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

Garibay, Iván; Oghaz, Toktam A.; Yousefi, Niloofar; Mutlu, Ece C.; Schiappa, Madeline C.; Scheinert, Steven R.; Anagnostopoulos, Georgios C.; Bouwens, Christina; Fiore, Stephen M.; Mantzaris, Alexander V.; Murphy, John T.; Rand, William; Salter, Anastasia; Stanfill, Mel; Sukthankar, Gita; Baral, Nisha; Fair, Gabriel; Gunaratne, Chathika; Hajiakhoond, Neda; Jasser, Jasser; Jayalath, Chathura; Newton, Olivia B.; Saadat, Samaneh; Senevirathna, Chathurani; Winter, Rachel; Xi, Zhang

doi:10.1007/978-3-030-77517-9_11

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…The works of [17] and [18] use neural networks on sequences of adjacency matrices to predict user activity over time in Twitter. In [19] the authors use a novel method that combines social science theory with a preferential attachement model.…”

Section: Direct User-level Prediction In Social Mediamentioning

confidence: 99%

Simulating User-Level Twitter Activity with XGBoost and Probabilistic Hybrid Models

Mubang¹,

Hall²

2022

Preprint

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The Volume-Audience-Match simulator, or VAM was applied to predict future activity on Twitter related to international economic affairs. VAM was applied to do timeseries forecasting to predict the: (1) number of total activities, (2) number of active old users, and (3) number of newly active users over the span of 24 hours from the start time of prediction. VAM then used these volume predictions to perform user link predictions. A user-user edge was assigned to each of the activities in the 24 future timesteps. VAM considerably outperformed a set of baseline models in both the time series and user-assignment tasks.

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Section: Direct User-level Prediction In Social Mediamentioning

confidence: 99%

Simulating User-Level Twitter Activity with XGBoost and Probabilistic Hybrid Models

Mubang¹,

Hall²

2022

Preprint

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“…Bhattacharya et al ( 2019 ) proposed an ABM that incorporates social and cognitive agents that model online human-decision making. Garibay et al ( 2020 ) proposed DeepAgent that can simulate social dynamics in a multi-resolution setting at the user, community, population, and content levels. Murić et al ( 2022 ) developed an ABM that provides support for simulations of cognitive behavior and shared state across multiple compute nodes.…”

Section: Introductionmentioning

confidence: 99%

Modeling information diffusion in social media: data-driven observations

et al. 2023

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Accurately modeling information diffusion within and across social media platforms has many practical applications, such as estimating the size of the audience exposed to a particular narrative or testing intervention techniques for addressing misinformation. However, it turns out that real data reveal phenomena that pose significant challenges to modeling: events in the physical world affect in varying ways conversations on different social media platforms; coordinated influence campaigns may swing discussions in unexpected directions; a platform's algorithms direct who sees which message, which affects in opaque ways how information spreads. This article describes our research efforts in the SocialSim program of the Defense Advanced Research Projects Agency. As formulated by DARPA, the intent of the SocialSim research program was “to develop innovative technologies for high-fidelity computational simulation of online social behavior ... [focused] specifically on information spread and evolution.” In this article we document lessons we learned over the 4+ years of the recently concluded project. Our hope is that an accounting of our experience may prove useful to other researchers should they attempt a related project.

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“…In the literature, graph networks modeling social media most commonly apply the user-as-node [56][57][58], and community-as-node [59][60][61][62] representations. There, connections (edges) are built on the basis of interactions (users commenting/talking) or common belonging (e.g., users subscribing to the same groups/subreddits).…”

mentioning

confidence: 99%

Applying Named Entity Recognition and Graph Networks to Extract Common Interests from Thematic Subfora on Reddit

Sawicki,

Ganzha,

Paprzycki

et al. 2024

Applied Sciences

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Reddit is the largest topically structured social network. Existing literature, reporting results of Reddit-related research, considers different phenomena, from social and political studies to recommender systems. The most common techniques used in these works, include natural language processing, e.g., named entity recognition, as well as graph networks representing online social networks. However, large-scale studies that take into account Reddit’s unique structure are scarce. In this contribution, similarity between subreddits is explored. Specifically, subreddit posts (from 3189 subreddits, spanning the year 2022) are processed using NER to build graph networks which are further mined for relations between subreddits. The evaluation of obtained results follows the state-of-the-art approaches used for a similar problem, i.e., recommender system metrics, and applies recall and AUC. Overall, the use of Reddit crossposts discloses previously unknown relations between subreddits. Interestingly, the proposed approach may allow for researchers to better connect their study topics with particular subreddits and shows promise for subreddit similarity mining.

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

Cited by 6 publications

References 14 publications

Simulating User-Level Twitter Activity with XGBoost and Probabilistic Hybrid Models

Simulating User-Level Twitter Activity with XGBoost and Probabilistic Hybrid Models

Modeling information diffusion in social media: data-driven observations

Applying Named Entity Recognition and Graph Networks to Extract Common Interests from Thematic Subfora on Reddit

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