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Traveling trends: Social butterflies or frequent fliers
- In Proc. of 2013 ACM Conf. on Online Social Networks (COSN’13
, 2013
"... Trending topics are the online conversations that grab collec-tive attention on social media. They are continually chang-ing and often reflect exogenous events that happen in the real world. Trends are localized in space and time as they are driven by activity in specific geographic areas that act a ..."
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Cited by 7 (5 self)
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Trending topics are the online conversations that grab collec-tive attention on social media. They are continually chang-ing and often reflect exogenous events that happen in the real world. Trends are localized in space and time as they are driven by activity in specific geographic areas that act as sources of traffic and information flow. Taken indepen-dently, trends and geography have been discussed in recent literature on online social media; although, so far, little has been done to characterize the relation between trends and geography. Here we investigate more than eleven thousand topics that trended on Twitter in 63 main US locations dur-ing a period of 50 days in 2013. This data allows us to study the origins and pathways of trends, how they com-pete for popularity at the local level to emerge as winners at the country level, and what dynamics underlie their pro-duction and consumption in different geographic areas. We identify two main classes of trending topics: those that sur-face locally, coinciding with three different geographic clus-ters (East coast, Midwest and Southwest); and those that emerge globally from several metropolitan areas, coinciding with the major air traffic hubs of the country. These hubs act as trendsetters, generating topics that eventually trend at the country level, and driving the conversation across the country. This poses an intriguing conjecture, drawing a parallel between the spread of information and diseases: Do trends travel faster by airplane than over the Internet?
Topicality and social impact: Diverse messages but focused messengers. Under review, arXiv 1402.5443
, 2014
"... Are users who comment on a variety of matters more likely to achieve high influence than those who delve into one fo-cused field? Do general Twitter hashtags, such as #lol, tend to be more popular than novel ones, such as #instant-lyinlove? Questions like these demand a way to detect top-ics hidden ..."
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Cited by 2 (2 self)
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Are users who comment on a variety of matters more likely to achieve high influence than those who delve into one fo-cused field? Do general Twitter hashtags, such as #lol, tend to be more popular than novel ones, such as #instant-lyinlove? Questions like these demand a way to detect top-ics hidden behind messages associated with an individual or a hashtag, and a gauge of similarity among these top-ics. Here we develop such an approach to identify clusters of similar hashtags by detecting communities in the hash-tag co-occurrence network. Then the topical diversity of a user’s interests is quantified by the entropy of her hashtags across different topic clusters. A similar measure is applied to hashtags, based on co-occurring tags. We find that high topical diversity of early adopters or co-occurring tags im-plies high future popularity of hashtags. In contrast, low diversity helps an individual accumulate social influence. In short, diverse messages and focused messengers are more likely to gain impact. Categories and Subject Descriptors [Information systems]: Information systems applications— Collaborative and social computing systems and tools, Social networking sites; [Information systems]: World Wide Web—Web applications, Web mining; [Human-centered
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.
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:
