no code implementations • 6 May 2024 • Keith Burghardt, Kai Chen, Kristina Lerman
Adversarial information operations can destabilize societies by undermining fair elections, manipulating public opinions on policies, and promoting scams.
no code implementations • 25 Mar 2024 • Georgios Chochlakis, Alexandros Potamianos, Kristina Lerman, Shrikanth Narayanan
The promise of ICL is that the LLM can adapt to perform the present task at a competitive or state-of-the-art level at a fraction of the cost.
1 code implementation • 6 Mar 2024 • Abhishek Anand, Negar Mokhberian, Prathyusha Naresh Kumar, Anweasha Saha, Zihao He, Ashwin Rao, Fred Morstatter, Kristina Lerman
Researchers have raised awareness about the harms of aggregating labels especially in subjective tasks that naturally contain disagreements among human annotators.
1 code implementation • 18 Feb 2024 • Kai Chen, Zihao He, Jun Yan, Taiwei Shi, Kristina Lerman
Large Language Models (LLMs) possess the potential to exert substantial influence on public perceptions and interactions with information.
no code implementations • 16 Feb 2024 • Zihao He, Siyi Guo, Ashwin Rao, Kristina Lerman
We define the problem of affective alignment, which measures how LMs' emotional and moral tone represents those of different groups.
1 code implementation • 2 Feb 2024 • Zihao He, Ashwin Rao, Siyi Guo, Negar Mokhberian, Kristina Lerman
Recent advances in NLP have improved our ability to understand the nuanced worldviews of online communities.
no code implementations • 17 Jan 2024 • Minh Duc Chu, Aryan Karnati, Zihao He, Kristina Lerman
We argue that social media platforms create a feedback loop that amplifies the growth of content and communities that promote eating disorders like anorexia and bulimia.
no code implementations • 16 Nov 2023 • Zihao He, Siyi Guo, Ashwin Rao, Kristina Lerman
Social media platforms are rife with politically charged discussions.
no code implementations • 16 Nov 2023 • Negar Mokhberian, Myrl G. Marmarelis, Frederic R. Hopp, Valerio Basile, Fred Morstatter, Kristina Lerman
Previous studies have shed light on the pitfalls of label aggregation and have introduced a handful of practical approaches to tackle this issue.
1 code implementation • 17 Jul 2023 • Wanying Zhao, Fiona Guo, Kristina Lerman, Yong-Yeol Ahn
Narrative is a foundation of human cognition and decision making.
1 code implementation • 16 May 2023 • Zihao He, Jonathan May, Kristina Lerman
Detecting norm violations in online communities is critical to maintaining healthy and safe spaces for online discussions.
no code implementations • 4 Apr 2023 • Siyi Guo, Negar Mokhberian, Kristina Lerman
Language models can be trained to recognize the moral sentiment of text, creating new opportunities to study the role of morality in human life.
no code implementations • 16 Jan 2023 • Christopher Tran, Keith Burghardt, Kristina Lerman, Elena Zheleva
In this work, we provide a survey of state-of-the-art data-driven methods for heterogeneous treatment effect estimation using machine learning, broadly categorizing them as methods that focus on counterfactual prediction and methods that directly estimate the causal effect.
1 code implementation • 21 Dec 2022 • Zihao He, Weituo Hao, Wei-Tsung Lu, Changyou Chen, Kristina Lerman, Xuchen Song
Music captioning has gained significant attention in the wake of the rising prominence of streaming media platforms.
no code implementations • 1 Dec 2022 • Kai Chen, Zihao He, Rong-Ching Chang, Jonathan May, Kristina Lerman
We collect discussions from a wide variety of topical forums and use emotion detection to recognize a range of emotions from text, including anger, fear, joy, admiration, etc.
1 code implementation • 31 Oct 2022 • Georgios Chochlakis, Gireesh Mahajan, Sabyasachee Baruah, Keith Burghardt, Kristina Lerman, Shrikanth Narayanan
In this work, we study how we can build a single model that can transition between these different configurations by leveraging multilingual models and Demux, a transformer-based model whose input includes the emotions of interest, enabling us to dynamically change the emotions predicted by the model.
1 code implementation • 28 Oct 2022 • Georgios Chochlakis, Gireesh Mahajan, Sabyasachee Baruah, Keith Burghardt, Kristina Lerman, Shrikanth Narayanan
First, we develop two modeling approaches to the problem in order to capture word associations of the emotion words themselves, by either including the emotions in the input, or by leveraging Masked Language Modeling (MLM).
no code implementations • 13 Oct 2022 • Negar Mokhberian, Frederic R. Hopp, Bahareh Harandizadeh, Fred Morstatter, Kristina Lerman
Morality classification relies on human annotators to label the moral expressions in text, which provides training data to achieve state-of-the-art performance.
