11 research outputs found
Distinctiveness Centrality in Social Networks
The determination of node centrality is a fundamental topic in social network
studies. As an addition to established metrics, which identify central nodes
based on their brokerage power, the number and weight of their connections, and
the ability to quickly reach all other nodes, we introduce five new measures of
Distinctiveness Centrality. These new metrics attribute a higher score to nodes
keeping a connection with the network periphery. They penalize links to
highly-connected nodes and serve the identification of social actors with more
distinctive network ties. We discuss some possible applications and properties
of these newly introduced metrics, such as their upper and lower bounds.
Distinctiveness centrality provides a viewpoint of centrality alternative to
that of established metrics
Brand Network Booster: A New System for Improving Brand Connectivity
This paper presents a new decision support system offered for an in-depth
analysis of semantic networks, which can provide insights for a better
exploration of a brand's image and the improvement of its connectivity. In
terms of network analysis, we show that this goal is achieved by solving an
extended version of the Maximum Betweenness Improvement problem, which includes
the possibility of considering adversarial nodes, constrained budgets, and
weighted networks - where connectivity improvement can be obtained by adding
links or increasing the weight of existing connections. We present this new
system together with two case studies, also discussing its performance. Our
tool and approach are useful both for network scholars and for supporting the
strategic decision-making processes of marketing and communication managers
Analyzing the Spread of Misinformation on Social Networks:A Process and Software Architecture for Detection and Analysis
The rapid dissemination of misinformation on social networks, particularly during public health crises like the COVID-19 pandemic, has become a significant concern. This study investigates the spread of misinformation on social network data using social network analysis (SNA) metrics, and more generally by using well known network science metrics. Moreover, we propose a process design that utilizes social network data from Twitter, to analyze the involvement of non-trusted accounts in spreading misinformation supported by a proof-of-concept prototype. The proposed prototype includes modules for data collection, data preprocessing, network creation, centrality calculation, community detection, and misinformation spreading analysis. We conducted an experimental study on a COVID-19-related Twitter dataset using the modules. The results demonstrate the effectiveness of our approach and process steps, and provides valuable insight into the application of network science metrics on social network data for analysing various influence-parameters in misinformation spreading.</p
Evaluating and improving social awareness of energy communities through semantic network analysis of online news
The implementation of energy communities represents a cross-disciplinary
phenomenon that has the potential to support the energy transition while
fostering citizens' participation throughout the energy system and their
exploitation of renewables. An important role is played by online information
sources in engaging people in this process and increasing their awareness of
associated benefits. In this view, this work analyses online news data on
energy communities to understand people's awareness and the media importance of
this topic. We use the Semantic Brand Score (SBS) indicator as an innovative
measure of semantic importance, combining social network analysis and text
mining methods. Results show different importance trends for energy communities
and other energy and society-related topics, also allowing the identification
of their connections. Our approach gives evidence to information gaps and
possible actions that could be taken to promote a low-carbon energy transition
Forecasting consumer confidence through semantic network analysis of online news
This research studies the impact of online news on social and economic
consumer perceptions through semantic network analysis. Using over 1.8 million
online articles on Italian media covering four years, we calculate the semantic
importance of specific economic-related keywords to see if words appearing in
the articles could anticipate consumers' judgments about the economic situation
and the Consumer Confidence Index. We use an innovative approach to analyze big
textual data, combining methods and tools of text mining and social network
analysis. Results show a strong predictive power for the judgments about the
current households and national situation. Our indicator offers a complementary
approach to estimating consumer confidence, lessening the limitations of
traditional survey-based methods
The language and social behavior of innovators
Innovators are creative people who can conjure the ground-breaking ideas that
represent the main engine of innovative organizations. Past research has
extensively investigated who innovators are and how they behave in work-related
activities. In this paper, we suggest that it is necessary to analyze how
innovators behave in other contexts, such as in informal communication spaces,
where knowledge is shared without formal structure, rules, and work
obligations. Drawing on communication and network theory, we analyze about
38,000 posts available in the intranet forum of a large multinational company.
From this, we explain how innovators differ from other employees in terms of
social network behavior and language characteristics. Through text mining, we
find that innovators write more, use a more complex language, introduce new
concepts/ideas, and use positive but factual-based language. Understanding how
innovators behave and communicate can support the decision-making processes of
managers who want to foster innovation