Meysam Alizadeh
Biographic Data
| ID | 3925356 |
|---|---|
| NAME | Meysam Alizadeh |
| GIVEN NAMES | Meysam |
| FAMILY NAME | Alizadeh |
| SIGNATURE | ALIZADEH M |
| AFFILIATIONS | University of Zurich |
| ORCID | 0000-0001-6696-6471 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 13 |
| AUTHOR COUNT | 7 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 3 |
How Negative Media Coverage Impacts Platform Governance: Evidence from Facebook, Twitter, and YouTube
Social media companies wield considerable power over what people can say and do online, with consequences for freedom of expression and participation in digital culture. Yet we still know little about the factors that shape these companies' policy decisions. Drawing on data collected from mainstream English-language news sources between 2005-2021 and on a novel dataset of policy documents from the Platform Governance Archive (PGA), we investigate…
Open-source LLMs for text annotation: A Practical Guide for Model Setting and Fine-Tuning
This paper studies the performance of open-source Large Language Models (LLMs) in text classification tasks typical for political science research. By examining tasks like stance, topic, and relevance classification, we aim to guide scholars in making informed decisions about their use of LLMs for text analysis and to establish a baseline performance benchmark that demonstrates the models’ effectiveness. Specifically, we conduct an assessment of …
Comparing methods for creating a national random sample of twitter users
Twitter data has been widely used by researchers across various social and computer science disciplines. A common aim when working with Twitter data is the construction of a random sample of users from a given country. However, while several methods have been proposed in the literature, their comparative performance is mostly unexplored. In this paper, we implement four common methods to create a random sample of Twitter users in the US: 1% Strea…
ChatGPT outperforms crowd workers for text-annotation tasks
Many NLP applications require manual text annotations for a variety of tasks, notably to train classifiers or evaluate the performance of unsupervised models. Depending on the size and degree of complexity, the tasks may be conducted by crowd workers on platforms such as MTurk as well as trained annotators, such as research assistants. Using four samples of tweets and news articles ( n = 6,183), we show that ChatGPT outperforms crowd workers for …
Activation Regimes in Opinion Dynamics: Comparing Asynchronous Updating Schemes
Activation Regimes in Opinion Dynamics: Comparing Asynchronous Updating Schemes
Empirical evidences have supported the large heterogeneity in the timing of individuals' activities. Moreover, computational analysis of the agent-based models has shown the importance of the activation regimes. In this paper, we apply four different asynchronous updating schemes including random, uniform, and two state-driven Poisson updating schemes on an agent-based opinion dynamics model. We compare the effect of these activation regimes by m…
Intergroup Conflict Escalation Leads to More Extremism
Empirical findings in intergroup conflict literature show that individuals that hold beliefs that include differentiation from out-groups become radicalized as intergroup tensions escalate. They also show that this differentiation is proportional to tension escalation. In this paper, we present and demonstrate an agent-based model that captures these findings to better understand the effect of perceived intergroup conflict escalation on the avera…
How Negative Media Coverage Impacts Platform Governance: Evidence from Facebook, Twitter, and YouTube
Social media companies wield considerable power over what people can say and do online, with consequences for freedom of expression and participation in digital culture. Yet we still know little about the factors that shape these companies' policy decisions. Drawing on data collected from mainstream English-language news sources between 2005-2021 and on a novel dataset of policy documents from the Platform Governance Archive (PGA), we investigate…
Intergroup Conflict Escalation Leads to More Extremism
Empirical findings in intergroup conflict literature show that individuals that hold beliefs that include differentiation from out-groups become radicalized as intergroup tensions escalate. They also show that this differentiation is proportional to tension escalation. In this paper, we present and demonstrate an agent-based model that captures these findings to better understand the effect of perceived intergroup conflict escalation on the avera…
Open-source LLMs for text annotation: A Practical Guide for Model Setting and Fine-Tuning
This paper studies the performance of open-source Large Language Models (LLMs) in text classification tasks typical for political science research. By examining tasks like stance, topic, and relevance classification, we aim to guide scholars in making informed decisions about their use of LLMs for text analysis and to establish a baseline performance benchmark that demonstrates the models’ effectiveness. Specifically, we conduct an assessment of …
Activation Regimes in Opinion Dynamics: Comparing Asynchronous Updating Schemes
Empirical evidences have supported the large heterogeneity in the timing of individuals' activities. Moreover, computational analysis of the agent-based models has shown the importance of the activation regimes. In this paper, we apply four different asynchronous updating schemes including random, uniform, and two state-driven Poisson updating schemes on an agent-based opinion dynamics model. We compare the effect of these activation regimes by m…
Intergroup Conflict Escalation Leads to More Extremism
Empirical findings in intergroup conflict literature show that individuals that hold beliefs that include differentiation from out-groups become radicalized as intergroup tensions escalate. They also show that this differentiation is proportional to tension escalation. In this paper, we present and demonstrate an agent-based model that captures these findings to better understand the effect of perceived intergroup conflict escalation on the avera…
Activation Regimes in Opinion Dynamics: Comparing Asynchronous Updating Schemes
Activation Regimes in Opinion Dynamics: Comparing Asynchronous Updating Schemes
Empirical evidences have supported the large heterogeneity in the timing of individuals' activities. Moreover, computational analysis of the agent-based models has shown the importance of the activation regimes. In this paper, we apply four different asynchronous updating schemes including random, uniform, and two state-driven Poisson updating schemes on an agent-based opinion dynamics model. We compare the effect of these activation regimes by m…
ChatGPT outperforms crowd workers for text-annotation tasks
Many NLP applications require manual text annotations for a variety of tasks, notably to train classifiers or evaluate the performance of unsupervised models. Depending on the size and degree of complexity, the tasks may be conducted by crowd workers on platforms such as MTurk as well as trained annotators, such as research assistants. Using four samples of tweets and news articles ( n = 6,183), we show that ChatGPT outperforms crowd workers for …
Comparing methods for creating a national random sample of twitter users
Twitter data has been widely used by researchers across various social and computer science disciplines. A common aim when working with Twitter data is the construction of a random sample of users from a given country. However, while several methods have been proposed in the literature, their comparative performance is mostly unexplored. In this paper, we implement four common methods to create a random sample of Twitter users in the US: 1% Strea…
How Negative Media Coverage Impacts Platform Governance: Evidence from Facebook, Twitter, and YouTube
Social media companies wield considerable power over what people can say and do online, with consequences for freedom of expression and participation in digital culture. Yet we still know little about the factors that shape these companies' policy decisions. Drawing on data collected from mainstream English-language news sources between 2005-2021 and on a novel dataset of policy documents from the Platform Governance Archive (PGA), we investigate…
Open-source LLMs for text annotation: A Practical Guide for Model Setting and Fine-Tuning
This paper studies the performance of open-source Large Language Models (LLMs) in text classification tasks typical for political science research. By examining tasks like stance, topic, and relevance classification, we aim to guide scholars in making informed decisions about their use of LLMs for text analysis and to establish a baseline performance benchmark that demonstrates the models’ effectiveness. Specifically, we conduct an assessment of …
Computer Science (6 works) · Opinion Dynamics and Social Influence (4 works) · Mathematics (3 works) · Physics (3 works) · Statistics (3 works) · Annotation (2 works) · Artificial Intelligence (2 works) · Asynchronous communication (2 works) · Complex Network Analysis Techniques (2 works) · Counterintuitive (2 works)