The power of indirect social ties

While direct social ties have been intensely studied in the context of computer-mediated social networks, indirect ties (e.g., friends of friends) have seen little attention. Yet in real life, we often rely on friends of our friends for recommendations (of good doctors, good schools, or good babysitters), for introduction to a new job opportunity, and for many other occasional needs. In this work we attempt to 1) quantify the strength of indirect social ties, 2) validate it, and 3) empirically demonstrate its usefulness for distributed applications on two examples. We quantify social strength of indirect ties using a(ny) measure of the strength of the direct ties that connect two people and the intuition provided by the sociology literature. We validate the proposed metric experimentally by comparing correlations with other direct social tie evaluators. We show via data-driven experiments that the proposed metric for social strength can be used successfully for social applications. Specifically, we show that it alleviates known problems in friend-to-friend storage systems by addressing two previously documented shortcomings: reduced set of storage candidates and data availability correlations. We also show that it can be used for predicting the effects of a social diffusion with an accuracy of up to 93.5%.Comment: Technical Repor

$1.00 per RT #BostonMarathon #PrayForBoston: analyzing fake content on Twitter

This study found that 29% of the most viral content on Twitter during the Boston bombing crisis were rumors and fake content.AbstractOnline social media has emerged as one of the prominent channels for dissemination of information during real world events. Malicious content is posted online during events, which can result in damage, chaos and monetary losses in the real world. We analyzed one such media i.e. Twitter, for content generated during the event of Boston Marathon Blasts, that occurred on April, 15th, 2013. A lot of fake content and malicious profiles originated on Twitter network during this event. The aim of this work is to perform in-depth characterization of what factors influenced in malicious content and profiles becoming viral. Our results showed that 29% of the most viral content on Twitter, during the Boston crisis were rumors and fake content; while 51% was generic opinions and comments; and rest was true information. We found that large number of users with high social reputation and verified accounts were responsible for spreading the fake content. Next, we used regression prediction model, to verify that, overall impact of all users who propagate the fake content at a given time, can be used to estimate the growth of that content in future. Many malicious accounts were created on Twitter during the Boston event, that were later suspended by Twitter. We identified over six thousand such user profiles, we observed that the creation of such profiles surged considerably right after the blasts occurred. We identified closed community structure and star formation in the interaction network of these suspended profiles amongst themselves

Disinformation and Fact-Checking in Contemporary Society

Funded by the European Media and Information Fund and research project PID2022-142755OB-I00

Knowledge transfer in a tourism destination: the effects of a network structure

Tourism destinations have a necessity to innovate to remain competitive in an increasingly global environment. A pre-requisite for innovation is the understanding of how destinations source, share and use knowledge. This conceptual paper examines the nature of networks and how their analysis can shed light upon the processes of knowledge sharing in destinations as they strive to innovate. The paper conceptualizes destinations as networks of connected organizations, both public and private, each of which can be considered as a destination stakeholder. In network theory they represent the nodes within the system. The paper shows how epidemic diffusion models can act as an analogy for knowledge communication and transfer within a destination network. These models can be combined with other approaches to network analysis to shed light on how destination networks operate, and how they can be optimized with policy intervention to deliver innovative and competitive destinations. The paper closes with a practical tourism example taken from the Italian destination of Elba. Using numerical simulations the case demonstrates how the Elba network can be optimized. Overall this paper demonstrates the considerable utility of network analysis for tourism in delivering destination competitiveness.Comment: 15 pages, 2 figures, 2 tables. Forthcoming in: The Service Industries Journal, vol. 30, n. 8, 2010. Special Issue on: Advances in service network analysis v2: addeded and corrected reference

The influences of personality and motivation on the sharing of misinformation on social media

Social media, featuring rich user-generated information, is becoming an important component of daily life. It has also become a fertile ground for misinformation (inaccurate information) due to lack of quality control mechanisms. This study proposed and directly tested three predictor categories – personality, motivation, and perceived characteristic of information – to understand users’ misinformation sharing on social media. A survey was conducted with 171 university students. The findings showed that user-intrinsic factors and three motivation factors played influential roles in the sharing behavior. We thus concluded that people’s sharing of misinformation on social media is mainly influenced by their personalities or specific motivations. The action of sharing, rather than the perceived accuracy and characteristics of the information being shared, is what matters most. In light of the findings, besides teaching information evaluating skills, professionals responsible for information literacy training may also want to address the non-informational motivations that propel misinformation sharing

Simulating Public Administration Crisis: A Novel Generative Agent-Based Simulation System to Lower Technology Barriers in Social Science Research

This article proposes a social simulation paradigm based on the GPT-3.5 large language model. It involves constructing Generative Agents that emulate human cognition, memory, and decision-making frameworks, along with establishing a virtual social system capable of stable operation and an insertion mechanism for standardized public events. The project focuses on simulating a township water pollution incident, enabling the comprehensive examination of a virtual government's response to a specific public administration event. Controlled variable experiments demonstrate that the stored memory in generative agents significantly influences both individual decision-making and social networks. The Generative Agent-Based Simulation System introduces a novel approach to social science and public administration research. Agents exhibit personalized customization, and public events are seamlessly incorporated through natural language processing. Its high flexibility and extensive social interaction render it highly applicable in social science investigations. The system effectively reduces the complexity associated with building intricate social simulations while enhancing its interpretability.Comment: 12 Pages, 14 figures. This paper was submitted to IEEE TCSS on November 12, 202

Artificial Intelligence and Fake News

Artificial intelligence depends on digital devices’ performance to perform tasks regularly, requiring human intelligence, using special software to accomplish work easier and faster, carrying out data-packed tasks, and providing useful analytics or solutions. It also requires a specialized laboratory that provides high-performance computing capabilities and a technical platform for deep machine learning. These resources will enable the artificial intelligence platform to master the machine learning techniques of using, developing, simulating, predicting models, and building ready-to-use technological solutions such as analytics platforms. In general, the artificial intelligence system manipulates and manages large amounts of training data to form correlations and patterns used in building future predictions . A limited-memory artificial intelligence system can store a limited amount of information based on the data that have been processed and dealt with previously to build knowledge by memory when combined with pre-programmed data. Consequently, one may ask how artificial intelligence applications contribute to verifying the truthfulness of the media through digital media. How do they contribute to preventing the spread of misleading and false news? This study tries to answer the following question: What methods and tools are adopted by artificial intelligence to detect fake news, especially on social media platforms and depending on artificial intelligence laboratories? This paper is framed within automation control theory and by defining the needed control tools and programs to detect fake news and verify media facts