Filippo Menczer
Biographic Data
| ID | 284922 |
|---|---|
| NAME | Filippo Menczer |
| GIVEN NAMES | Filippo |
| FAMILY NAME | Menczer |
| SIGNATURE | MENCZER F |
| AFFILIATIONS | Indiana University Bloomington |
| ORCID | 0000-0003-4384-2876 |
| VERIFIED | Yes |
| TOTAL WORKS | 23 |
| TOTAL CITATIONS | 47 |
| AUTHOR COUNT | 23 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2008 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 5 |
Demystifying Misconceptions in Social Bots Research
Research on social bots aims at advancing knowledge and providing solutions to one of the most debated forms of online manipulation. Yet, social bot research is plagued by widespread biases, hyped results, and misconceptions that set the stage for ambiguities, unrealistic expectations, and seemingly irreconcilable findings. Overcoming such issues is instrumental toward ensuring reliable solutions and reaffirming the validity of the scientific met…
Political audience diversity and news reliability in algorithmic ranking
Political Polarization on Twitter
In this study we investigate how social media shape the networked public sphere and facilitate communication between communities with different political orientations. We examine two networks of political communication on Twitter, comprised of more than 250,000 tweets from the six weeks leading up to the 2010 U.S. congressional midterm elections. Using a combination of network clustering algorithms and manually-annotated data we demonstrate that …
Detecting and Tracking Political Abuse in Social Media
We study astroturf political campaigns on microblogging platforms: politically-motivated individuals and organizations that use multiple centrally-controlled accounts to create the appearance of widespread support for a candidate or opinion. We describe a machine learning framework that combines topological, content-based and crowdsourced features of information diffusion networks on Twitter to detect the early stages of viral spreading of politi…
On the challenges of predicting microscopic dynamics of online conversations
To what extent can we predict the structure of online conversation trees? We present a generative model to predict the size and evolution of threaded conversations on social media by combining machine learning algorithms. The model is evaluated using datasets that span two topical domains (cryptocurrency and cyber-security) and two platforms (Reddit and Twitter). We show that it is able to predict both macroscopic features of the final trees and …
Uncovering Coordinated Networks on Social Media: Methods and Case Studies
Coordinated campaigns are used to influence and manipulate social media platforms and their users, a critical challenge to the free exchange of information online. Here we introduce a general, unsupervised network-based methodology to uncover groups of accounts that are likely coordinated. The proposed method constructs coordination networks based on arbitrary behavioral traces shared among accounts. We present five case studies of influence camp…
The Covid-19 Infodemic: Twitter versus Facebook
The global spread of the novel coronavirus is affected by the spread of related misinformation-the so-called COVID-19 Infodemic-that makes populations more vulnerable to the disease through resistance to mitigation efforts. Here, we analyze the prevalence and diffusion of links to low-credibility content about the pandemic across two major social media platforms, Twitter and Facebook. We characterize cross-platform similarities and differences in…
Recency predicts bursts in the evolution of author citations
The citations process for scientific papers has been studied extensively. But while the citations accrued by authors are the sum of the citations of their papers, translating the dynamics of citation accumulation from the paper to the author level is not trivial. Here we conduct a systematic study of the evolution of author citations, and in particular their bursty dynamics. We find empirical evidence of a correlation between the number of citati…
Asymmetrical perceptions of partisan political bots
Political bots are social media algorithms that impersonate political actors and interact with other users, aiming to influence public opinion. This study investigates the ability to differentiate bots with partisan personas from humans on Twitter. Our online experiment ( N = 656) explores how various characteristics of the participants and of the stimulus profiles bias recognition accuracy. The analysis reveals asymmetrical partisan-motivated re…
Social influence and unfollowing accelerate the emergence of echo chambers
While social media make it easy to connect with and access information from anyone, they also facilitate basic influence and unfriending mechanisms that may lead to segregated and polarized clusters known as “echo chambers.” Here we study the conditions in which such echo chambers emerge by introducing a simple model of information sharing in online social networks with the two ingredients of influence and unfriending. Users can change both their…
Arming the public with artificial intelligence to counter social bots
The increased relevance of social media in our daily life has been accompanied by efforts to manipulate online conversations and opinions. Deceptive social bots—automated or semi-automated accounts designed to impersonate humans—have been successfully exploited for these kinds of abuse. Researchers have responded by developing artificial intelligence (AI) tools to arm the public in the fight against social bots. Here we review the literature on d…
Online Human-Bot Interactions: Detection, Estimation, and Characterization
Increasing evidence suggests that a growing amount of social media content is generated by autonomous entities known as social bots. In this work we present a framework to detect such entities on Twitter. We leverage more than a thousand features extracted from public data and meta-data about users: friends, tweet content and sentiment, network patterns, and activity time series. We benchmark the classification framework by using a publicly avail…
Limited individual attention and online virality of low-quality information
BotOrNot: A System to Evaluate Social Bots
While most online social media accounts are controlled by humans, these platforms also host automated agents called social bots or sybil accounts. Recent literature reported on cases of social bots imitating humans to manipulate discussions, alter the popularity of users, pollute content and spread misinformation, and even perform terrorist propaganda and recruitment actions. Here we present BotOrNot, a publicly-available service that leverages m…
The rise of social bots
Today's social bots are sophisticated and sometimes menacing. Indeed, their presence can endanger online ecosystems as well as our society.
