Large scale homophily analysis in twitter using a twixonomy

In this paper we perform a large-scale homophily analysis on Twitter using a hierarchical representation of users' interests which we call a Twixonomy. In order to build a population, community, or single-user Twixonomy we first associate "topical" friends in users' friendship lists (i.e. friends representing an interest rather than a social relation between peers) with Wikipedia categories. A wordsense disambiguation algorithm is used to select the appropriate wikipage for each topical friend. Starting from the set of wikipages representing "primitive" interests, we extract all paths connecting these pages with topmost Wikipedia category nodes, and we then prune the resulting graph G efficiently so as to induce a direct acyclic graph. This graph is the Twixonomy. Then, to analyze homophily, we compare different methods to detect communities in a peer friends Twitter network, and then for each community we compute the degree of homophily on the basis of a measure of pairwise semantic similarity. We show that the Twixonomy provides a means for describing users' interests in a compact and readable way and allows for a fine-grained homophily analysis. Furthermore, we show that midlow level categories in the Twixonomy represent the best balance between informativeness and compactness of the representation

A Topic Recommender for Journalists

The way in which people acquire information on events and form their own opinion on them has changed dramatically with the advent of social media. For many readers, the news gathered from online sources become an opportunity to share points of view and information within micro-blogging platforms such as Twitter, mainly aimed at satisfying their communication needs. Furthermore, the need to deepen the aspects related to news stimulates a demand for additional information which is often met through online encyclopedias, such as Wikipedia. This behaviour has also influenced the way in which journalists write their articles, requiring a careful assessment of what actually interests the readers. The goal of this paper is to present a recommender system, What to Write and Why, capable of suggesting to a journalist, for a given event, the aspects still uncovered in news articles on which the readers focus their interest. The basic idea is to characterize an event according to the echo it receives in online news sources and associate it with the corresponding readers’ communicative and informative patterns, detected through the analysis of Twitter and Wikipedia, respectively. Our methodology temporally aligns the results of this analysis and recommends the concepts that emerge as topics of interest from Twitter and Wikipedia, either not covered or poorly covered in the published news articles

Regarding "Easy alternatives to difficult clamping of distal vessels of the leg"

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Can Twitter be a source of information on allergy? Correlation of pollen counts with tweets reporting symptoms of allergic rhinoconjunctivitis and names of antihistamine drugs

Pollen forecasts are in use everywhere to inform therapeutic decisions for patients with allergic rhinoconjunctivitis (ARC). We exploited data derived from Twitter in order to identify tweets reporting a combination of symptoms consistent with a case definition of ARC and those reporting the name of an antihistamine drug. In order to increase the sensitivity of the system, we applied an algorithm aimed at automatically identifying jargon expressions related to medical terms. We compared weekly Twitter trends with National Allergy Bureau weekly pollen counts derived from US stations, and found a high correlation of the sum of the total pollen counts from each stations with tweets reporting ARC symptoms (Pearson's correlation coefficient: 0.95) and with tweets reporting antihistamine drug names (Pearson's correlation coefficient: 0.93). Longitude and latitude of the pollen stations affected the strength of the correlation. Twitter and other social networks may play a role in allergic disease surveillance and in signaling drug consumptions trends

Chapter Analysis and representation for Digital Humanities: la Mappa Mosaico di Madaba. Digitalizzazione, analisi, decostruzione

The 43rd UID conference, held in Genova, takes up the theme of ‘Dialogues’ as practice and debate on many fundamental topics in our social life, especially in these complex and not yet resolved times. The city of Genova offers the opportunity to ponder on the value of comparison and on the possibilities for the community, naturally focused on the aspects that concern us, as professors, researchers, disseminators of knowledge, or on all the possibile meanings of the discipline of representation and its dialogue with ‘others’, which we have broadly catalogued in three macro areas: History, Semiotics, Science / Technology. Therefore, “dialogue” as a profitable exchange based on a common language, without which it is impossible to comprehend and understand one another; and the graphic sign that connotes the conference is the precise transcription of this concept: the title ‘translated’ into signs, derived from the visual alphabet designed for the visual identity of the UID since 2017. There are many topics which refer to three macro sessions: - Witnessing (signs and history) - Communicating (signs and semiotics) - Experimenting (signs and sciences) Thanks to the different points of view, an exceptional resource of our disciplinary area, we want to try to outline the prevailing theoretical-operational synergies, the collaborative lines of an instrumental nature, the recent updates of the repertoires of images that attest and nourish the relations among representation, history, semiotics, sciences