diffusion modeling
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2022 ◽  
Vol 16 (1) ◽  
pp. 1-24
Author(s):  
Marinos Poiitis ◽  
Athena Vakali ◽  
Nicolas Kourtellis

Aggression in online social networks has been studied mostly from the perspective of machine learning, which detects such behavior in a static context. However, the way aggression diffuses in the network has received little attention as it embeds modeling challenges. In fact, modeling how aggression propagates from one user to another is an important research topic, since it can enable effective aggression monitoring, especially in media platforms, which up to now apply simplistic user blocking techniques. In this article, we address aggression propagation modeling and minimization in Twitter, since it is a popular microblogging platform at which aggression had several onsets. We propose various methods building on two well-known diffusion models, Independent Cascade ( IC ) and Linear Threshold ( LT ), to study the aggression evolution in the social network. We experimentally investigate how well each method can model aggression propagation using real Twitter data, while varying parameters, such as seed users selection, graph edge weighting, users’ activation timing, and so on. It is found that the best performing strategies are the ones to select seed users with a degree-based approach, weigh user edges based on their social circles’ overlaps, and activate users according to their aggression levels. We further employ the best performing models to predict which ordinary real users could become aggressive (and vice versa) in the future, and achieve up to AUC = 0.89 in this prediction task. Finally, we investigate aggression minimization by launching competitive cascades to “inform” and “heal” aggressors. We show that IC and LT models can be used in aggression minimization, providing less intrusive alternatives to the blocking techniques currently employed by Twitter.


2023 ◽  
Vol 55 (1) ◽  
pp. 1-51
Author(s):  
Huacheng Li ◽  
Chunhe Xia ◽  
Tianbo Wang ◽  
Sheng Wen ◽  
Chao Chen ◽  
...  

Studying information diffusion in SNS (Social Networks Service) has remarkable significance in both academia and industry. Theoretically, it boosts the development of other subjects such as statistics, sociology, and data mining. Practically, diffusion modeling provides fundamental support for many downstream applications (e.g., public opinion monitoring, rumor source identification, and viral marketing). Tremendous efforts have been devoted to this area to understand and quantify information diffusion dynamics. This survey investigates and summarizes the emerging distinguished works in diffusion modeling. We first put forward a unified information diffusion concept in terms of three components: information, user decision, and social vectors, followed by a detailed introduction of the methodologies for diffusion modeling. And then, a new taxonomy adopting hybrid philosophy (i.e., granularity and techniques) is proposed, and we made a series of comparative studies on elementary diffusion models under our taxonomy from the aspects of assumptions, methods, and pros and cons. We further summarized representative diffusion modeling in special scenarios and significant downstream tasks based on these elementary models. Finally, open issues in this field following the methodology of diffusion modeling are discussed.


Assessment ◽  
2022 ◽  
pp. 107319112110690
Author(s):  
Kyler Mulhauser ◽  
Bruno Giordani ◽  
Voyko Kavcic ◽  
L. D. Nicolas May ◽  
Arijit Bhaumik ◽  
...  

Cognitive testing data are essential to the diagnosis of mild cognitive impairment (MCI), and computerized cognitive testing, such as the Cogstate Brief Battery, has proven helpful in efficiently identifying harbingers of dementia. This study provides a side-by-side comparison of traditional Cogstate outcomes and diffusion modeling of these outcomes in predicting MCI diagnosis. Participants included 257 older adults (160 = normal cognition; 97 = MCI). Results showed that both traditional Cogstate and diffusion modeling analyses predicted MCI diagnosis with acceptable accuracy. Cogstate measures of recognition learning and working memory accuracy and diffusion modeling variable of decision-making efficiency (drift rate) and nondecisional time were most predictive of MCI. While participants with normal cognition demonstrated a change in response caution (boundary separation) when transitioning tasks, participants with MCI did not evidence this change.


Geology ◽  
2022 ◽  
Author(s):  
Martin F. Mangler ◽  
Chiara Maria Petrone ◽  
Julie Prytulak

Diffusion chronometry has produced petrological evidence that magma recharge in mafic to intermediate systems can trigger volcanic eruptions within weeks to months. However, less is known about longer-term recharge frequencies and durations priming magma reservoirs for eruptions. We use Fe-Mg diffusion modeling in orthopyroxene to show that the duration, frequency, and timing of pre-eruptive recharge at Popocatépetl volcano (Mexico) vary systematically with eruption style and magnitude. Effusive eruptions are preceded by 9–13 yr of increased recharge activity, compared to 15–100 yr for explosive eruptions. Explosive eruptions also record a higher number of individual recharge episodes priming the plumbing system. The largest explosive eruptions are further distinguished by an ~1 yr recharge hiatus directly prior to eruption. Our results offer valuable context for the interpretation of ongoing activity at Popocatépetl, and seeking similar correlations at other arc volcanoes may advance eruption forecasting by including constraints on potential eruption size and style.


Information ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 13
Author(s):  
Firdaniza Firdaniza ◽  
Budi Nurani Ruchjana ◽  
Diah Chaerani ◽  
Jaziar Radianti

Information diffusion, information spread, and influencers are important concepts in many studies on social media, especially Twitter analytics. However, literature overviews on the information diffusion of Twitter analytics are sparse, especially on the use of continuous time Markov chain (CTMC). This paper examines the following topics: (1) the purposes of studies about information diffusion on Twitter, (2) the methods adopted to model information diffusion on Twitter, (3) the metrics applied, and (4) measures used to determine influencer rankings. We employed a systematic literature review (SLR) to explore the studies related to information diffusion on Twitter extracted from four digital libraries. In this paper, a two-stage analysis was conducted. First, we implemented a bibliometric analysis using VOSviewer and R-bibliometrix software. This approach was applied to select 204 papers after conducting a duplication check and assessing the inclusion–exclusion criteria. At this stage, we mapped the authors’ collaborative networks/collaborators and the evolution of research themes. Second, we analyzed the gap in research themes on the application of CTMC information diffusion on Twitter. Further filtering criteria were applied, and 34 papers were analyzed to identify the research objectives, methods, metrics, and measures used by each researcher. Nonhomogeneous CTMC has never been used in Twitter information diffusion modeling. This finding motivates us to further study nonhomogeneous CTMC as a modeling approach for Twitter information diffusion.


2021 ◽  
Author(s):  
Camarin E Rolle ◽  
Mads L Pedersen ◽  
Noriah Johnson ◽  
Ken-ichi Amemori ◽  
Maria Ironside ◽  
...  

Abstract Approach–Avoidance conflict (AAC) arises from decisions with embedded positive and negative outcomes, such that approaching leads to reward and punishment and avoiding to neither. Despite its importance, the field lacks a mechanistic understanding of which regions are driving avoidance behavior during conflict. In the current task, we utilized transcranial magnetic stimulation (TMS) and drift-diffusion modeling to investigate the role of one of the most prominent regions relevant to AAC—the dorsolateral prefrontal cortex (dlPFC). The first experiment uses in-task disruption to examine the right dlPFC’s (r-dlPFC) causal role in avoidance behavior. The second uses single TMS pulses to probe the excitability of the r-dlPFC, and downstream cortical activations, during avoidance behavior. Disrupting r-dlPFC during conflict decision-making reduced reward sensitivity. Further, r-dlPFC was engaged with a network of regions within the lateral and medial prefrontal, cingulate, and temporal cortices that associate with behavior during conflict. Together, these studies use TMS to demonstrate a role for the dlPFC in reward sensitivity during conflict and elucidate the r-dlPFC’s network of cortical regions associated with avoidance behavior. By identifying r-dlPFC’s mechanistic role in AAC behavior, contextualized within its conflict-specific downstream neural connectivity, we advance dlPFC as a potential neural target for psychiatric therapeutics.


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