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Online popularity and topical interests through the lens of Instagram
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, 2014
"... Online socio-technical systems can be studied as proxy of the real world to investigate human behavior and social in-teractions at scale. Here we focus on Instagram, a media-sharing online platform whose popularity has been rising up to gathering hundred millions users. Instagram exhibits a mixture ..."
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Online socio-technical systems can be studied as proxy of the real world to investigate human behavior and social in-teractions at scale. Here we focus on Instagram, a media-sharing online platform whose popularity has been rising up to gathering hundred millions users. Instagram exhibits a mixture of features including social structure, social tag-ging and media sharing. The network of social interac-tions among users models various dynamics including fol-lower/followee relations and users ’ communication by means of posts/comments. Users can upload and tag media such as photos and pictures, and they can “like ” and comment each piece of information on the platform. In this work we inves-tigate three major aspects on our Instagram dataset: (i) the structural characteristics of its network of heterogeneous in-teractions, to unveil the emergence of self organization and topically-induced community structure; (ii) the dynamics of content production and consumption, to understand how global trends and popular users emerge; (iii) the behavior of users labeling media with tags, to determine how they de-vote their attention and to explore the variety of their topical interests. Our analysis provides clues to understand human behavior dynamics on socio-technical systems, specifically users and content popularity, the mechanisms of users ’ in-teractions in online environments and how collective trends emerge from individuals ’ topical interests. 1.
Evolution of Online User Behavior During a Social
"... Social media represent powerful tools of mass communica-tion and information diffusion. They played a pivotal role during recent social uprisings and political mobilizations across the world. Here we present a study of the Gezi Park movement in Turkey through the lens of Twitter. We an-alyze over 2. ..."
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Social media represent powerful tools of mass communica-tion and information diffusion. They played a pivotal role during recent social uprisings and political mobilizations across the world. Here we present a study of the Gezi Park movement in Turkey through the lens of Twitter. We an-alyze over 2.3 million tweets produced during the 25 days of protest occurred between May and June 2013. We first characterize the spatio-temporal nature of the conversation about the Gezi Park demonstrations, showing that similar-ity in trends of discussion mirrors geographic cues. We then describe the characteristics of the users involved in this con-versation and what roles they played. We study how roles and individual influence evolved during the period of the up-heaval. This analysis reveals that the conversation becomes more democratic as events unfold, with a redistribution of influence over time in the user population. We conclude by observing how the online and offline worlds are tightly in-tertwined, showing that exogenous events, such as political speeches or police actions, affect social media conversations and trigger changes in individual behavior.
Connecting Dream Networks Across Cultures
"... Many species dream, yet there remain many open research questions in the study of dreams. The symbolism of dreams and their interpretation is present in cultures throughout history. Analysis of online data sources for dream interpreta-tion using network science leads to understanding symbolism in dr ..."
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Many species dream, yet there remain many open research questions in the study of dreams. The symbolism of dreams and their interpretation is present in cultures throughout history. Analysis of online data sources for dream interpreta-tion using network science leads to understanding symbolism in dreams and their associated meaning. In this study, we introduce dream interpretation networks for English, Chi-nese and Arabic that represent different cultures from var-ious parts of the world. We analyze communities in these networks, finding that symbols within a community are se-mantically related. The central nodes in communities give insight about cultures and symbols in dreams. The commu-nity structure of different networks highlights cultural sim-ilarities and differences. Interconnections between different networks are also identified by translating symbols from dif-ferent languages into English. Structural correlations across networks point out relationships between cultures. Simi-larities between network communities are also investigated by analysis of sentiment in symbol interpretations. We find that interpretations within a community tend to have similar sentiment. Furthermore, we cluster communities based on their sentiment, yielding three main categories of positive, negative, and neutral dream symbols.
Contents
, 2014
"... Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavi ..."
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Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.
1 How to Improve Group Homogeneity in Online Social Networks
"... Abstract — The formation and evolution of interest groups in Online Social Networks is driven by both the users ’ preferences and the choices of the groups ’ administrators. In this context, the notion of homogeneity of a social group is crucial: it accounts for determining the mutual similarity amo ..."
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Abstract — The formation and evolution of interest groups in Online Social Networks is driven by both the users ’ preferences and the choices of the groups ’ administrators. In this context, the notion of homogeneity of a social group is crucial: it accounts for determining the mutual similarity among the members of a group and it’s often regarded as fundamental to determine the satisfaction of group members. In this paper we propose a group homogeneity measure that takes into account behavioral information of users, and an algorithm to optimize such a measure in a social network scenario by matching users and groups profiles. We provide an advantageous formulation of such framework by means of a fully-distributed multi-agent system. Experiments on simulated social network data clearly highlight the performance improvement brought by our approach.
Clustering memes in social media streams
"... The problem of clustering content in social media has pervasive appli-cations, including the identification of discussion topics, event detection, and content recommendation. Here we describe a streaming framework for online detection and clustering of memes in social media, specifically Twitter. A ..."
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The problem of clustering content in social media has pervasive appli-cations, including the identification of discussion topics, event detection, and content recommendation. Here we describe a streaming framework for online detection and clustering of memes in social media, specifically Twitter. A pre-clustering procedure, namely protomeme detection, first isolates atomic tokens of information carried by the tweets. Protomemes are thereafter aggregated, based on multiple similarity measures, to obtain memes as cohesive groups of tweets reflecting actual concepts or topics of discussion. The clustering algorithm takes into account various dimen-sions of the data and metadata, including natural language, the social network, and the patterns of information diffusion. As a result, our sys-tem can build clusters of semantically, structurally, and topically related tweets. The clustering process is based on a variant of Online K-means that incorporates a memory mechanism, used to “forget ” old memes and replace them over time with the new ones. The evaluation of our frame-work is carried out by using a dataset of Twitter trending topics. Over a one-week period, we systematically determined whether our algorithm was able to recover the trending hashtags. We show that the proposed method outperforms baseline algorithms that only use content features, as well as a state-of-the-art event detection method that assumes full knowledge of the underlying follower network. We finally show that our online learning framework is flexible, due to its independence of the adopted clustering algorithm, and best suited to work in a streaming scenario. 1
Web Data Extraction, Applications and Techniques: A Survey
"... Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavi ..."
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Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains. Keywords:
