CNR Author
operator:
and / or
Typology
operator:
and / or
Language
operator:
and / or
Date
operator:
and / or
Rights
operator:
and / or
2023
Journal article
Open Access
Cognitive network neighborhoods quantify feelings expressed in suicide notes and Reddit mental health communities
Joseph S. M., Citraro S., Morini V., Rossetti G., Stella M.
Writing messages is key to expressing feelings. This study adopts cognitive network science to reconstruct how individuals report their feelings in clinical narratives like suicide notes or mental health posts. We achieve this by reconstructing syntactic/semantic associations between concepts in texts as co-occurrences enriched with affective data. We transform 142 suicide notes and 77,000 Reddit posts from the r/anxiety, r/depression, r/schizophrenia, and r/do-it-your-own (r/DIY) forums into 5 cognitive networks, each one expressing meanings and emotions as reported by authors. These networks reconstruct the semantic frames surrounding "feel", stem for "to feel" and "feelings", enabling a quantification of prominent associations and emotions focused around feelings. We find strong feelings of sadness across all clinical Reddit boards, added to fear r/depression, and replaced by joy/anticipation in r/DIY. Semantic communities and topic modeling both highlight key narrative topics of "regret", "unhealthy lifestyle" and "low mental well-being". Importantly, negative associations and emotions co-existed with trustful/positive language, focused on "getting better". This emotional polarization provides quantitative evidence that online clinical boards possess a complex structure, where users mix both positive and negative outlooks. This dichotomy is absent in the DIY reference board and in suicide notes, where negative emotional associations about regret and pain persist but are overwhelmed by positive jargon addressing loved ones. Our network-based comparisons provide quantitative evidence that suicide notes encapsulate different ways of expressing feelings compared to online Reddit boards, the latter acting more like personal diaries and relief valves. Our findings provide an interpretable network-based aid for supporting psychological inquiries of human feelings in digital and clinical settings.Source: Physica. A (Print) 610 (2023). doi:10.1016/j.physa.2022.128336
DOI: 10.1016/j.physa.2022.128336
Project(s): SoBigData-PlusPlus
Metrics:
Joseph S. M., Citraro S., Morini V., Rossetti G., Stella M.
Writing messages is key to expressing feelings. This study adopts cognitive network science to reconstruct how individuals report their feelings in clinical narratives like suicide notes or mental health posts. We achieve this by reconstructing syntactic/semantic associations between concepts in texts as co-occurrences enriched with affective data. We transform 142 suicide notes and 77,000 Reddit posts from the r/anxiety, r/depression, r/schizophrenia, and r/do-it-your-own (r/DIY) forums into 5 cognitive networks, each one expressing meanings and emotions as reported by authors. These networks reconstruct the semantic frames surrounding "feel", stem for "to feel" and "feelings", enabling a quantification of prominent associations and emotions focused around feelings. We find strong feelings of sadness across all clinical Reddit boards, added to fear r/depression, and replaced by joy/anticipation in r/DIY. Semantic communities and topic modeling both highlight key narrative topics of "regret", "unhealthy lifestyle" and "low mental well-being". Importantly, negative associations and emotions co-existed with trustful/positive language, focused on "getting better". This emotional polarization provides quantitative evidence that online clinical boards possess a complex structure, where users mix both positive and negative outlooks. This dichotomy is absent in the DIY reference board and in suicide notes, where negative emotional associations about regret and pain persist but are overwhelmed by positive jargon addressing loved ones. Our network-based comparisons provide quantitative evidence that suicide notes encapsulate different ways of expressing feelings compared to online Reddit boards, the latter acting more like personal diaries and relief valves. Our findings provide an interpretable network-based aid for supporting psychological inquiries of human feelings in digital and clinical settings.Source: Physica. A (Print) 610 (2023). doi:10.1016/j.physa.2022.128336
DOI: 10.1016/j.physa.2022.128336
Project(s): SoBigData-PlusPlus
Metrics:
See at:
ISTI Repository 
| Physica A Statistical Mechanics and its Applications 
| www.sciencedirect.com | CNR ExploRA
2023
Conference article
Open Access
Exposing racial dialect bias in abusive language detection: can explainability play a role?
Manerba M. M., Morini V.
Biases can arise and be introduced during each phase of a supervised learning pipeline, eventually leading to harm. Within the task of automatic abusive language detection, this matter becomes particularly severe since unintended bias towards sensitive topics such as gender, sexual orientation, or ethnicity can harm underrepresented groups. The role of the datasets used to train these models is crucial to address these challenges. In this contribution, we investigate whether explainability methods can expose racial dialect bias attested within a popular dataset for abusive language detection. Through preliminary experiments, we found that pure explainability techniques cannot effectively uncover biases within the dataset under analysis: the rooted stereotypes are often more implicit and complex to retrieve.Source: ECML PKDD 2022 - Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 483–497, Grenoble, France, 19-23/09/2022
DOI: 10.1007/978-3-031-23618-1_32
Project(s): TAILOR, HumanE-AI-Net , SoBigData-PlusPlus
Metrics:
Manerba M. M., Morini V.
Biases can arise and be introduced during each phase of a supervised learning pipeline, eventually leading to harm. Within the task of automatic abusive language detection, this matter becomes particularly severe since unintended bias towards sensitive topics such as gender, sexual orientation, or ethnicity can harm underrepresented groups. The role of the datasets used to train these models is crucial to address these challenges. In this contribution, we investigate whether explainability methods can expose racial dialect bias attested within a popular dataset for abusive language detection. Through preliminary experiments, we found that pure explainability techniques cannot effectively uncover biases within the dataset under analysis: the rooted stereotypes are often more implicit and complex to retrieve.Source: ECML PKDD 2022 - Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 483–497, Grenoble, France, 19-23/09/2022
DOI: 10.1007/978-3-031-23618-1_32
Project(s): TAILOR
, HumanE-AI-Net , SoBigData-PlusPlus Metrics:
See at:
link.springer.com | ISTI Repository | doi.org | CNR ExploRA
2023
Contribution to book
Unknown
Towards a social Artificial Intelligence
Pedreschi D., Dignum F., Morini V., Pansanella V., Cornacchia G.
Artificial Intelligence can both empower individuals to face complex societal challenges and exacerbate problems and vulnerabilities, such as bias, inequalities, and polarization. For scientists, an open challenge is how to shape and regulate human-centered Artificial Intelligence ecosystems that help mitigate harms and foster beneficial outcomes oriented at the social good. In this tutorial, we discuss such an issue from two sides. First, we explore the network effects of Artificial Intelligence and their impact on society by investigating its role in social media, mobility, and economic scenarios. We further provide different strategies that can be used to model, characterize and mitigate the network effects of particular Artificial Intelligence driven individual behavior. Secondly, we promote the use of behavioral models as an addition to the data-based approach to get a further grip on emerging phenomena in society that depend on physical events for which no data are readily available. An example of this is tracking extremist behavior in order to prevent violent events. In the end, we illustrate some case studies in-depth and provide the appropriate tools to get familiar with these concepts.Source: Human-Centered Artificial Intelligence, edited by Chetouani M., Dignum V., Lukowicz P., Sierra C., pp. 415–428, 2023
DOI: 10.1007/978-3-031-24349-3_21
Metrics:
Pedreschi D., Dignum F., Morini V., Pansanella V., Cornacchia G.
Artificial Intelligence can both empower individuals to face complex societal challenges and exacerbate problems and vulnerabilities, such as bias, inequalities, and polarization. For scientists, an open challenge is how to shape and regulate human-centered Artificial Intelligence ecosystems that help mitigate harms and foster beneficial outcomes oriented at the social good. In this tutorial, we discuss such an issue from two sides. First, we explore the network effects of Artificial Intelligence and their impact on society by investigating its role in social media, mobility, and economic scenarios. We further provide different strategies that can be used to model, characterize and mitigate the network effects of particular Artificial Intelligence driven individual behavior. Secondly, we promote the use of behavioral models as an addition to the data-based approach to get a further grip on emerging phenomena in society that depend on physical events for which no data are readily available. An example of this is tracking extremist behavior in order to prevent violent events. In the end, we illustrate some case studies in-depth and provide the appropriate tools to get familiar with these concepts.Source: Human-Centered Artificial Intelligence, edited by Chetouani M., Dignum V., Lukowicz P., Sierra C., pp. 415–428, 2023
DOI: 10.1007/978-3-031-24349-3_21
Metrics:
See at: link.springer.com | CNR ExploRA
2023
Conference article
Open Access
Will you take the knee? Italian twitter echo chambers' genesis during EURO 2020
Buongiovanni C., Candusso R., Cerretini G., Febbe D., Morini V., Rossetti G.
Echo chambers can be described as situations in which individuals encounter and interact only with viewpoints that confirm their own, thus moving, as a group, to more polarized and extreme positions. Recent literature mainly focuses on characterizing such entities via static observations, thus disregarding their temporal dimension. In this work, distancing from such a trend, we study, at multiple topological levels, echo chambers genesis related to the social discussions that took place in Italy during the EURO 2020 Championship. Our analysis focuses on a well-defined topic (i.e., BLM/racism) discussed on Twitter during a perfect temporally bound (sporting) event. Such characteristics allow us to track the rise and evolution of echo chambers in time, thus relating their existence to specific episodes.Source: COMPLEX NETWORKS 2022 - Eleventh International Conference on Complex Networks and Their Applications, pp. 29–40, Palermo, Italy, 08-10/11/2022
DOI: 10.1007/978-3-031-21127-0_3
Project(s): SoBigData-PlusPlus
Metrics:
Buongiovanni C., Candusso R., Cerretini G., Febbe D., Morini V., Rossetti G.
Echo chambers can be described as situations in which individuals encounter and interact only with viewpoints that confirm their own, thus moving, as a group, to more polarized and extreme positions. Recent literature mainly focuses on characterizing such entities via static observations, thus disregarding their temporal dimension. In this work, distancing from such a trend, we study, at multiple topological levels, echo chambers genesis related to the social discussions that took place in Italy during the EURO 2020 Championship. Our analysis focuses on a well-defined topic (i.e., BLM/racism) discussed on Twitter during a perfect temporally bound (sporting) event. Such characteristics allow us to track the rise and evolution of echo chambers in time, thus relating their existence to specific episodes.Source: COMPLEX NETWORKS 2022 - Eleventh International Conference on Complex Networks and Their Applications, pp. 29–40, Palermo, Italy, 08-10/11/2022
DOI: 10.1007/978-3-031-21127-0_3
Project(s): SoBigData-PlusPlus
Metrics:
See at:
ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2022
Contribution to book
Open Access
Big Data Analytics and Instagram: an exploratory study on Italian hotel accounts
Pianese T., Rossetti G., Morini V.
A wealth of tourism-related data is available on the Internet, particularly on social networking sites (SNSs) like Facebook and Instagram. Big data analytics (BDA) allows this large quantity of data to be processed, supported by machine learning and artificial intelligence, and gain an in-depth understanding of traveller preferences and behaviours. With regard to hotels, the analysis of data from SNSs provides countless actionable insights into customers'socio-demographic features, habits, daily trends and brand attitudes. This enables communication to be perfectly targeted, besides supplying valuable information to improve customer satisfaction. Nevertheless, the study of the implications of the automatic processing of data from SNSs in the hotel industry is still in its embryonic state. In order to demonstrate the utility of BDA to under- stand how hotels leverage SNSs, we conducted an exploratory study on the Instagram accounts - the photo-sharing SNS known worldwide - of eleven Italian hotels. To this end, the average sentiment score, the average length, lexical diversity and word clouds were calculated on textual data, collected with the instagrapi python package and pre-processed leveraging a standard NLP pipeline. These evidenced different stages of implementation of digital communication on SNSs, shorter text-based messages written on Instagram compared to other SNSs, and specific patterns of user engagement in hotel accounts. BDA also provides information about the online self-promotion process: hotel digital communication is clearly connected to destination, and hashtags are chosen to reach the desired community of travellers.Source: Tourism, Hospitality and Culture 4.0: shifting towards the metaverse, edited by Buonincontri Piera, Luisa Errichiello, Roberto Micera, pp. 13–36. Milano: Mc Graw-Hill, 2022
Project(s): SoBigData-PlusPlus
Pianese T., Rossetti G., Morini V.