2 code implementations • WASSA (ACL) 2022 • Zihao He, Negar Mokhberian, Kristina Lerman
Stance detection infers a text author's attitude towards a target.
no code implementations • 29 Mar 2022 • Julie Jiang, Kristina Lerman, Emilio Ferrara
While developments in machine learning led to impressive performance gains on big data, many human subjects data are, in actuality, small and sparsely labeled.
1 code implementation • 18 Jan 2022 • Keith Burghardt, Kristina Lerman
In this work, we explore algorithmic confounding in collaborative filtering-based recommendation algorithms through teacher-student learning simulations.
no code implementations • 27 Oct 2021 • Yuzi He, Christopher Tran, Julie Jiang, Keith Burghardt, Emilio Ferrara, Elena Zheleva, Kristina Lerman
The popularity of online gaming has grown dramatically, driven in part by streaming and the billion-dollar e-sports industry.
1 code implementation • Findings (EMNLP) 2021 • Zihao He, Leili Tavabi, Kristina Lerman, Mohammad Soleymani
Dialogue Act (DA) classification is the task of classifying utterances with respect to the function they serve in a dialogue.
Ranked #2 on Dialogue Act Classification on Switchboard corpus
1 code implementation • 31 Aug 2021 • Nazanin Alipourfard, Keith Burghardt, Kristina Lerman
Quantitative analysis of large-scale data is often complicated by the presence of diverse subgroups, which reduce the accuracy of inferences they make on held-out data.
no code implementations • 30 May 2021 • Nazgol Tavabi, Kristina Lerman
The growing popularity of wearable sensors has generated large quantities of temporal physiological and activity data.
1 code implementation • Findings (EMNLP) 2021 • Zihao He, Negar Mokhberian, Antonio Camara, Andres Abeliuk, Kristina Lerman
We apply our method to a dataset of news articles about the COVID-19 pandemic.
no code implementations • 22 Feb 2021 • Julie Jiang, Kristina Lerman, Emilio Ferrara
Individual behavior and decisions are substantially influenced by their contexts, such as location, environment, and time.
no code implementations • 30 Oct 2020 • Yuzi He, Keith Burghardt, Siyi Guo, Kristina Lerman
Explicit and implicit bias clouds human judgement, leading to discriminatory treatment of minority groups.
no code implementations • 23 Oct 2020 • Keith Burghardt, Tad Hogg, Raissa M. D'Souza, Kristina Lerman, Marton Posfai
We use this data to construct a model that quantifies how judgement heuristics and option quality combine when deciding between two options.
Social and Information Networks Human-Computer Interaction
no code implementations • 6 Apr 2020 • Nazgol Tavabi, Andrés Abeliuk, Negar Mokhberian, Jeremy Abramson, Kristina Lerman
As we show in this paper, the process of filtering reduces the predictability of cyber-attacks.
no code implementations • 18 Mar 2020 • Karel Mundnich, Brandon M. Booth, Michelle L'Hommedieu, Tiantian Feng, Benjamin Girault, Justin L'Hommedieu, Mackenzie Wildman, Sophia Skaaden, Amrutha Nadarajan, Jennifer L. Villatte, Tiago H. Falk, Kristina Lerman, Emilio Ferrara, Shrikanth Narayanan
We designed the study to investigate the use of off-the-shelf wearable and environmental sensors to understand individual-specific constructs such as job performance, interpersonal interaction, and well-being of hospital workers over time in their natural day-to-day job settings.
7 code implementations • 16 Mar 2020 • Emily Chen, Kristina Lerman, Emilio Ferrara
At the time of this writing, the novel coronavirus (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources and economies around the world.
Social and Information Networks Populations and Evolution
no code implementations • 16 Nov 2019 • Nazgol Tavabi, Homa Hosseinmardi, Jennifer L. Villatte, Andrés Abeliuk, Shrikanth Narayanan, Emilio Ferrara, Kristina Lerman
Continuous collection of physiological data from wearable sensors enables temporal characterization of individual behaviors.
1 code implementation • 28 Oct 2019 • Yuzi He, Keith Burghardt, Kristina Lerman
To reduce human error and prejudice, many high-stakes decisions have been turned over to machine algorithms.
1 code implementation • 9 Sep 2019 • Sandeep Soni, Kristina Lerman, Jacob Eisenstein
However, simply knowing that a word has changed in meaning is insufficient to identify the instances of word usage that convey the historical or the newer meaning.
2 code implementations • 23 Aug 2019 • Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, Aram Galstyan
With the commercialization of these systems, researchers are becoming aware of the biases that these applications can contain and have attempted to address them.
no code implementations • 21 May 2019 • Homa Hosseinmardi, Hsien-Te Kao, Kristina Lerman, Emilio Ferrara
In recent years, the rapid growth in technology has increased the opportunity for longitudinal human behavioral studies.
1 code implementation • 13 May 2019 • Nazanin Alipourfard, Buddhika Nettasinghe, Andres Abeliuk, Vikram Krishnamurthy, Kristina Lerman
For example, in an online network of a social media platform, the number of people who mention a topic in their posts---i. e., its global popularity---can be dramatically different from how people see it in their social feeds---i. e., its perceived popularity---where the feeds aggregate their friends' posts.
Social and Information Networks Physics and Society
3 code implementations • 30 Apr 2019 • Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan
Existing popular methods for semi-supervised learning with Graph Neural Networks (such as the Graph Convolutional Network) provably cannot learn a general class of neighborhood mixing relationships.
no code implementations • 1 Mar 2019 • Nazgol Tavabi, Nathan Bartley, Andrés Abeliuk, Sandeep Soni, Emilio Ferrara, Kristina Lerman
The deep and darkweb (d2web) refers to limited access web sites that require registration, authentication, or more complex encryption protocols to access them.
no code implementations • 31 Aug 2018 • Homa Hosseinmardi, Amir Ghasemian, Shrikanth Narayanan, Kristina Lerman, Emilio Ferrara
Today's densely instrumented world offers tremendous opportunities for continuous acquisition and analysis of multimodal sensor data providing temporal characterization of an individual's behaviors.
no code implementations • 8 Jun 2018 • Palash Goyal, KSM Tozammel Hossain, Ashok Deb, Nazgol Tavabi, Nathan Bartley, Andr'es Abeliuk, Emilio Ferrara, Kristina Lerman
Cyber attacks are growing in frequency and severity.
1 code implementation • 8 May 2018 • Nazanin Alipourfard, Peter G. Fennell, Kristina Lerman
We describe a data-driven discovery method that leverages Simpson's paradox to uncover interesting patterns in behavioral data.
no code implementations • 14 Apr 2018 • Ashok Deb, Kristina Lerman, Emilio Ferrara
We propose a novel approach to predict cyber events using sentiment analysis.
2 code implementations • 13 Jan 2018 • Nazanin Alipourfard, Peter G. Fennell, Kristina Lerman
We present a statistical method to automatically identify Simpson's paradox in data by comparing statistical trends in the aggregate data to those in the disaggregated subgroups.
Computers and Society
no code implementations • 7 Sep 2017 • Hao Wu, Kristina Lerman
We propose a neural embedding algorithm called Network Vector, which learns distributed representations of nodes and the entire networks simultaneously.
no code implementations • 24 Feb 2016 • Keith Burghardt, Emanuel F. Alsina, Michelle Girvan, William Rand, Kristina Lerman
Our results suggest that, rather than evaluate all available answers to a question, users rely on simple cognitive heuristics to choose an answer to vote for or accept.
no code implementations • 20 Jan 2016 • V. S. Subrahmanian, Amos Azaria, Skylar Durst, Vadim Kagan, Aram Galstyan, Kristina Lerman, Linhong Zhu, Emilio Ferrara, Alessandro Flammini, Filippo Menczer, Andrew Stevens, Alexander Dekhtyar, Shuyang Gao, Tad Hogg, Farshad Kooti, Yan Liu, Onur Varol, Prashant Shiralkar, Vinod Vydiswaran, Qiaozhu Mei, Tim Hwang
A number of organizations ranging from terrorist groups such as ISIS to politicians and nation states reportedly conduct explicit campaigns to influence opinion on social media, posing a risk to democratic processes.
no code implementations • 15 Dec 2015 • Farshad Kooti, Kristina Lerman, Luca Maria Aiello, Mihajlo Grbovic, Nemanja Djuric, Vladan Radosavljevic
Linking online shopping to income, we find that shoppers from more affluent areas purchase more expensive items and buy them more frequently, resulting in significantly more money spent on online purchases.
Social and Information Networks Computers and Society
no code implementations • 24 Feb 2014 • Linhong Zhu, Aram Galstyan, James Cheng, Kristina Lerman
We further investigate the evolution of user-level sentiments and latent feature vectors in an online framework and devise an efficient online algorithm to sequentially update the clustering of tweets, users and features with newly arrived data.
no code implementations • 11 Mar 2013 • Laura M. Smith, Kristina Lerman, Cristina Garcia-Cardona, Allon G. Percus, Rumi Ghosh
Existing methods for spectral clustering use the eigenvalues and eigenvectors of the graph Laplacian, an operator that is closely associated with random walks on graphs.