arXiv subjects

Ivan Garibay

Publications and source records attributed to Ivan Garibay.

At least 37 records · Page 2Linked to original sources

arXiv2024

Controlling the Misinformation Diffusion in Social Media by the Effect of Different Classes of Agents

The rapid and widespread dissemination of misinformation through social networks is a growing concern in today's digital age. This study focused on modeling fake news diffusion, discovering the spreading dynamics, and designing control strategies. A common approach for modeling the misinformation dynamics is SIR-based models. Our approach is an extension of a model called 'SBFC' which is a SIR-based model. This model has three states, Susceptible, Believer, and Fact-Checker. The dynamics and transition between states are based on neighbors' beliefs, hoax credibility, spreading rate, probability of verifying the news, and probability of forgetting the current state. Our contribution is to push this model to real social networks by considering different classes of agents with their characteristics. We proposed two main strategies for confronting misinformation diffusion. First, we can educate a minor class, like scholars or influencers, to improve their ability to verify the news or remember their state longer. The second strategy is adding fact-checker bots to the network to spread the facts and influence their neighbors' states. Our result shows that both of these approaches can effectively control the misinformation spread.

cs.MA↗

arXiv2022

Resilience from Diversity: Population-based approach to harden models against adversarial attacks

Traditional deep learning networks (DNN) exhibit intriguing vulnerabilities that allow an attacker to force them to fail at their task. Notorious attacks such as the Fast Gradient Sign Method (FGSM) and the more powerful Projected Gradient Descent (PGD) generate adversarial samples by adding a magnitude of perturbation $ε$ to the input's computed gradient, resulting in a deterioration of the effectiveness of the model's classification. This work introduces a model that is resilient to adversarial attacks. Our model leverages an established mechanism of defense which utilizes randomness and a population of DNNs. More precisely, our model consists of a population of $n$ diverse submodels, each one of them trained to individually obtain a high accuracy for the task at hand, while forced to maintain meaningful differences in their weights. Each time our model receives a classification query, it selects a submodel from its population at random to answer the query. To counter the attack transferability, diversity is introduced and maintained in the population of submodels. Thus introducing the concept of counter linking weights. A Counter-Linked Model (CLM) consists of a population of DNNs of the same architecture where a periodic random similarity examination is conducted during the simultaneous training to guarantee diversity while maintaining accuracy. Though the randomization technique proved to be resilient against adversarial attacks, we show that by retraining the DNNs ensemble or training them from the start with counter linking would enhance the robustness by around 20\% when tested on the MNIST dataset and at least 15\% when tested on the CIFAR-10 dataset. When CLM is coupled with adversarial training, this defense mechanism achieves state-of-the-art robustness.

cs.LG↗

arXiv2022

Dominant Set-based Active Learning for Text Classification and its Application to Online Social Media

Recent advances in natural language processing (NLP) in online social media are evidently owed to large-scale datasets. However, labeling, storing, and processing a large number of textual data points, e.g., tweets, has remained challenging. On top of that, in applications such as hate speech detection, labeling a sufficiently large dataset containing offensive content can be mentally and emotionally taxing for human annotators. Thus, NLP methods that can make the best use of significantly less labeled data points are of great interest. In this paper, we present a novel pool-based active learning method that can be used for the training of large unlabeled corpus with minimum annotation cost. For that, we propose to find the dominant sets of local clusters in the feature space. These sets represent maximally cohesive structures in the data. Then, the samples that do not belong to any of the dominant sets are selected to be used to train the model, as they represent the boundaries of the local clusters and are more challenging to classify. Our proposed method does not have any parameters to be tuned, making it dataset-independent, and it can approximately achieve the same classification accuracy as full training data, with significantly fewer data points. Additionally, our method achieves a higher performance in comparison to the state-of-the-art active learning strategies. Furthermore, our proposed algorithm is able to incorporate conventional active learning scores, such as uncertainty-based scores, into its selection criteria. We show the effectiveness of our method on different datasets and using different neural network architectures.