Clustering memes in social media streams
Virality Prediction and Community Structure in Social Networks
How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most …
Competition among memes in a world with limited attention
The wide adoption of social media has increased the competition among ideas for our finite attention. We employ a parsimonious agent-based model to study whether such a competition may affect the popularity of different memes, the diversity of information we are exposed to, and the fading of our collective interests for specific topics. Agents share messages on a social network but can only pay attention to a portion of the information they recei…
Partisan asymmetries in online political activity
We examine partisan differences in the behavior, communication patterns and social interactions of more than 18,000 politically-active Twitter users to produce evidence that points to changing levels of partisan engagement with the American online political landscape. Analysis of a network defined by the communication activity of these users in proximity to the 2010 midterm congressional elections reveals a highly segregated, well clustered, part…
Predicting the Political Alignment of Twitter Users
The widespread adoption of social media for political communication creates unprecedented opportunities to monitor the opinions of large numbers of politically active individuals in real time. However, without a way to distinguish between users of opposing political alignments, conflicting signals at the individual level may, in the aggregate, obscure partisan differences in opinion that are important to political strategy. In this article we des…
Effect of rule choice in dynamic interactive spatial commons
This paper uses laboratory experiments to examine the effect of an endogenous rule change from open access to private property as a potential solution to overharvesting in commons dilemmas. A novel, spatial, real-time renewable resource environment was used to investigate whether participants were willing to invest in changing the rules from an open access situation to a private property system. We found that half of the participants invested in …
Visual comparison of search results: A censorship case study
Understanding the qualitative differences between the sets of results from different search engines can be a difficult task. How many links must you follow from each list before you can reach a conclusion? We describe a user interface that allows users to quickly identify the most significant differences in content between two lists of Web pages. We have implemented this interface in CenSEARCHip, a system for comparing the effects of censorship p…
Effect of rule choice in dynamic interactive spatial commons
This paper uses laboratory experiments to examine the effect of an endogenous rule change from open access to private property as a potential solution to overharvesting in commons dilemmas. A novel, spatial, real-time renewable resource environment was used to investigate whether participants were willing to invest in changing the rules from an open access situation to a private property system. We found that half of the participants invested in …
Uncovering Coordinated Networks on Social Media: Methods and Case Studies
Coordinated campaigns are used to influence and manipulate social media platforms and their users, a critical challenge to the free exchange of information online. Here we introduce a general, unsupervised network-based methodology to uncover groups of accounts that are likely coordinated. The proposed method constructs coordination networks based on arbitrary behavioral traces shared among accounts. We present five case studies of influence camp…
Limited individual attention and online virality of low-quality information
The Covid-19 Infodemic: Twitter versus Facebook
The global spread of the novel coronavirus is affected by the spread of related misinformation-the so-called COVID-19 Infodemic-that makes populations more vulnerable to the disease through resistance to mitigation efforts. Here, we analyze the prevalence and diffusion of links to low-credibility content about the pandemic across two major social media platforms, Twitter and Facebook. We characterize cross-platform similarities and differences in…
Social influence and unfollowing accelerate the emergence of echo chambers
While social media make it easy to connect with and access information from anyone, they also facilitate basic influence and unfriending mechanisms that may lead to segregated and polarized clusters known as “echo chambers.” Here we study the conditions in which such echo chambers emerge by introducing a simple model of information sharing in online social networks with the two ingredients of influence and unfriending. Users can change both their…
Political audience diversity and news reliability in algorithmic ranking
Asymmetrical perceptions of partisan political bots
Political bots are social media algorithms that impersonate political actors and interact with other users, aiming to influence public opinion. This study investigates the ability to differentiate bots with partisan personas from humans on Twitter. Our online experiment ( N = 656) explores how various characteristics of the participants and of the stimulus profiles bias recognition accuracy. The analysis reveals asymmetrical partisan-motivated re…
Demystifying Misconceptions in Social Bots Research
Research on social bots aims at advancing knowledge and providing solutions to one of the most debated forms of online manipulation. Yet, social bot research is plagued by widespread biases, hyped results, and misconceptions that set the stage for ambiguities, unrealistic expectations, and seemingly irreconcilable findings. Overcoming such issues is instrumental toward ensuring reliable solutions and reaffirming the validity of the scientific met…
Effect of rule choice in dynamic interactive spatial commons
This paper uses laboratory experiments to examine the effect of an endogenous rule change from open access to private property as a potential solution to overharvesting in commons dilemmas. A novel, spatial, real-time renewable resource environment was used to investigate whether participants were willing to invest in changing the rules from an open access situation to a private property system. We found that half of the participants invested in …
Visual comparison of search results: A censorship case study
Understanding the qualitative differences between the sets of results from different search engines can be a difficult task. How many links must you follow from each list before you can reach a conclusion? We describe a user interface that allows users to quickly identify the most significant differences in content between two lists of Web pages. We have implemented this interface in CenSEARCHip, a system for comparing the effects of censorship p…
Effect of rule choice in dynamic interactive spatial commons
This paper uses laboratory experiments to examine the effect of an endogenous rule change from open access to private property as a potential solution to overharvesting in commons dilemmas. A novel, spatial, real-time renewable resource environment was used to investigate whether participants were willing to invest in changing the rules from an open access situation to a private property system. We found that half of the participants invested in …
Predicting the Political Alignment of Twitter Users
The widespread adoption of social media for political communication creates unprecedented opportunities to monitor the opinions of large numbers of politically active individuals in real time. However, without a way to distinguish between users of opposing political alignments, conflicting signals at the individual level may, in the aggregate, obscure partisan differences in opinion that are important to political strategy. In this article we des…
Competition among memes in a world with limited attention
The wide adoption of social media has increased the competition among ideas for our finite attention. We employ a parsimonious agent-based model to study whether such a competition may affect the popularity of different memes, the diversity of information we are exposed to, and the fading of our collective interests for specific topics. Agents share messages on a social network but can only pay attention to a portion of the information they recei…
Partisan asymmetries in online political activity
We examine partisan differences in the behavior, communication patterns and social interactions of more than 18,000 politically-active Twitter users to produce evidence that points to changing levels of partisan engagement with the American online political landscape. Analysis of a network defined by the communication activity of these users in proximity to the 2010 midterm congressional elections reveals a highly segregated, well clustered, part…
Virality Prediction and Community Structure in Social Networks
How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most …
Clustering memes in social media streams
BotOrNot: A System to Evaluate Social Bots
While most online social media accounts are controlled by humans, these platforms also host automated agents called social bots or sybil accounts. Recent literature reported on cases of social bots imitating humans to manipulate discussions, alter the popularity of users, pollute content and spread misinformation, and even perform terrorist propaganda and recruitment actions. Here we present BotOrNot, a publicly-available service that leverages m…
The rise of social bots
Today's social bots are sophisticated and sometimes menacing. Indeed, their presence can endanger online ecosystems as well as our society.
Online Human-Bot Interactions: Detection, Estimation, and Characterization
Increasing evidence suggests that a growing amount of social media content is generated by autonomous entities known as social bots. In this work we present a framework to detect such entities on Twitter. We leverage more than a thousand features extracted from public data and meta-data about users: friends, tweet content and sentiment, network patterns, and activity time series. We benchmark the classification framework by using a publicly avail…
Limited individual attention and online virality of low-quality information
Arming the public with artificial intelligence to counter social bots
The increased relevance of social media in our daily life has been accompanied by efforts to manipulate online conversations and opinions. Deceptive social bots—automated or semi-automated accounts designed to impersonate humans—have been successfully exploited for these kinds of abuse. Researchers have responded by developing artificial intelligence (AI) tools to arm the public in the fight against social bots. Here we review the literature on d…
Recency predicts bursts in the evolution of author citations
The citations process for scientific papers has been studied extensively. But while the citations accrued by authors are the sum of the citations of their papers, translating the dynamics of citation accumulation from the paper to the author level is not trivial. Here we conduct a systematic study of the evolution of author citations, and in particular their bursty dynamics. We find empirical evidence of a correlation between the number of citati…
Asymmetrical perceptions of partisan political bots
Political bots are social media algorithms that impersonate political actors and interact with other users, aiming to influence public opinion. This study investigates the ability to differentiate bots with partisan personas from humans on Twitter. Our online experiment ( N = 656) explores how various characteristics of the participants and of the stimulus profiles bias recognition accuracy. The analysis reveals asymmetrical partisan-motivated re…
Social influence and unfollowing accelerate the emergence of echo chambers
While social media make it easy to connect with and access information from anyone, they also facilitate basic influence and unfriending mechanisms that may lead to segregated and polarized clusters known as “echo chambers.” Here we study the conditions in which such echo chambers emerge by introducing a simple model of information sharing in online social networks with the two ingredients of influence and unfriending. Users can change both their…
Political Polarization on Twitter
In this study we investigate how social media shape the networked public sphere and facilitate communication between communities with different political orientations. We examine two networks of political communication on Twitter, comprised of more than 250,000 tweets from the six weeks leading up to the 2010 U.S. congressional midterm elections. Using a combination of network clustering algorithms and manually-annotated data we demonstrate that …
Detecting and Tracking Political Abuse in Social Media
We study astroturf political campaigns on microblogging platforms: politically-motivated individuals and organizations that use multiple centrally-controlled accounts to create the appearance of widespread support for a candidate or opinion. We describe a machine learning framework that combines topological, content-based and crowdsourced features of information diffusion networks on Twitter to detect the early stages of viral spreading of politi…
On the challenges of predicting microscopic dynamics of online conversations
To what extent can we predict the structure of online conversation trees? We present a generative model to predict the size and evolution of threaded conversations on social media by combining machine learning algorithms. The model is evaluated using datasets that span two topical domains (cryptocurrency and cyber-security) and two platforms (Reddit and Twitter). We show that it is able to predict both macroscopic features of the final trees and …
Uncovering Coordinated Networks on Social Media: Methods and Case Studies
Coordinated campaigns are used to influence and manipulate social media platforms and their users, a critical challenge to the free exchange of information online. Here we introduce a general, unsupervised network-based methodology to uncover groups of accounts that are likely coordinated. The proposed method constructs coordination networks based on arbitrary behavioral traces shared among accounts. We present five case studies of influence camp…
The Covid-19 Infodemic: Twitter versus Facebook
The global spread of the novel coronavirus is affected by the spread of related misinformation-the so-called COVID-19 Infodemic-that makes populations more vulnerable to the disease through resistance to mitigation efforts. Here, we analyze the prevalence and diffusion of links to low-credibility content about the pandemic across two major social media platforms, Twitter and Facebook. We characterize cross-platform similarities and differences in…
Political audience diversity and news reliability in algorithmic ranking
Demystifying Misconceptions in Social Bots Research
Research on social bots aims at advancing knowledge and providing solutions to one of the most debated forms of online manipulation. Yet, social bot research is plagued by widespread biases, hyped results, and misconceptions that set the stage for ambiguities, unrealistic expectations, and seemingly irreconcilable findings. Overcoming such issues is instrumental toward ensuring reliable solutions and reaffirming the validity of the scientific met…
Misinformation and Its Impacts (16 works) · Social media (16 works) · Computer Science (15 works) · Opinion Dynamics and Social Influence (10 works) · Political science (10 works) · World Wide Web (10 works) · Data science (9 works) · Complex Network Analysis Techniques (8 works) · Artificial Intelligence (6 works) · Internet privacy (6 works)