Typology
operator:
and / or
Language
operator:
and / or
Date
operator:
and / or
Rights
operator:
and / or
2017
Journal article
Open Access
Perception of social phenomena through the multidimensional analysis of online social networks
Coletto M., Esuli A., Lucchese C., Muntean C. I., Nardini F. M., Perego R., Renso C.
We propose an analytical framework aimed at investigating different views of the discussions regarding polarized topics which occur in Online Social Networks (OSNs). The framework supports the analysis along multiple dimensions, i.e., time, space and sentiment of the opposite views about a controversial topic emerging in an OSN. To assess its usefulness in mining insights about social phenomena, we apply it to two different Twitter case studies: the discussions about the refugee crisis and the United Kingdom European Union membership referendum. These complex and contended topics are very important issues for EU citizens and stimulated a multitude of Twitter users to take side and actively participate in the discussions. Our framework allows to monitor in a scalable way the raw stream of relevant tweets and to automatically enrich them with location information (user and mentioned locations), and sentiment polarity (positive vs. negative). The analyses we conducted show how the framework captures the differences in positive and negative user sentiment over time and space. The resulting knowledge can support the understanding of complex dynamics by identifying variations in the perception of specific events and locations.Source: Online social networks and media 1 (2017): 14–32. doi:10.1016/j.osnem.2017.03.001
DOI: 10.1016/j.osnem.2017.03.001
Project(s): SoBigData
Metrics:
Coletto M., Esuli A., Lucchese C., Muntean C. I., Nardini F. M., Perego R., Renso C.
We propose an analytical framework aimed at investigating different views of the discussions regarding polarized topics which occur in Online Social Networks (OSNs). The framework supports the analysis along multiple dimensions, i.e., time, space and sentiment of the opposite views about a controversial topic emerging in an OSN. To assess its usefulness in mining insights about social phenomena, we apply it to two different Twitter case studies: the discussions about the refugee crisis and the United Kingdom European Union membership referendum. These complex and contended topics are very important issues for EU citizens and stimulated a multitude of Twitter users to take side and actively participate in the discussions. Our framework allows to monitor in a scalable way the raw stream of relevant tweets and to automatically enrich them with location information (user and mentioned locations), and sentiment polarity (positive vs. negative). The analyses we conducted show how the framework captures the differences in positive and negative user sentiment over time and space. The resulting knowledge can support the understanding of complex dynamics by identifying variations in the perception of specific events and locations.Source: Online social networks and media 1 (2017): 14–32. doi:10.1016/j.osnem.2017.03.001
DOI: 10.1016/j.osnem.2017.03.001
Project(s): SoBigData
Metrics:
See at:
ISTI Repository 
| Online Social Networks and Media 
| www.sciencedirect.com | CNR ExploRA
2016
Journal article
Restricted
Simple outlier labeling based on quantileregression, with application to thesteelmaking process
Bellio R., Coletto M.
This paper introduces some methods for outlier identiîEUR,cation in the regression setting, motivated by the analysis of steelmakingprocess data. The proposed methodology extends to the regression setting the boxplot rule, commonly used for outlier screening withunivariate data. The focus here is on bivariate settings with a single covariate, but extensions are possible. The proposal is basedon quantile regression, including an additional transformation parameter for selecting the best scale for linearity of the conditionalquantiles. The resulting method is used to perform effective labeling of potential outliers, with a quite low computational complexity,allowing for simple implementation within statistical software as well as commonly used spreadsheets. Some simulation experimentshave been carried out to study the swamping and masking properties of the proposal. The methodology is also illustrated by somereal life examples, taking as the response variable the energy consumed in the melting process.Source: Applied stochastic models in business and industry (Online) 32 (2016): 228–232. doi:10.1002/asmb.2146
DOI: 10.1002/asmb.2146
Metrics:
Bellio R., Coletto M.
This paper introduces some methods for outlier identiîEUR,cation in the regression setting, motivated by the analysis of steelmakingprocess data. The proposed methodology extends to the regression setting the boxplot rule, commonly used for outlier screening withunivariate data. The focus here is on bivariate settings with a single covariate, but extensions are possible. The proposal is basedon quantile regression, including an additional transformation parameter for selecting the best scale for linearity of the conditionalquantiles. The resulting method is used to perform effective labeling of potential outliers, with a quite low computational complexity,allowing for simple implementation within statistical software as well as commonly used spreadsheets. Some simulation experimentshave been carried out to study the swamping and masking properties of the proposal. The methodology is also illustrated by somereal life examples, taking as the response variable the energy consumed in the melting process.Source: Applied stochastic models in business and industry (Online) 32 (2016): 228–232. doi:10.1002/asmb.2146
DOI: 10.1002/asmb.2146
Metrics:
See at:
Applied Stochastic Models in Business and Industry | onlinelibrary.wiley.com | CNR ExploRA
2016
Conference article
Open Access
On the behaviour of deviant communities in online social networks
Coletto M., Aiello L. M., Lucchese C., Silvestri F.
On-line social networks are complex ensembles of inter-linked communities that interact on different topics. Some communities are characterized by what are usually referred to as deviant behaviours, conducts that are commonly considered inappropriate with respect to the society's norms or moral standards. Eating disorders, drug use, and adult content consumption are just a few examples. We refer to such communities as deviant networks. It is commonly believed that such deviant networks are niche, isolated social groups, whose activity is well separated from the mainstream social media life. According to this assumption, research studies have mostly considered them in isolation. In this work we focused on adult content consumption networks, which are present in many on-line social media and in the Web in general. We found that few small and densely connected communities are responsible for most of the content production. Differently from previous work, we studied how such communities interact with the whole social network. We found that the produced content flows to the rest of the network mostly directly or through bridge-communities, reaching at least 450 times more users.We also show that a large fraction of the users can be inadvertently exposed to such content through indirect content resharing. We also discuss a demographic analysis of the producers and consumers networks. Finally, we show that it is easily possible to identify a few core users to radically uproot the diffusion process. We aim at setting the basis to study deviant communities in context.Source: ICWSM 2016 - Tenth International AAAI Conference on Web and Social Media, pp. 72, Cologne, Germany, 17-20 May 2016
Coletto M., Aiello L. M., Lucchese C., Silvestri F.
On-line social networks are complex ensembles of inter-linked communities that interact on different topics. Some communities are characterized by what are usually referred to as deviant behaviours, conducts that are commonly considered inappropriate with respect to the society's norms or moral standards. Eating disorders, drug use, and adult content consumption are just a few examples. We refer to such communities as deviant networks. It is commonly believed that such deviant networks are niche, isolated social groups, whose activity is well separated from the mainstream social media life. According to this assumption, research studies have mostly considered them in isolation. In this work we focused on adult content consumption networks, which are present in many on-line social media and in the Web in general. We found that few small and densely connected communities are responsible for most of the content production. Differently from previous work, we studied how such communities interact with the whole social network. We found that the produced content flows to the rest of the network mostly directly or through bridge-communities, reaching at least 450 times more users.We also show that a large fraction of the users can be inadvertently exposed to such content through indirect content resharing. We also discuss a demographic analysis of the producers and consumers networks. Finally, we show that it is easily possible to identify a few core users to radically uproot the diffusion process. We aim at setting the basis to study deviant communities in context.Source: ICWSM 2016 - Tenth International AAAI Conference on Web and Social Media, pp. 72, Cologne, Germany, 17-20 May 2016
See at:
www.aaai.org | CNR ExploRA
2015
Report
Open Access
Twitter for election forecasts: a joint machine learning and complex network approach applied to an italian case study
Coletto M., Lucchese C., Orlando S., Perego R., Chessa A., Puliga M.
Several studies have shown how to approximately predict real-world phenomena, such as political elections, by ana- lyzing user activities in micro-blogging platforms. This ap- proach has proven to be interesting but with some limita- tions, such as the representativeness of the sample of users, and the hardness of understanding polarity in short mes- sages. We believe that predictions based on social network analysis can be significantly improved by exploiting machine learning and complex network tools, where the latter pro- vides valuable high-level features to support the former in learning an accurate prediction function.Source: ISTI Technical reports, 2015
Coletto M., Lucchese C., Orlando S., Perego R., Chessa A., Puliga M.
Several studies have shown how to approximately predict real-world phenomena, such as political elections, by ana- lyzing user activities in micro-blogging platforms. This ap- proach has proven to be interesting but with some limita- tions, such as the representativeness of the sample of users, and the hardness of understanding polarity in short mes- sages. We believe that predictions based on social network analysis can be significantly improved by exploiting machine learning and complex network tools, where the latter pro- vides valuable high-level features to support the former in learning an accurate prediction function.Source: ISTI Technical reports, 2015
See at:
ISTI Repository | CNR ExploRA
2015
Journal article
Open Access
Science vs Conspiracy: Collective Narratives in the Age of Misinformation
Bessi A., Coletto M., Davidescu G. A., Scala A., Caldarelli G., Quattrociocchi W.
The large availability of user provided contents on online social media facilitates people aggregation around shared beliefs, interests, worldviews and narratives. In spite of the enthusiastic rhetoric about the so called collective intelligence unsubstantiated rumors and conspiracy theories--e.g., chemtrails, reptilians or the Illuminati--are pervasive in online social networks (OSN). In this work we study, on a sample of 1.2 million of individuals, how information related to very distinct narratives--i.e. main stream scientific and conspiracy news--are consumed and shape communities on Facebook. Our results show that polarized communities emerge around distinct types of contents and usual consumers of conspiracy news result to be more focused and self-contained on their specific contents. To test potential biases induced by the continued exposure to unsubstantiated rumors on users' content selection, we conclude our analysis measuring how users respond to 4,709 troll information--i.e. parodistic and sarcastic imitation of conspiracy theories. We find that 77.92% of likes and 80.86% of comments are from users usually interacting with conspiracy stories.Source: PloS one 10 (2015). doi:10.1371/journal.pone.0118093
DOI: 10.1371/journal.pone.0118093
DOI: 10.48550/arxiv.1408.1667
Project(s): DOLFINS, SIMPOL , MULTIPLEX
Metrics:
Bessi A., Coletto M., Davidescu G. A., Scala A., Caldarelli G., Quattrociocchi W.
The large availability of user provided contents on online social media facilitates people aggregation around shared beliefs, interests, worldviews and narratives. In spite of the enthusiastic rhetoric about the so called collective intelligence unsubstantiated rumors and conspiracy theories--e.g., chemtrails, reptilians or the Illuminati--are pervasive in online social networks (OSN). In this work we study, on a sample of 1.2 million of individuals, how information related to very distinct narratives--i.e. main stream scientific and conspiracy news--are consumed and shape communities on Facebook. Our results show that polarized communities emerge around distinct types of contents and usual consumers of conspiracy news result to be more focused and self-contained on their specific contents. To test potential biases induced by the continued exposure to unsubstantiated rumors on users' content selection, we conclude our analysis measuring how users respond to 4,709 troll information--i.e. parodistic and sarcastic imitation of conspiracy theories. We find that 77.92% of likes and 80.86% of comments are from users usually interacting with conspiracy stories.Source: PloS one 10 (2015). doi:10.1371/journal.pone.0118093
DOI: 10.1371/journal.pone.0118093
DOI: 10.48550/arxiv.1408.1667
Project(s): DOLFINS
, SIMPOL , MULTIPLEX Metrics:
See at:
arXiv.org e-Print Archive | PLoS ONE | PLoS ONE | PLoS ONE | PLoS ONE | journals.plos.org | doi.org | CNR ExploRA
2015
Contribution to conference
Open Access
Twitter for election forecasts: a joint machine learning and complex network approach applied to an italian case study
Coletto M., Lucchese C., Orlando S., Perego R., Chessa A., Puliga M.
Several studies have shown how to approximately predict real-world phenomena, such as political elections, by analyzing user activities in micro-blogging platforms. This approach has proven to be interesting but with some limitations, such as the representativeness of the sample of users, and the hardness of understanding polarity in short messages. We believe that predictions based on social network analysis can be significantly improved by exploiting machine learning and complex network tools, where the latter pro- vides valuable high-level features to support the former in learning an accurate prediction function.Source: International Conference on Computational Social Science (ICCSS 2015), Helsinki, Finland, 08-11/06/2015
Coletto M., Lucchese C., Orlando S., Perego R., Chessa A., Puliga M.
Several studies have shown how to approximately predict real-world phenomena, such as political elections, by analyzing user activities in micro-blogging platforms. This approach has proven to be interesting but with some limitations, such as the representativeness of the sample of users, and the hardness of understanding polarity in short messages. We believe that predictions based on social network analysis can be significantly improved by exploiting machine learning and complex network tools, where the latter pro- vides valuable high-level features to support the former in learning an accurate prediction function.Source: International Conference on Computational Social Science (ICCSS 2015), Helsinki, Finland, 08-11/06/2015
See at:
ISTI Repository | CNR ExploRA
2014
Conference article
Unknown
Misinformation in the loop: the emergence of narratives in online social networks
Coletto M., Bessi A., Davidescu G. A., Scala A., Quattrociocchi W.
The interlink between information and belief formation and revision is a fundamental aspect of social dynamics. The growth of knowledge fostered by a hyper-connected world together with the unprecedented acceleration of scientific progress has exposed individuals, governments and countries to an increasing level of complexity to explain reality and its phenomena. Despite the enthusiastic rhetoric about the so called collective intelligence, conspiracy theories and other unsubstantiated claims find on the Web a natural medium for their diffusion. Cases in which these kinds of false information are used in political debates are far from unimaginable. In this work, we study the behavior of users supporting different (and opposite) worldviews - i.e. scientific and conspiracist thinking - that commented the posts of the Facebook page of a large italian political party that advocates direct democracy and e-Participation. We find that users supporting different narratives consume political information in a similar way. Moreover, by analyzing the composition of users active on the page in terms of commenting activity, we notice that almost one fifth of them is represented by polarized consumers of conspiracy stories, and those are able to generate almost one third of total comments to the posts of the page.Source: XI Conference of the Italian Chapter of AIS Digital Innovation and Inclusive Knowledge in Times of Change, Genova, Italy, 21-22/11/2014
Coletto M., Bessi A., Davidescu G. A., Scala A., Quattrociocchi W.
The interlink between information and belief formation and revision is a fundamental aspect of social dynamics. The growth of knowledge fostered by a hyper-connected world together with the unprecedented acceleration of scientific progress has exposed individuals, governments and countries to an increasing level of complexity to explain reality and its phenomena. Despite the enthusiastic rhetoric about the so called collective intelligence, conspiracy theories and other unsubstantiated claims find on the Web a natural medium for their diffusion. Cases in which these kinds of false information are used in political debates are far from unimaginable. In this work, we study the behavior of users supporting different (and opposite) worldviews - i.e. scientific and conspiracist thinking - that commented the posts of the Facebook page of a large italian political party that advocates direct democracy and e-Participation. We find that users supporting different narratives consume political information in a similar way. Moreover, by analyzing the composition of users active on the page in terms of commenting activity, we notice that almost one fifth of them is represented by polarized consumers of conspiracy stories, and those are able to generate almost one third of total comments to the posts of the page.Source: XI Conference of the Italian Chapter of AIS Digital Innovation and Inclusive Knowledge in Times of Change, Genova, Italy, 21-22/11/2014
See at: CNR ExploRA