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This study investigates the concept of flexibility within League of Legends, a popular online multiplayer game, focusing on the relationship between user adaptability and team success. Utilizing a dataset encompassing players of varying skill levels and play styles, we calculate two measures of flexibility for each player: overall flexibility and temporal flexibility. Our findings suggest that the...Show More
Zero-Shot Meta-Learning for Small-Scale Data From Human Subjects
2023 IEEE 11th International Conference on Healthcare Informatics (ICHI)
Year: 2023 | Conference Paper |
While developments in machine learning led to impressive performance gains on big data, many human subjects data are, in actuality, small and sparsely labeled. Existing methods applied to such data often do not easily generalize to out-of-sample subjects. Instead, models must make predictions on test data that may be drawn from a different distribution, a problem known as zero-shot learning. To ad...Show More
Tensor Embedding: A Supervised Framework for Human Behavioral Data Mining and Prediction
Homa Hosseinmardi;Amir Ghasemian;Kristina Lerman;Shrikanth Narayanan;Emilio Ferrara
2023 IEEE 11th International Conference on Healthcare Informatics (ICHI)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Today’s densely instrumented world offers tremendous opportunities for continuous acquisition and analysis of multimodal sensor data, providing temporal characterization of individual behaviors. Is it possible to efficiently couple such rich sensor data with predictive modeling techniques to provide contextual and insightful assessments of individual behavior? Such data is noisy, incomplete, and c...Show More
Win prediction is crucial to understanding skill modeling, teamwork and matchmaking in esports. In this paper we propose GCN-WP, a semi-supervised win prediction model for esports based on graph convolutional networks. This model learns the structure of an esports league over the course of a season (1 year) and makes predictions on another similar league. This model integrates over 30 features abo...Show More
Pictures as a Form of Protest: A Survey and Analysis of Images Posted During the Stop Asian Hate Movement on Twitter
2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS)
Year: 2021 | Conference Paper |
Modern protests are not limited to on-the-ground operations, and the ease and speed at which users can upload images to social media platforms has enabled protests to manifest online. Previous analysis of protest imagery from social media sites categorized these images into groups including texts, screenshots, memes, and artwork. However, large-scale manual annotation to identify different types o...Show More
Mitigating the Bias of Heterogeneous Human Behavior in Affective Computing
Shen Yan;Hsien-Te Kao;Kristina Lerman;Shrikanth Narayanan;Emilio Ferrara
2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII)
Year: 2021 | Conference Paper |
Cited by: Papers (4)
Affective computing is broadly applied to decision making systems ranging from mental health assessment to employability evaluation. The heterogeneity of human behavioral data poses challenges for both model validity and fairness. The limited access to sensitive attributes (e,g., race, gender) in real-world settings makes it more difficult to mitigate the unfairness of the model outcomes. In this ...Show More
The Influence of Social Ties on Performance in Team-Based Online Games
IEEE Transactions on Games
Cited by: Papers (6)
Social ties are the invisible glue that keeps together human ecosystems. Despite the massive amount of research studying the role of social ties in communities (groups, teams, etc.) and society at large, little attention has been devoted to study their interplay with other human behavioral dynamics. Of particular interest is the influence that social ties have on human performance in collaborative...Show More
Individualized Context-Aware Tensor Factorization for Online Games Predictions
2020 International Conference on Data Mining Workshops (ICDMW)
Year: 2020 | Conference Paper |
Cited by: Papers (1)
Individual behavior and decisions are substantially influenced by their contexts, such as location, environment, and time. Changes along these dimensions can be readily observed in Multiplayer Online Battle Arena games (MOBA), where players face different in-game settings for each match and are subject to frequent game patches. Existing methods utilizing contextual information generalize the effec...Show More
Charting the Landscape of Online Cryptocurrency Manipulation
Leonardo Nizzoli;Serena Tardelli;Marco Avvenuti;Stefano Cresci;Maurizio Tesconi;Emilio Ferrara
Cryptocurrencies represent one of the most attractive markets for financial speculation. As a consequence, they have attracted unprecedented attention on social media. Besides genuine discussions and legitimate investment initiatives, several deceptive activities have flourished. In this work, we chart the online cryptocurrency landscape across multiple platforms. To reach our goal, we collected a...Show More
Effects of Network Structure on Subjective Preference Diversity
2019 IEEE International Conference on Big Data (Big Data)
Year: 2019 | Conference Paper |
Different social media environments enable different levels of community connectedness, which in turn affects the information that a user is exposed to. In this study, we create an agent-based model to investigate how the different levels of connectedness as well as network structure affects a group's diversity of opinions. In the model, agents are tasked with “liking” or “disliking” a set of obje...Show More
Estimating Individualized Daily Self-Reported Affect with Wearable Sensors
Shen Yan;Homa Hosseinmardi;Hsien-Te Kao;Shrikanth Narayanan;Kristina Lerman;Emilio Ferrara
2019 IEEE International Conference on Healthcare Informatics (ICHI)
Year: 2019 | Conference Paper |
Cited by: Papers (9)
Wearable sensors (smart watches, health/fitness trackers, etc.) are experiencing an explosion in popularity. Their pervasiveness allows for effective data collections to quantify human behavior in natural settings, enriching traditional behavioral science research opportunities. In this paper, we focus on the problem of affect estimation from sensor-generated data, whereas ground truth is availabl...Show More
Both offline and online human behaviors are affected by personality. Of special interests are online games, where players have to impersonate specific roles and their behaviors are extensively tracked by the game. In this paper, we propose to study the relationship between players' personality and game behavior in League of Legends (LoL), one of the most popular Multiplayer Online Battle Arena (MO...Show More
SoReC: A Social-Relation Based Centrality Measure in Mobile Social Networks
2019 26th International Conference on Telecommunications (ICT)
Year: 2019 | Conference Paper |
Cited by: Papers (2)
Mobile Social Networks (MSNs) have been evolving and enabling various fields in recent years. Recent advances in mobile edge computing, caching, and device-to-device communications, can have significant impacts on 5G systems. In those settings, identifying central users is crucial. It can provide important insights into designing and deploying diverse services and applications. However, it is chal...Show More
Discovering Latent Psychological Structures from Self-Report Assessments of Hospital Workers
Hsien-Te Kao;Homa Hosseinmardi;Shen Yan;Michelle Hasan;Shrikanth Narayanan;Kristina Lerman;Emilio Ferrara
2018 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC)
Year: 2018 | Conference Paper |
Cited by: Papers (4)
Hospitals are high-stress environments where workers face a high risk of occupational burnout due to a mix of imbalanced schedules, understaffing, and emotional stress. In this paper, we propose a computational framework to infer the latent psychological makeup and traits of hospital workers. We apply machine learning models to psychometric data obtained from a suite of psychological survey instru...Show More
Capturing Edge Attributes via Network Embedding
Network embedding, which aims to learn low-dimensional representations of nodes, has been used for various graph related tasks including visualization, link prediction, and node classification. Most existing embedding methods rely solely on network structure. However, in practice, we often have auxiliary information about the nodes and/or their interactions, e.g., the content of scientific papers ...Show More
Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign
2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Year: 2018 | Conference Paper |
Cited by: Papers (170)
Until recently, social media was seen to promote democratic discourse on social and political issues. However, this powerful communication platform has come under scrutiny for allowing hostile actors to exploit online discussions in an attempt to manipulate public opinion. A case in point is the ongoing U.S. Congress investigation of Russian interference in the 2016 U.S. election campaign, with Ru...Show More
Social Bots for Online Public Health Interventions
Ashok Deb;Anuja Majmundar;Sungyong Seo;Akira Matsui;Rajat Tandon;Shen Yan;Jon-Patrick Allem;Emilio Ferrara
2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Year: 2018 | Conference Paper |
Cited by: Papers (6)
According to the Center for Disease Control and Prevention, hundreds of thousands initiate smoking each year, and millions live with smoking-related diseases in the United States. Many tobacco users discuss their opinions, habits and preferences on social media. This work conceptualizes a framework for targeted health interventions to inform tobacco users about the consequences of tobacco use. We ...Show More
Performance Dynamics and Success in Online Games
2017 IEEE International Conference on Data Mining Workshops (ICDMW)
Year: 2017 | Conference Paper |
Cited by: Papers (6)
Online data provide a way to monitor how users behave in social systems like social networks and online games, and understand which features turn an ordinary individual into a successful one. Here, we propose to study individual performance and success in Multiplayer Online Battle Arena (MOBA) games. Our purpose is to identify those behaviors and playing styles that are characteristic of players w...Show More
Early Warnings of Cyber Threats in Online Discussions
Anna Sapienza;Alessandro Bessi;Saranya Damodaran;Paulo Shakarian;Kristina Lerman;Emilio Ferrara
2017 IEEE International Conference on Data Mining Workshops (ICDMW)
Year: 2017 | Conference Paper |
Cited by: Papers (64)
We introduce a system for automatically generating warnings of imminent or current cyber-threats. Our system leverages the communication of malicious actors on the darkweb, as well as activity of cyber security experts on social media platforms like Twitter. In a time period between September, 2016 and January, 2017, our method generated 661 alerts of which about 84% were relevant to current or im...Show More
The DARPA Twitter Bot Challenge
V.S. Subrahmanian;Amos Azaria;Skylar Durst;Vadim Kagan;Aram Galstyan;Kristina Lerman;Linhong Zhu;Emilio Ferrara;Alessandro Flammini;Filippo Menczer
From politicians and nation states to terrorist groups, numerous organizations reportedly conduct explicit campaigns to influence opinions on social media, posing a risk to freedom of expression. Thus, there is a need to identify and eliminate "influence bots" - realistic, automated identities that illicitly shape discussions on sites like Twitter and Facebook - before they get too influential.Show More
Trust Networks: Topology, Dynamics, and Measurements
Santa Agreste;Pasquale De Meo;Emilio Ferrara;Sebastiano Piccolo;Alessandro Provetti
Thanks to the availability of user profiles and records of activity, online social network analysis can discover complex individual and social behavior patterns. The emergence of trust between users of online services is one of the most important phenomena, but it's also hard to detect in records of users' interactions, and even harder to replicate by abstract, generative models. Here, the authors...Show More
Parallel Clustering of High-Dimensional Social Media Data Streams
2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
Year: 2015 | Conference Paper |
Cited by: Papers (11)
We introduce Cloud DIKW (Data, Information, Knowledge, Wisdom) as an analysis environment supporting scientific discovery through integrated parallel batch and streaming processing, and apply it to one representative domain application: social media data stream clustering. In this context, recent work demonstrated that high-quality clusters can be generated by representing the data points using hi...Show More
Analysis of a Heterogeneous Social Network of Humans and Cultural Objects
Santa Agreste;Pasquale De Meo;Emilio Ferrara;Sebastiano Piccolo;Alessandro Provetti
Modern online social platforms allow their members to be involved in a broad range of activities including getting friends, joining groups, posting, and commenting resources. In this paper, we investigate whether a correlation emerges across the different activities a user can take part in. For our analysis, we focused on aNobii, a social platform with a world-wide user base of book readers, who p...Show More
Trust and Compactness in Social Network Groups
Pasquale De Meo;Emilio Ferrara;Domenico Rosaci;Giuseppe M. L. Sarné
IEEE Transactions on Cybernetics
Cited by: Papers (66)
Understanding the dynamics behind group formation and evolution in social networks is considered an instrumental milestone to better describe how individuals gather and form communities, how they enjoy and share the platform contents, how they are driven by their preferences/tastes, and how their behaviors are influenced by peers. In this context, the notion of compactness of a social group is par...Show More
Clustering memes in social media
Emilio Ferrara;Mohsen JafariAsbagh;Onur Varol;Vahed Qazvinian;Filippo Menczer;Alessandro Flammini
2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)
Year: 2013 | Conference Paper |
The increasing pervasiveness of social media creates new opportunities to study human social behavior, while challenging our capability to analyze their massive data streams. One of the emerging tasks is to distinguish between different kinds of activities, for example engineered misinformation campaigns versus spontaneous communication. Such detection problems require a formal definition of meme,...Show More
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