A wealth of tourism-related data is available on the Internet, particularly on social networking sites (SNSs) like Facebook and Instagram. Big data analytics (BDA) allows this large quantity of data to be processed, supported by machine learning and artificial intelligence, and gain an in-depth understanding of traveller preferences and behaviours. With regard to hotels, the analysis of data from SNSs provides countless actionable insights into customers'socio-demographic features, habits, daily trends and brand attitudes. This enables communication to be perfectly targeted, besides supplying valuable information to improve customer satisfaction. Nevertheless, the study of the implications of the automatic processing of data from SNSs in the hotel industry is still in its embryonic state. In order to demonstrate the utility of BDA to under- stand how hotels leverage SNSs, we conducted an exploratory study on the Instagram accounts - the photo-sharing SNS known worldwide - of eleven Italian hotels. To this end, the average sentiment score, the average length, lexical diversity and word clouds were calculated on textual data, collected with the instagrapi python package and pre-processed leveraging a standard NLP pipeline. These evidenced different stages of implementation of digital communication on SNSs, shorter text-based messages written on Instagram compared to other SNSs, and specific patterns of user engagement in hotel accounts. BDA also provides information about the online self-promotion process: hotel digital communication is clearly connected to destination, and hashtags are chosen to reach the desired community of travellers.Source: Tourism, Hospitality and Culture 4.0: shifting towards the metaverse, edited by Buonincontri Piera, Luisa Errichiello, Roberto Micera, pp. 13–36. Milano: Mc Graw-Hill, 2022
Project(s): SoBigData-PlusPlus
See at:
ISTI Repository | CNR ExploRA
2021
Journal article
Open Access
UTLDR: an agent-based framework for modeling infectious diseases and public interventions
Rossetti G., Milli L., Citraro S., Morini V.
Due to the SARS-CoV-2 pandemic, epidemic modeling is now experiencing a constantly growing interest from researchers of heterogeneous study fields. Indeed, due to such an increased attention, several software libraries and scientific tools have been developed to ease the access to epidemic modeling. However, only a handful of such resources were designed with the aim of providing a simple proxy for the study of the potential effects of public interventions (e.g., lockdown, testing, contact tracing). In this work, we introduce UTLDR, a framework that, overcoming such limitations, allows to generate "what if" epidemic scenarios incorporating several public interventions (and their combinations). UTLDR is designed to be easy to use and capable to leverage information provided by stratified populations of agents (e.g., age, gender, geographical allocation, and mobility patterns...). Moreover, the proposed framework is generic and not tailored for a specific epidemic phenomena: it aims to provide a qualitative support to understanding the effects of restrictions, rather than produce forecasts/explanation of specific data-driven phenomena.Source: Journal of intelligent information systems (2021). doi:10.1007/s10844-021-00649-6
DOI: 10.1007/s10844-021-00649-6
Project(s): SoBigData-PlusPlus
Metrics:
Rossetti G., Milli L., Citraro S., Morini V.
Due to the SARS-CoV-2 pandemic, epidemic modeling is now experiencing a constantly growing interest from researchers of heterogeneous study fields. Indeed, due to such an increased attention, several software libraries and scientific tools have been developed to ease the access to epidemic modeling. However, only a handful of such resources were designed with the aim of providing a simple proxy for the study of the potential effects of public interventions (e.g., lockdown, testing, contact tracing). In this work, we introduce UTLDR, a framework that, overcoming such limitations, allows to generate "what if" epidemic scenarios incorporating several public interventions (and their combinations). UTLDR is designed to be easy to use and capable to leverage information provided by stratified populations of agents (e.g., age, gender, geographical allocation, and mobility patterns...). Moreover, the proposed framework is generic and not tailored for a specific epidemic phenomena: it aims to provide a qualitative support to understanding the effects of restrictions, rather than produce forecasts/explanation of specific data-driven phenomena.Source: Journal of intelligent information systems (2021). doi:10.1007/s10844-021-00649-6
DOI: 10.1007/s10844-021-00649-6
Project(s): SoBigData-PlusPlus
Metrics:
See at:
link.springer.com | ISTI Repository | CNR ExploRA
2021
Journal article
Open Access
Toward a standard approach for echo chamber detection: Reddit case study
Morini V., Pollacci L., Rossetti G.
