such as the GPT-2/GPT-3 contain tremendous amounts of information ... more Modern natural language models such as the GPT-2/GPT-3 contain tremendous amounts of information about human belief in a consistently interrogatable form. If these models could be shown to accurately reflect the underlying beliefs of the human beings that produced the data used to train these models, then such models become a powerful sociological tool in ways that are distinct from traditional methods, such as interviews and surveys. In this study, We train a version of the GPT-2 on a corpora of historical chess games, and then compare the learned relationships of words in the model to the known ground truth of the chess board, move legality, and historical patterns of play. We find that the percentages of moves by piece using the model are substantially similar from human patterns. We further find that the model creates an accurate latent representation of the chessboard, and that it is possible to plot trajectories of legal moves across the board using this knowledge.
Tabletop fantasy role-playing games (TFRPGs) have existed in offline and online contexts for many... more Tabletop fantasy role-playing games (TFRPGs) have existed in offline and online contexts for many decades, yet are rarely featured in scientific literature. This paper presents a case study where TFRPGs were used to generate and collect data for maps of belief environments using fiction co-created by multiple small groups of online tabletop gamers. The affordances of TFRPGs allowed us to collect repeatable, targeted data in online field conditions. These data not only included terms that allowed us to build our maps, but also to explore nuanced ethical problems from a situated, collaborative perspective.
This paper describes a method for using Transformer-based Language Models (TLMs) to understand pu... more This paper describes a method for using Transformer-based Language Models (TLMs) to understand public opinion from social media posts. In this approach, we train a set of GPT models on several COVID-19 tweet corpora. We then use prompt-based queries to probe these models to reveal insights into the opinions of social media users. We demonstrate how this approach can be used to produce results which resemble polling the public on diverse social, political and public health issues. The results on the COVID-19 tweet data show that transformer language models are promising tools that can help us understand public opinions on social media at scale.
ArXiv, 2019
Tabletop fantasy role-playing games (TFRPGs) have existed in offline and online contexts for many... more Tabletop fantasy role-playing games (TFRPGs) have existed in offline and online contexts for many decades, yet are rarely featured in scientific literature. This paper presents a case study where TFRPGs were used to generate and collect data for maps of belief environments using fiction co-created by multiple small groups of online tabletop gamers. The affordances of TFRPGs allowed us to collect repeatable, targeted data in online field conditions. These data not only included terms that allowed us to build our maps, but also to explore nuanced ethical problems from a situated, collaborative perspective.
Modern natural language models such as the GPT-2/GPT-3 contain tremendous amounts of information ... more Modern natural language models such as the GPT-2/GPT-3 contain tremendous amounts of information about human belief in a consistently interrogatable form. If these models could be shown to accurately reflect the underlying beliefs of the human beings that produced the data used to train these models, then such models become a powerful sociological tool in ways that are distinct from traditional methods, such as interviews and surveys. In this study, We train a version of the GPT-2 on a corpora of historical chess games, and then compare the learned relationships of words in the model to the known ground truth of the chess board, move legality, and historical patterns of play. We find that the percentages of moves by piece using the model are substantially similar from human patterns. We further find that the model creates an accurate latent representation of the chessboard, and that it is possible to plot trajectories of legal moves across the board using this knowledge.
IEEE Trans. Intell. Transp. Syst., 2021
ArXiv, 2021
This paper describes a method for using Transformer-based Language Models (TLMs) to understand pu... more This paper describes a method for using Transformer-based Language Models (TLMs) to understand public opinion from social media posts. In this approach, we train a set of GPT models on several COVID-19 tweet corpora that reflect populations of users with distinctive views. We then use promptbased queries to probe these models to reveal insights into the biases and opinions of the users. We demonstrate how this approach can be used to produce results which resemble polling the public on diverse social, political and public health issues. The results on the COVID-19 tweet data show that transformer language models are promising tools that can help us understand public opinions on social media at scale.
28th International Conference on Intelligent User Interfaces
We have developed a set of Python applications that use large language models to identify and ana... more We have developed a set of Python applications that use large language models to identify and analyze data from social media platforms relevant to a population of interest. Our pipeline begins with using OpenAI's GPT-3 to generate potential keywords for identifying relevant text content from the target population. The keywords are then validated, and the content downloaded and analyzed using GPT-3 embedding and manifold reduction. Corpora are then created to fine-tune GPT-2 models to explore latent information via prompt-based queries. These tools allow researchers and practitioners to gain valuable insights into population subgroups online. CCS CONCEPTS • Software and its engineering → Software libraries and repositories; • Information systems → Specialized information retrieval; • Human-centered computing → User interface toolkits; • Computer systems organization → Embedded systems.
IEEE Technology and Society Magazine, 2019
Proceedings of the 2018 Conference on Human Information Interaction&Retrieval - CHIIR '18
The detection of echo chambers and information bubbles is becoming increasingly relevant in this ... more The detection of echo chambers and information bubbles is becoming increasingly relevant in this era of polarized information. It may be possible to evaluate information trustworthiness by examining the behavior of individuals in belief space rather than evaluating the information itself, which is a harder problem. To explore this, I propose to research a model for information retrieval that integrates two levels of information interaction. On the individual level, I leverage Munson and Resnick»s diversity-seeker, confirmer, and avoider patterns. At a group level, I integrate individual behaviors according to Moskivici»s work on crowd polarization. These perspectives have been integrated in a simulation that employs insights from animal collective behavior to model agent groups, which enable the systematic exploration of belief navigation behaviors that can be detected algorithmically. Viewing information retrieval from the perspective of belief spaces may shed light on current practices and lay out consideration for future design work.
ACM Computing Surveys
Social media is a modern person’s digital voice to project and engage with new ideas and mobilise... more Social media is a modern person’s digital voice to project and engage with new ideas and mobilise communities—a power shared with extremists. Given the societal risks of unvetted content-moderating algorithms for Extremism, Radicalisation , and Hate speech (ERH) detection, responsible software engineering must understand the who, what, when, where, and why such models are necessary to protect user safety and free expression. Hence, we propose and examine the unique research field of ERH context mining to unify disjoint studies. Specifically, we evaluate the start-to-finish design process from socio-technical definition-building and dataset collection strategies to technical algorithm design and performance. Our 2015–2021 51-study Systematic Literature Review (SLR) provides the first cross-examination of textual, network, and visual approaches to detecting extremist affiliation, hateful content, and radicalisation towards groups and movements. We identify consensus-driven ERH definit...
Modern natural language models such as the GPT-2/GPT-3 contain tremendous amounts of information ... more Modern natural language models such as the GPT-2/GPT-3 contain tremendous amounts of information about human belief in a consistently testable form. If these models could be shown to accurately reflect the underlying beliefs of the human beings that produced the data used to train these models, then such models become a powerful sociological tool in ways that are distinct from traditional methods, such as interviews and surveys. In this study, We train a version of the GPT-2 on a corpora of historical chess games, and then "launch" clusters of synthetic agents into the model, using text strings to create context and orientation. We compare the trajectories contained in the text generated by the agents/model and compare that to the known ground truth of the chess board, move legality, and historical patterns of play. We find that the percentages of moves by piece using the model are substantially similar from human patterns. We further find that the model creates an accurate latent representation of the chessboard, and that it is possible to plot trajectories of legal moves across the board using this knowledge.
Text analysis of social media for sentiment, topic analysis, and other analysis depends initially... more Text analysis of social media for sentiment, topic analysis, and other analysis depends initially on the selection of keywords and phrases that will be used to create the research corpora. However, keywords that researchers choose may occur infrequently, leading to errors that arise from using small samples. In this paper, we use the capacity for memorization, interpolation, and extrapolation of Transformer Language Models such as the GPT series to learn the linguistic behaviors of a subgroup within larger corpora of Yelp reviews. We then use prompt-based queries to generate synthetic text that can be analyzed to produce insights into specific opinions held by the populations that the models were trained on. Once learned, more specific sentiment queries can be made of the model with high levels of accuracy when compared to traditional keyword searches. We show that even in cases where a specific keyphrase is limited or not present at all in the training corpora, the GPT is able to accurately generate large volumes of text that have the correct sentiment.
ArXiv, 2019
Beliefs are not facts, but they are factive - they feel like facts. This property is what can mak... more Beliefs are not facts, but they are factive - they feel like facts. This property is what can make misinformation dangerous. Being able to deliberately navigate through a landscape of often conflicting factive statements is difficult when there is no way to show the relationships between them without incorporating the information in linear, narrative forms. In this paper, we present a mechanism to produce maps of belief places, where populations agree on salient features of fictional environments, and belief spaces, where subgroups have related but distinct perspectives. Using a model developed using agent-based simulation, we show that by observing the repeated behaviors of human participants in the same social context, it is possible to build maps that show the shared narrative environment overlaid with traces that show unique, individual or subgroup perspectives. Our contribution is a proof-of-concept system, based on the affordances of fantasy tabletop role-playing games, which ...
Our information environment can be viewed as a densely-connected socio-technical ecosystem. We co... more Our information environment can be viewed as a densely-connected socio-technical ecosystem. We communicate with each other through computers. Our networks include more than the obvious technologies like VOIP or social media. Whenever we run a search, algorithms monitor our behavior -- if enough of us pick the second or third link, then that link will rise, and a self-reinforcing pattern of query and result is created. These feedback loops crowd out the different or unusual, making the accessible information environment become less diverse. At scale, this can make our socio-technical ecosystem brittle and less resilient to shocks or manipulation. This dissertation explores these issues in two parts. The first is the development of Stampede Theory, which shows how animal behavior patterns in physical environments are similar to human belief-based behavior in online environments. There are nomadic explorers, individuals or small groups dispersed over large areas. Those who flock, engag...
ArXiv, 2018
In the parable of Simon's Ant, an ant follows a complex path along a beach on to reach its go... more In the parable of Simon's Ant, an ant follows a complex path along a beach on to reach its goal. The story shows how the interaction of simple rules and a complex environment result in complex behavior. But this relationship can be looked at in another way - given path and rules, we can infer the environment. With a large population of agents - human or animal - it should be possible to build a detailed map of a population's social and physical environment. In this abstract, we describe the development of a framework to create such maps of human belief space. These maps are built from the combined trajectories of a large number of agents. Currently, these maps are built using multidimensional agent-based simulation, but the framework is designed to work using data from computer-mediated human communication. Maps incorporating human data should support visualization and navigation of the "plains of research", "fashionable foothills" and "conspiracy cl...
The problem of determining if a military unit has correctly understood an order and is properly e... more The problem of determining if a military unit has correctly understood an order and is properly executing on it is one that has bedeviled military planners throughout history. The advent of advanced language models such as OpenAI's GPT-series offers new possibilities for addressing this problem. This paper presents a mechanism to harness the narrative output of large language models and produce diagrams or "maps" of the relationships that are latent in the weights of such models as the GPT-3. The resulting "Neural Narrative Maps" (NNMs), are intended to provide insight into the organization of information, opinion, and belief in the model, which in turn provide means to understand intent and response in the context of physical distance. This paper discusses the problem of mapping information spaces in general, and then presents a concrete implementation of this concept in the context of OpenAI's GPT-3 language model for determining if a subordinate is fol...
This paper describes a method for using Transformer-based Language Models (TLMs) to understand pu... more This paper describes a method for using Transformer-based Language Models (TLMs) to understand public opinion from social media posts. In this approach, we train a set of GPT models on several COVID-19 tweet corpora that reflect populations of users with distinctive views. We then use prompt-based queries to probe these models to reveal insights into the biases and opinions of the users. We demonstrate how this approach can be used to produce results which resemble polling the public on diverse social, political and public health issues. The results on the COVID-19 tweet data show that transformer language models are promising tools that can help us understand public opinions on social media at scale.
This paper describes the use of neural networks to enhance simulations for subsequent training of... more This paper describes the use of neural networks to enhance simulations for subsequent training of anomaly-detection systems. Simulations can provide edge conditions for anomaly detection which may be sparse or non-existent in real-world data. Simulations suffer, however, by producing data that is "too clean" resulting in anomaly detection systems that cannot transition from simulated data to actual conditions. Our approach enhances simulations using neural networks trained on real-world data to create outputs that are more realistic and variable than traditional simulations.
Modern natural language models such as the GPT-2/GPT-3 contain tremendous amounts of information ... more Modern natural language models such as the GPT-2/GPT-3 contain tremendous amounts of information about human belief in a consistently interrogatable form. If these models could be shown to accurately reflect the underlying beliefs of the human beings that produced the data used to train these models, then such models become a powerful sociological tool in ways that are distinct from traditional methods, such as interviews and surveys. In this study, We train a version of the GPT-2 on a corpora of historical chess games, and then compare the learned relationships of words in the model to the known ground truth of the chess board, move legality, and historical patterns of play. We find that the percentages of moves by piece using the model are substantially similar from human patterns. We further find that the model creates an accurate latent representation of the chessboard, and that it is possible to plot trajectories of legal moves across the board using this knowledge.
Tabletop fantasy role-playing games (TFRPGs) have existed in offline and online contexts for many... more Tabletop fantasy role-playing games (TFRPGs) have existed in offline and online contexts for many decades, yet are rarely featured in scientific literature. This paper presents a case study where TFRPGs were used to generate and collect data for maps of belief environments using fiction co-created by multiple small groups of online tabletop gamers. The affordances of TFRPGs allowed us to collect repeatable, targeted data in online field conditions. These data not only included terms that allowed us to build our maps, but also to explore nuanced ethical problems from a situated, collaborative perspective.
This paper describes a method for using Transformer-based Language Models (TLMs) to understand pu... more This paper describes a method for using Transformer-based Language Models (TLMs) to understand public opinion from social media posts. In this approach, we train a set of GPT models on several COVID-19 tweet corpora. We then use prompt-based queries to probe these models to reveal insights into the opinions of social media users. We demonstrate how this approach can be used to produce results which resemble polling the public on diverse social, political and public health issues. The results on the COVID-19 tweet data show that transformer language models are promising tools that can help us understand public opinions on social media at scale.
ArXiv, 2019
Tabletop fantasy role-playing games (TFRPGs) have existed in offline and online contexts for many... more Tabletop fantasy role-playing games (TFRPGs) have existed in offline and online contexts for many decades, yet are rarely featured in scientific literature. This paper presents a case study where TFRPGs were used to generate and collect data for maps of belief environments using fiction co-created by multiple small groups of online tabletop gamers. The affordances of TFRPGs allowed us to collect repeatable, targeted data in online field conditions. These data not only included terms that allowed us to build our maps, but also to explore nuanced ethical problems from a situated, collaborative perspective.
Modern natural language models such as the GPT-2/GPT-3 contain tremendous amounts of information ... more Modern natural language models such as the GPT-2/GPT-3 contain tremendous amounts of information about human belief in a consistently interrogatable form. If these models could be shown to accurately reflect the underlying beliefs of the human beings that produced the data used to train these models, then such models become a powerful sociological tool in ways that are distinct from traditional methods, such as interviews and surveys. In this study, We train a version of the GPT-2 on a corpora of historical chess games, and then compare the learned relationships of words in the model to the known ground truth of the chess board, move legality, and historical patterns of play. We find that the percentages of moves by piece using the model are substantially similar from human patterns. We further find that the model creates an accurate latent representation of the chessboard, and that it is possible to plot trajectories of legal moves across the board using this knowledge.
IEEE Trans. Intell. Transp. Syst., 2021
ArXiv, 2021
This paper describes a method for using Transformer-based Language Models (TLMs) to understand pu... more This paper describes a method for using Transformer-based Language Models (TLMs) to understand public opinion from social media posts. In this approach, we train a set of GPT models on several COVID-19 tweet corpora that reflect populations of users with distinctive views. We then use promptbased queries to probe these models to reveal insights into the biases and opinions of the users. We demonstrate how this approach can be used to produce results which resemble polling the public on diverse social, political and public health issues. The results on the COVID-19 tweet data show that transformer language models are promising tools that can help us understand public opinions on social media at scale.