cs.CL↗

arXiv2021

NL4Py: Agent-Based Modeling in Python with Parallelizable NetLogo Workspaces

External control of agent-based models is vital for complex adaptive systems research. Often these experiments require vast numbers of simulation runs and are computationally expensive. NetLogo is the language of choice for most agent-based modelers but lacks direct API access through Python. NL4Py is a Python package for the parallel execution of NetLogo simulations via Python, designed for speed, scalability, and simplicity of use. NL4Py provides access to the large number of open-source machine learning and analytics libraries of Python and enables convenient and efficient parallelization of NetLogo simulations with minimal coding expertise by domain scientists.

cs.MA↗

arXiv2021

Ethical AI for Social Good

The concept of AI for Social Good(AI4SG) is gaining momentum in both information societies and the AI community. Through all the advancement of AI-based solutions, it can solve societal issues effectively. To date, however, there is only a rudimentary grasp of what constitutes AI socially beneficial in principle, what constitutes AI4SG in reality, and what are the policies and regulations needed to ensure it. This paper fills the vacuum by addressing the ethical aspects that are critical for future AI4SG efforts. Some of these characteristics are new to AI, while others have greater importance due to its usage.

cs.CY↗

arXiv2021

Audit and Assurance of AI Algorithms: A framework to ensure ethical algorithmic practices in Artificial Intelligence

Algorithms are becoming more widely used in business, and businesses are becoming increasingly concerned that their algorithms will cause significant reputational or financial damage. We should emphasize that any of these damages stem from situations in which the United States lacks strict legislative prohibitions or specified protocols for measuring damages. As a result, governments are enacting legislation and enforcing prohibitions, regulators are fining businesses, and the judiciary is debating whether or not to make artificially intelligent computer models as the decision-makers in the eyes of the law. From autonomous vehicles and banking to medical care, housing, and legal decisions, there will soon be enormous amounts of algorithms that make decisions with limited human interference. Governments, businesses, and society would have an algorithm audit, which would have systematic verification that algorithms are lawful, ethical, and secure, similar to financial audits. A modern market, auditing, and assurance of algorithms developed to professionalize and industrialize AI, machine learning, and related algorithms. Stakeholders of this emerging field include policymakers and regulators, along with industry experts and entrepreneurs. In addition, we foresee audit thresholds and frameworks providing valuable information to all who are concerned with governance and standardization. This paper aims to review the critical areas required for auditing and assurance and spark discussion in this novel field of study and practice.

cs.CY↗

arXiv2021

Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks

This paper explains the design of a social network analysis framework, developed under DARPA's SocialSim program, with novel architecture that models human emotional, cognitive and social factors. Our framework is both theory and data-driven, and utilizes domain expertise. Our simulation effort helps in understanding how information flows and evolves in social media platforms. We focused on modeling three information domains: cryptocurrencies, cyber threats, and software vulnerabilities for the three interrelated social environments: GitHub, Reddit, and Twitter. We participated in the SocialSim DARPA Challenge in December 2018, in which our models were subjected to extensive performance evaluation for accuracy, generalizability, explainability, and experimental power. This paper reports the main concepts and models, utilized in our social media modeling effort in developing a multi-resolution simulation at the user, community, population, and content levels.

cs.SI↗

arXiv2021

Interpretable Multi-Head Self-Attention model for Sarcasm Detection in social media

Sarcasm is a linguistic expression often used to communicate the opposite of what is said, usually something that is very unpleasant with an intention to insult or ridicule. Inherent ambiguity in sarcastic expressions, make sarcasm detection very difficult. In this work, we focus on detecting sarcasm in textual conversations from various social networking platforms and online media. To this end, we develop an interpretable deep learning model using multi-head self-attention and gated recurrent units. Multi-head self-attention module aids in identifying crucial sarcastic cue-words from the input, and the recurrent units learn long-range dependencies between these cue-words to better classify the input text. We show the effectiveness of our approach by achieving state-of-the-art results on multiple datasets from social networking platforms and online media. Models trained using our proposed approach are easily interpretable and enable identifying sarcastic cues in the input text which contribute to the final classification score. We visualize the learned attention weights on few sample input texts to showcase the effectiveness and interpretability of our model.

cs.CL↗

arXiv2020

Review on Learning and Extracting Graph Features for Link Prediction

Link prediction in complex networks has attracted considerable attention from interdisciplinary research communities, due to its ubiquitous applications in biological networks, social networks, transportation networks, telecommunication networks, and, recently, knowledge graphs. Numerous studies utilized link prediction approaches in order sto find missing links or predict the likelihood of future links as well as employed for reconstruction networks, recommender systems, privacy control, etc. This work presents an extensive review of state-of-art methods and algorithms proposed on this subject and categorizes them into four main categories: similarity-based methods, probabilistic methods, relational models, and learning-based methods. Additionally, a collection of network data sets has been presented in this paper, which can be used in order to study link prediction. We conclude this study with a discussion of recent developments and future research directions.

cs.SI↗

arXiv2020

Facial Expression Phoenix (FePh): An Annotated Sequenced Dataset for Facial and Emotion-Specified Expressions in Sign Language

Facial expressions are important parts of both gesture and sign language recognition systems. Despite the recent advances in both fields, annotated facial expression datasets in the context of sign language are still scarce resources. In this manuscript, we introduce an annotated sequenced facial expression dataset in the context of sign language, comprising over $3000$ facial images extracted from the daily news and weather forecast of the public tv-station PHOENIX. Unlike the majority of currently existing facial expression datasets, FePh provides sequenced semi-blurry facial images with different head poses, orientations, and movements. In addition, in the majority of images, identities are mouthing the words, which makes the data more challenging. To annotate this dataset we consider primary, secondary, and tertiary dyads of seven basic emotions of "sad", "surprise", "fear", "angry", "neutral", "disgust", and "happy". We also considered the "None" class if the image's facial expression could not be described by any of the aforementioned emotions. Although we provide FePh as a facial expression dataset of signers in sign language, it has a wider application in gesture recognition and Human Computer Interaction (HCI) systems.

cs.CV↗

arXiv2020

A Stance Data Set on Polarized Conversations on Twitter about the Efficacy of Hydroxychloroquine as a Treatment for COVID-19

At the time of this study, the SARS-CoV-2 virus that caused the COVID-19 pandemic has spread significantly across the world. Considering the uncertainty about policies, health risks, financial difficulties, etc. the online media, specially the Twitter platform, is experiencing a high volume of activity related to this pandemic. Among the hot topics, the polarized debates about unconfirmed medicines for the treatment and prevention of the disease have attracted significant attention from online media users. In this work, we present a stance data set, COVID-CQ, of user-generated content on Twitter in the context of COVID-19. We investigated more than 14 thousand tweets and manually annotated the opinions of the tweet initiators regarding the use of "chloroquine" and "hydroxychloroquine" for the treatment or prevention of COVID-19. To the best of our knowledge, COVID-CQ is the first data set of Twitter users' stances in the context of the COVID-19 pandemic, and the largest Twitter data set on users' stances towards a claim, in any domain. We have made this data set available to the research community via GitHub. We expect this data set to be useful for many research purposes, including stance detection, evolution and dynamics of opinions regarding this outbreak, and changes in opinions in response to the exogenous shocks such as policy decisions and events.

cs.SI↗

arXiv2020

CD-SEIZ: Cognition-Driven SEIZ Compartmental Model for the Prediction of Information Cascades on Twitter

Information spreading social media platforms has become ubiquitous in our lives due to viral information propagation regardless of its veracity. Some information cascades turn out to be viral since they circulated rapidly on the Internet. The uncontrollable virality of manipulated or disorientated true information (fake news) might be quite harmful, while the spread of the true news is advantageous, especially in emergencies. We tackle the problem of predicting information cascades by presenting a novel variant of SEIZ (Susceptible/ Exposed/ Infected/ Skeptics) model that outperforms the original version by taking into account the cognitive processing depth of users. We define an information cascade as the set of social media users' reactions to the original content which requires at least minimal physical and cognitive effort; therefore, we considered retweet/ reply/ quote (mention) activities and tested our framework on the Syrian White Helmets Twitter data set from April 1st, 2018 to April 30th, 2019. In the prediction of cascade pattern via traditional compartmental models, all the activities are grouped, and their summation is taken into account; however, transition rates between compartments should vary according to the activity type since their requirements of physical and cognitive efforts are not same. Based on this assumption, we design a cognition-driven SEIZ (CD-SEIZ) model in the prediction of information cascades on Twitter. We tested SIS, SEIZ, and CD-SEIZ models on 1000 Twitter cascades and found that CD-SEIZ has a significantly low fitting error and provides a statistically more accurate estimation.

cs.SI↗

arXiv2020

Controversial information spreads faster and further in Reddit

Online users discuss and converse about all sorts of topics on social networks. Facebook, Twitter, Reddit are among many other networks where users can have this freedom of information sharing. The abundance of information shared over these networks makes them an attractive area for investigating all aspects of human behavior on information dissemination. Among the many interesting behaviors, controversiality within social cascades is of high interest to us. It is known that controversiality is bound to happen within online discussions. The online social network platform Reddit has the feature to tag comments as controversial if the users have mixed opinions about that comment. The difference between this study and previous attempts at understanding controversiality on social networks is that we do not investigate topics that are known to be controversial. On the contrary, we examine typical cascades with comments that the readers deemed to be controversial concerning the matter discussed. This work asks whether controversially initiated information cascades have distinctive characteristics than those not controversial in Reddit. We used data collected from Reddit consisting of around 17 million posts and their corresponding comments related to cybersecurity issues to answer these emerging questions. From the comparative analyses conducted, controversial content travels faster and further from its origin. Understanding this phenomenon would shed light on how users or organization might use it to their help in controlling and spreading a specific beneficiary message.

cs.SI↗

arXiv2020

Effects of Non-Cognitive Factors on Post-Secondary Persistence of Deaf Students: An Agent-Based Modeling Approach

Post-secondary education persistence is the likelihood of a student remaining in post-secondary education. Although statistics show that post-secondary persistence for deaf students has increased recently, there are still many obstacles obstructing students from completing their post-secondary degree goals. Therefore, increasing the persistence rate is crucial to increase education and work goals for deaf students. In this work, we present an agent-based model using NetLogo software for the persistence phenomena of deaf students. We consider four non-cognitive factors: having clear goals, social integration, social skills, and academic experience, which influence the departure decision of deaf students. Progress and results of this work suggest that agent-based modeling approaches promise to give better understanding of what will increase persistence.

cs.CY↗

arXiv2020

Effects of Assortativity on Consensus Formation with Heterogeneous Agents

Despite the widespread use of Barabasi's scale-free networks and Erdos-Renyi networks of which degree correlation (assortativity) is neutral, numerous studies demonstrated that online social networks tend to show assortative mixing (positive degree correlation), while non-social networks show a disassortative mixing (negative degree correlation). First, we analyzed the variability in the assortativity coefficients of different groups of the same platform by using three different subreddits in Reddit. Our data analysis results showed that Reddit is disassortative, and assortativity coefficients of the aforementioned subreddits are computed as -0.0384, -0.0588 and -0.1107, respectively. Motivated by the variability in the results even in the same platform, we decided to investigate the sensitivity of dynamics of consensus formation to the assortativity of the network. We concluded that the system is more likely to reach a consensus when the network is disassortatively mixed or neutral; however, the likelihood of the consensus significantly decreases when the network is assortatively mixed. Surprisingly, the time elapsed until all nodes fix their opinions is slightly lower when the network is neutral compared to either assortative or disassortative networks. These results are more pronounced when the thresholds of agents are more heterogeneously distributed.

cs.SI↗

arXiv2020

Quantifying Latent Moral Foundations in Twitter Narratives: The Case of the Syrian White Helmets Misinformation

For years, many studies employed sentiment analysis to understand the reasoning behind people's choices and feelings, their communication styles, and the communities which they belong to. We argue that gaining more in-depth insight into moral dimensions coupled with sentiment analysis can potentially provide superior results. Understanding moral foundations can yield powerful results in terms of perceiving the intended meaning of the text data, as the concept of morality provides additional information on the unobservable characteristics of information processing and non-conscious cognitive processes. Therefore, we studied latent moral loadings of Syrian White Helmets-related tweets of Twitter users from April 1st, 2018 to April 30th, 2019. For the operationalization and quantification of moral rhetoric in tweets, we use Extended Moral Foundations Dictionary in which five psychological dimensions (Harm/Care, Fairness/Reciprocity, In-group/Loyalty, Authority/Respect and Purity/Sanctity) are considered. We show that people tend to share more tweets involving the virtue moral rhetoric than the tweets involving the vice rhetoric. We observe that the pattern of the moral rhetoric of tweets among these five dimensions are very similar during different time periods, while the strength of the five dimension is time-variant. Even though there is no significant difference between the use of Fairness/Reciprocity, In-group/Loyalty or Purity/Sanctity rhetoric, the less use of Harm/Care rhetoric is significant and remarkable. Besides, the strength of the moral rhetoric and the polarization in morality across people are mostly observed in tweets involving Harm/Care rhetoric despite the number of tweets involving the Harm/Care dimension is low.

cs.SI↗

arXiv2020

Growing green: the role of path dependency and structural jumps in the green economy expansion

Existing research argues that countries increase their production basket by adding products which require similar capabilities to those they already produce, a process referred to as path dependency. Green economic growth is a global movement that seeks to achieve economic expansion while at the same time mitigating environmental risks. We postulate that countries engaging in green economic growth are motivated to invest strategically to develop new capabilities that will help them transition to a green economy. As a result, they could potentially increase their production baskets not only by a path dependent process but also by the non path dependent process we term, high investment structural jumps. The main objective of this research is to determine whether countries increase their green production basket mainly by a process of path dependency, or alternatively, by a process of structural jumps. We analyze data from 65 countries and over a period from years 2007 to 2017. We focus on China as our main case study. The results of this research show that countries not only increase their green production baskets based on their available capabilities, following path dependency, but also expand to products that path dependency does not predict by investing in innovating and developing new environmental related technologies.

econ.GN↗

arXiv2020

Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model

Online social media platforms are turning into the prime source of news and narratives about worldwide events. However,a systematic summarization-based narrative extraction that can facilitate communicating the main underlying events is lacking. To address this issue, we propose a novel event-based narrative summary extraction framework. Our proposed framework is designed as a probabilistic topic model, with categorical time distribution, followed by extractive text summarization. Our topic model identifies topics' recurrence over time with a varying time resolution. This framework not only captures the topic distributions from the data, but also approximates the user activity fluctuations over time. Furthermore, we define significance-dispersity trade-off (SDT) as a comparison measure to identify the topic with the highest lifetime attractiveness in a timestamped corpus. We evaluate our model on a large corpus of Twitter data, including more than one million tweets in the domain of the disinformation campaigns conducted against the White Helmets of Syria. Our results indicate that the proposed framework is effective in identifying topical trends, as well as extracting narrative summaries from text corpus with timestamped data.

cs.SI↗