scholarly journals Inferring mechanisms of response prioritization on social media under information overload

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chathika Gunaratne ◽  
William Rand ◽  
Ivan Garibay
Keyword(s):  
Social Media ◽  
Information Overload ◽  
User Interaction ◽  
Evolutionary Model ◽  
Online Information ◽  
Information Cascades ◽  
Agent Based Model ◽  
Agent Based ◽  
Highly Active ◽  
Online Social Media

AbstractHuman decision-making is subject to the biological limits of cognition. The fluidity of information propagation over online social media often leads users to experience information overload. This in turn affects which information received by users are processed and gain a response to, imposing constraints on volumes of, and participation in, information cascades. In this study, we investigate properties contributing to the visibility of online social media notifications by highly active users experiencing information overload via cross-platform social influence. We analyze simulations of a coupled agent-based model of information overload and the multi-action cascade model of conversation with evolutionary model discovery. Evolutionary model discovery automates mechanistic inference on agent-based models by enabling random forest importance analysis on genetically programmed agent-based model rules. The mechanisms of information overload have shown to contribute to a multitude of global properties of online information cascades. We investigate nine characteristics of online messages that may contribute to the prioritization of messages for response. Our results indicate that recency had the largest contribution to message visibility, with individuals prioritizing more recent notifications. Global popularity of the conversation originator had the second highest contribution, and reduced message visibility. Messages that presented opportunity for novel user interaction, yet high reciprocity showed to have relatively moderate contribution to message visibility. Finally, insights from the evolutionary model discovery results helped inform response prioritization rules, which improved the robustness and accuracy of the model of information overload.

scholarly journals Agent Based Model of Anti-Vaccination Movements: Simulations and Comparison with Empirical Data

Vaccines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 809
Author(s):  
Pawel Sobkowicz ◽  
Antoni Sobkowicz
Keyword(s):  
Social Media ◽  
Critical Parameters ◽  
Rational Approach ◽  
The Internet ◽  
High Temporal Resolution ◽  
Nonlinear Feedback ◽  
Agent Based Model ◽  
Linear Processes ◽  
Agent Based ◽  
The Social

Background: A realistic description of the social processes leading to the increasing reluctance to various forms of vaccination is a very challenging task. This is due to the complexity of the psychological and social mechanisms determining the positioning of individuals and groups against vaccination and associated activities. Understanding the role played by social media and the Internet in the current spread of the anti-vaccination (AV) movement is of crucial importance. Methods: We present novel, long-term Big Data analyses of Internet activity connected with the AV movement for such different societies as the US and Poland. The datasets we analyzed cover multiyear periods preceding the COVID-19 pandemic, documenting the behavior of vaccine related Internet activity with high temporal resolution. To understand the empirical observations, in particular the mechanism driving the peaks of AV activity, we propose an Agent Based Model (ABM) of the AV movement. The model includes the interplay between multiple driving factors: contacts with medical practitioners and public vaccination campaigns, interpersonal communication, and the influence of the infosphere (social networks, WEB pages, user comments, etc.). The model takes into account the difference between the rational approach of the pro-vaccination information providers and the largely emotional appeal of anti-vaccination propaganda. Results: The datasets studied show the presence of short-lived, high intensity activity peaks, much higher than the low activity background. The peaks are seemingly random in size and time separation. Such behavior strongly suggests a nonlinear nature for the social interactions driving the AV movement instead of the slow, gradual growth typical of linear processes. The ABM simulations reproduce the observed temporal behavior of the AV interest very closely. For a range of parameters, the simulations result in a relatively small fraction of people refusing vaccination, but a slight change in critical parameters (such as willingness to post anti-vaccination information) may lead to a catastrophic breakdown of vaccination support in the model society, due to nonlinear feedback effects. The model allows the effectiveness of strategies combating the anti-vaccination movement to be studied. An increase in intensity of standard pro-vaccination communications by government agencies and medical personnel is found to have little effect. On the other hand, focused campaigns using the Internet and social media and copying the highly emotional and narrative-focused format used by the anti-vaccination activists can diminish the AV influence. Similar effects result from censoring and taking down anti-vaccination communications by social media platforms. The benefit of such tactics might, however, be offset by their social cost, for example, the increased polarization and potential to exploit it for political goals, or increased ‘persecution’ and ‘martyrdom’ tropes.

On the Interplay between Extortion and Punishment. An Agent Based Model of "Camorra"

2013 ◽  
pp. 65-77
Author(s):  
Barbara Sonzogni ◽  
Federico Cecconi ◽  
Rosaria Conte
Keyword(s):  
Social Learning ◽  
Agent Based Model ◽  
Learning Mechanisms ◽  
Italian Region ◽  
Agent Based ◽  
Preliminary Results ◽  
Highly Active

This paper presents an Agent-Based Model aimed to reproduce the demographics, economic and employment variables of a Southern Italian region (Campania) where one specific variant of Extortion Racketeering Systems (Erss), camorra, is highly active and prosperous. Preliminary results of a set of simulations show the effects of varying levels of extortion and punishment on the rates of inactivity, employment, etc. of a population of agents endowed with social learning mechanisms

scholarly journals Consumer perceptions of using social media for health purposes: Benefits and drawbacks

2018 ◽  
Vol 25 (4) ◽  
pp. 1661-1674 ◽  
Author(s):  
Arcelio Benetoli ◽  
Timothy F Chen ◽  
Parisa Aslani
Keyword(s):  
Social Media ◽  
Health Knowledge ◽  
Information Overload ◽  
Treatment Options ◽  
Online Information ◽  
Social And Emotional ◽  
Advantages And Disadvantages ◽  
Related Information ◽  
Health Related ◽  
Use Of Social Media

Consumers are increasingly using social media to interact with other consumers about health conditions and treatment options. This study aimed to investigate the advantages and disadvantages of using social media for health-related purposes from the consumers’ perspectives. Five focus groups with 36 Australian adults with a chronic condition and on medication were conducted, audio-recorded, transcribed verbatim, and thematically analysed. Consumers reported that social media was very convenient, for accessing health-related information and for peer engagement; user-friendly; improved their health knowledge; empowered them; and provided social and emotional support. The disadvantages included information overload, wasting time; negative feelings; doubts about online information credibility; and issues related to online interactions. Despite some disadvantages, health-related use of social media led consumers to feel supported, knowledgeable, and empowered. Consumers’ motivation to keep accessing social media for health-related purposes opens up avenues for the delivery of services via social media.

scholarly journals Impact of Online Information on Self-Isolation Intention During the COVID-19 Pandemic: Cross-Sectional Study

10.2196/19128 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e19128 ◽  
Author(s):  
Ali Farooq ◽  
Samuli Laato ◽  
A K M Najmul Islam
Keyword(s):  
Social Media ◽  
Online Survey ◽  
Information Overload ◽  
Information Source ◽  
Partial Least Square ◽  
Least Square ◽  
Equation Modeling ◽  
Online Information ◽  
Living Situation ◽  
The Impact

Background During the coronavirus disease (COVID-19) pandemic, governments issued movement restrictions and placed areas into quarantine to combat the spread of the disease. In addition, individuals were encouraged to adopt personal health measures such as social isolation. Information regarding the disease and recommended avoidance measures were distributed through a variety of channels including social media, news websites, and emails. Previous research suggests that the vast amount of available information can be confusing, potentially resulting in overconcern and information overload. Objective This study investigates the impact of online information on the individual-level intention to voluntarily self-isolate during the pandemic. Using the protection-motivation theory as a framework, we propose a model outlining the effects of cyberchondria and information overload on individuals’ perceptions and motivations. Methods To test the proposed model, we collected data with an online survey (N=225) and analyzed it using partial least square-structural equation modeling. The effects of social media and living situation were tested through multigroup analysis. Results Cyberchondria and information overload had a significant impact on individuals’ threat and coping perceptions, and through them on self-isolation intention. Among the appraisal constructs, perceived severity (P=.002) and self-efficacy (P=.003) positively impacted self-isolation intention, while response cost (P<.001) affected the intention negatively. Cyberchondria (P=.003) and information overload (P=.003) indirectly affected self-isolation intention through the aforementioned perceptions. Using social media as an information source increased both cyberchondria and information overload. No differences in perceptions were found between people living alone and those living with their families. Conclusions During COVID-19, frequent use of social media contributed to information overload and overconcern among individuals. To boost individuals’ motivation to adopt preventive measures such as self-isolation, actions should focus on lowering individuals’ perceived response costs in addition to informing them about the severity of the situation.

scholarly journals A new approach of ranking prevalent news topics from social media using unsupervised techniques

2019 ◽  
Vol 8 (07) ◽  
pp. 24683-24789
Author(s):  
Dr. D. Murali ◽  
Vinutha BA
Keyword(s):  
Social Media ◽  
News Media ◽  
User Interaction ◽  
Common People ◽  
New Approach ◽  
Online Social Media ◽  
The Social ◽  
User Attention ◽  
The Common ◽  
Filter Noise

The precious data from online origin has developed into a extended research. The mass media and news media provides the daily events to the common people. Huge amount of information is been achieved by an online social media suchlike Twitter, which contains more information about news-associated content. It is necessary to find a way to filter noise, for these resources to be useful and grab the content that is depend on the similarity to news media. Despite after the noise is eliminated the excessive data still remain in the data so it is essential to prioritize it for utilization. We are introducing three factors for prioritization. The unsupervised technique finds the news topics that are common in the pair of social media and news media, and then ranks them by the applicability factors such as MF, UA and UI. Initially the temporal prevalence of the appropriate topic in news media focus (MF). Secondary the temporal prevalence of the appropriate topic in social media illustrates the user attention (UA). Finally the interconnection among the social media users who specify this topic demonstrates the power of the society who is discussing; it is termed as the user interaction (UI).  

scholarly journals NAVIGATING THE POST-TRUTH ERA: TRUST, MISINFORMATION, AND CREDIBILITY ASSESSMENT ON ONLINE SOCIAL MEDIA

2019 ◽  
Vol 2019 ◽  
Author(s):  
Eleni A. Kyza ◽  
Christiana Varda
Keyword(s):  
Social Media ◽  
Online News ◽  
Online Information ◽  
Theory Approach ◽  
Credibility Assessment ◽  
Individual Interviews ◽  
Online Social Media ◽  
Phase Study ◽  
Social Media Platforms ◽  
Study Participants

As access to news is increasingly mediated through social media platforms, there are rising concerns for citizens’ ability to evaluate online information and detect potentially misleading items. While many studies have reported on how people assess the credibility of information, there are few reports on processes related to evaluating information online and people’s decision to trust and share the information with others. This paper reports on the first part of a three-phase study which aimed to gain an in-depth understanding of citizens’ practices and needs in assessing the credibility of information shared online and co-create solutions to address this problem. Data were collected from three European countries, through a survey on misinformation perceptions, focus groups, follow-up individual interviews, and co-creation activities with three stakeholder groups. The data were analyzed qualitatively, using, primarily, a grounded theory approach. Results from the citizens’ stakeholder group indicate that personal biases, emotions, time constraints, and lack of supporting technologies impacts the credibility assessment of online news. Study participants also discussed the need for increased media literacy actions, especially in youth. Based on preliminary findings we argue that we need a diversified approach to support citizens’ resilience against the spread of misinformation.

scholarly journals Impact of Online Information on Self-Isolation Intention During the COVID-19 Pandemic: Cross-Sectional Study (Preprint)

2020 ◽  
Author(s):  
Ali Farooq ◽  
Samuli Laato ◽  
A K M Najmul Islam
Keyword(s):  
Social Media ◽  
Online Survey ◽  
Information Overload ◽  
Information Source ◽  
Partial Least Square ◽  
Least Square ◽  
Equation Modeling ◽  
Online Information ◽  
Living Situation ◽  
The Impact

BACKGROUND During the coronavirus disease (COVID-19) pandemic, governments issued movement restrictions and placed areas into quarantine to combat the spread of the disease. In addition, individuals were encouraged to adopt personal health measures such as social isolation. Information regarding the disease and recommended avoidance measures were distributed through a variety of channels including social media, news websites, and emails. Previous research suggests that the vast amount of available information can be confusing, potentially resulting in overconcern and information overload. OBJECTIVE This study investigates the impact of online information on the individual-level intention to voluntarily self-isolate during the pandemic. Using the protection-motivation theory as a framework, we propose a model outlining the effects of cyberchondria and information overload on individuals’ perceptions and motivations. METHODS To test the proposed model, we collected data with an online survey (N=225) and analyzed it using partial least square-structural equation modeling. The effects of social media and living situation were tested through multigroup analysis. RESULTS Cyberchondria and information overload had a significant impact on individuals’ threat and coping perceptions, and through them on self-isolation intention. Among the appraisal constructs, perceived severity (<i>P</i>=.002) and self-efficacy (<i>P</i>=.003) positively impacted self-isolation intention, while response cost (<i>P</i>&lt;.001) affected the intention negatively. Cyberchondria (<i>P</i>=.003) and information overload (<i>P</i>=.003) indirectly affected self-isolation intention through the aforementioned perceptions. Using social media as an information source increased both cyberchondria and information overload. No differences in perceptions were found between people living alone and those living with their families. CONCLUSIONS During COVID-19, frequent use of social media contributed to information overload and overconcern among individuals. To boost individuals’ motivation to adopt preventive measures such as self-isolation, actions should focus on lowering individuals’ perceived response costs in addition to informing them about the severity of the situation.

Looking At the Other Side

2016 ◽  
pp. 150-160
Author(s):  
Rebecca English ◽  
Shaun Nykvist
Keyword(s):  
Public Policy ◽  
Social Media ◽  
Critical Discourse Analysis ◽  
The Other ◽  
Online Information ◽  
Policy Debate ◽  
The Public ◽  
Online Social Media ◽  
Policy Work ◽  
Vaccine Information

The choice to vaccinate or not to vaccinate a child is usually an ‘informed decision', however, it is how this decision is informed which is of most importance. More frequently, families are turning to the Internet, in particular social media, as a data source to support their decisions. However, much of the online information may be unscientific or biased. While issues such as vaccination will always see dissenting voices, engaging with that ‘other side' is difficult in the public policy debate which is informed by evidence based science. This chapter investigates the other side in light of the growing adoption and reliance on social media as a source of anti-vaccine information. The study adopts a qualitative approach to data collection and is based on a critical discourse analysis of online social media discourse. The findings demonstrate the valuable contribution this approach can make to public policy work in vaccination.

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