Featured Application: The framework proposed in this paper could be used to detect echo chambers in a standard way across multiple online social networks (i.e., leveraging features they commonly share). Such application then allows for comparative analysis between different platforms, thus discovering if some are more polarized than others. Further, a standard echo chamber characterization could be a starting point for designing a Recommendation System able to recognize and mitigate such a phenomenon. Abstract: In a digital environment, the term echo chamber refers to an alarming phenomenon in which beliefs are amplified or reinforced by communication repetition inside a closed system and insulated from rebuttal. Up to date, a formal definition, as well as a platform-independent approach for its detection, is still lacking. This paper proposes a general framework to identify echo chambers on online social networks built on top of features they commonly share. Our approach is based on a four-step pipeline that involves (i) the identification of a controversial issue; (ii) the inference of users' ideology on the controversy; (iii) the construction of users' debate network; and (iv) the detection of homogeneous meso-scale communities. We further apply our framework in a detailed case study on Reddit, covering the first two and a half years of Donald Trump's presidency. Our main purpose is to assess the existence of Pro-Trump and Anti-Trump echo chambers among three sociopolitical issues, as well as to analyze their stability and consistency over time. Even if users appear strongly polarized with respect to their ideology, most tend not to insulate themselves in echo chambers. However, the found polarized communities were proven to be definitely stable over time.Source: Applied sciences 11 (2021). doi:10.3390/app11125390
DOI: 10.3390/app11125390
Project(s): SoBigData-PlusPlus
Metrics:
Morini V., Pollacci L., Rossetti G.
Featured Application: The framework proposed in this paper could be used to detect echo chambers in a standard way across multiple online social networks (i.e., leveraging features they commonly share). Such application then allows for comparative analysis between different platforms, thus discovering if some are more polarized than others. Further, a standard echo chamber characterization could be a starting point for designing a Recommendation System able to recognize and mitigate such a phenomenon. Abstract: In a digital environment, the term echo chamber refers to an alarming phenomenon in which beliefs are amplified or reinforced by communication repetition inside a closed system and insulated from rebuttal. Up to date, a formal definition, as well as a platform-independent approach for its detection, is still lacking. This paper proposes a general framework to identify echo chambers on online social networks built on top of features they commonly share. Our approach is based on a four-step pipeline that involves (i) the identification of a controversial issue; (ii) the inference of users' ideology on the controversy; (iii) the construction of users' debate network; and (iv) the detection of homogeneous meso-scale communities. We further apply our framework in a detailed case study on Reddit, covering the first two and a half years of Donald Trump's presidency. Our main purpose is to assess the existence of Pro-Trump and Anti-Trump echo chambers among three sociopolitical issues, as well as to analyze their stability and consistency over time. Even if users appear strongly polarized with respect to their ideology, most tend not to insulate themselves in echo chambers. However, the found polarized communities were proven to be definitely stable over time.Source: Applied sciences 11 (2021). doi:10.3390/app11125390
DOI: 10.3390/app11125390
Project(s): SoBigData-PlusPlus
Metrics:
See at:
ISTI Repository | www.mdpi.com | CNR ExploRA
2020
Conference article
Open Access
Capturing Political Polarization of Reddit Submissions in the Trump Era
Morini V., Pollacci L., Rossetti G.
The American political situation of the last years, combined with the incredible growth of Social Networks, led to the diffusion of political polarization's phenomenon online. Our work presents a model that attempts to measure the political polarization of Reddit submissions during the first half of Donald Trump's presidency. To do so, we design a text classification task: Political polarization of submissions is assessed by quantifying those who align themselves with pro-Trump ideologies and vice versa. We build our ground truth by picking submissions from subreddits known to be strongly polarized. Then, for model selection, we use a Neural Network with word embeddings and Long Short Time Memory layer and, finally, we analyze how model performances change trying different hyper-parameters and types of embeddings.Source: SEBD 2020 28th SYMPOSIUM ON ADVANCED DATABASE SYSTEMS, pp. 80–87, Villasimius, Sardinia, Italy, June 21-24, 2020
Project(s): SoBigData-PlusPlus
Morini V., Pollacci L., Rossetti G.
The American political situation of the last years, combined with the incredible growth of Social Networks, led to the diffusion of political polarization's phenomenon online. Our work presents a model that attempts to measure the political polarization of Reddit submissions during the first half of Donald Trump's presidency. To do so, we design a text classification task: Political polarization of submissions is assessed by quantifying those who align themselves with pro-Trump ideologies and vice versa. We build our ground truth by picking submissions from subreddits known to be strongly polarized. Then, for model selection, we use a Neural Network with word embeddings and Long Short Time Memory layer and, finally, we analyze how model performances change trying different hyper-parameters and types of embeddings.Source: SEBD 2020 28th SYMPOSIUM ON ADVANCED DATABASE SYSTEMS, pp. 80–87, Villasimius, Sardinia, Italy, June 21-24, 2020
Project(s): SoBigData-PlusPlus
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA