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 customisable pipeline for continuously harvesting socially-minded Twitter users

On social media platforms and Twitter in particular, specific classes of users such as influencers have been given satisfactory operational definitions in terms of network and content metrics. Others, for instance online activists, are not less important but their characterisation still requires experimenting. We make the hypothesis that such interesting users can be found within temporally and spatially localised contexts, i.e., small but topical fragments of the network containing interactions about social events or campaigns with a significant footprint on Twitter. To explore this hypothesis, we have designed a continuous user profile discovery pipeline that produces an ever-growing dataset of user profiles by harvesting and analysing contexts from the Twitter stream. The profiles dataset includes key network and content-based users metrics, enabling experimentation with user-defined score functions that characterise specific classes of online users. The paper describes the design and implementation of the pipeline and its empirical evaluation on a case study consisting of healthcare-related campaigns in the UK, showing how it supports the operational definitions of online activism, by comparing three experimental ranking functions. The code is publicly available.Comment: Procs. ICWE 2019, June 2019, Kore

Analyzing the strength of ties of Retweet in health domain

Social Network (SN) is created whenever people interact with other people. Online SN gained considerable popularity in the last years such as Fa- cebook, Twitter and etc Twitter is SN and microblogging service that creates some interesting social network structures - follow relationships. Users follow someone mostly because they share common interests and they may exchange messages called tweets. If a user post a tweet, if their follower like it they repost it or retweet it. In this context, we aim to explore and study the topological structure of user‟s retweet network, as well, new scaling measures based on strength of retweet ties. The findings suggested that relations of “friendship” are important but not enough to find out how important users are. We uncovered other some principles that must be studied like, homophily phenomenon. Ho- mophily explores properties of social network relationships, i.e. the preference for associating with individuals of the same background. Last but not least, it is worth emphasizing that we uncovered a weak correlation between Degree Cen- trality and Betweenness Centrality (49 percent) in Retweet-network and strong correlation between Degree and Betweenness centrality in Follower-network (89 percent). These find suggests that retweet network may have some fractal properties

De retibus socialibus et legibus momenti

Online Social Networks (OSNs) are a cutting edge topic. Almost everybody --users, marketers, brands, companies, and researchers-- is approaching OSNs to better understand them and take advantage of their benefits. Maybe one of the key concepts underlying OSNs is that of influence which is highly related, although not entirely identical, to those of popularity and centrality. Influence is, according to Merriam-Webster, "the capacity of causing an effect in indirect or intangible ways". Hence, in the context of OSNs, it has been proposed to analyze the clicks received by promoted URLs in order to check for any positive correlation between the number of visits and different "influence" scores. Such an evaluation methodology is used in this paper to compare a number of those techniques with a new method firstly described here. That new method is a simple and rather elegant solution which tackles with influence in OSNs by applying a physical metaphor.Comment: Changes made for third revision: Brief description of the dataset employed added to Introduction. Minor changes to the description of preparation of the bit.ly datasets. Minor changes to the captions of Tables 1 and 3. Brief addition in the Conclusions section (future line of work added). Added references 16 and 18. Some typos and grammar polishe

Twitter Analysis of Covid-19 Misinformation in Spain

A graph analysis on the tweets and users networks from a set of curated news was done to study the existing difference in communication patterns between legitimate and misinformation news. Our findings suggest there is no difference in the influence of misinformation and legitimate news but misinformation news tend to be more shared and present than legitimate news, meaning that while misinformation tweets do not have more influence, their authors are more prolific. Misinformation reach wider audience even if the tweets, individually, are not more influential. A subsequent qualitative analysis on the users reveal that there is also influence of misinformation spreading in Spain from other Spanish speaking countries.Peer ReviewedPostprint (author's final draft

You are now an Influencer! Measuring CEO Reputation in Social Media

We know that reputation in organisational contexts can be understood as a valuable asset that requires diligent management. It directly affects how a firm is publicly perceived, and indirectly, how a firm will perform economically. The establishment of social media as ubiquitous tools of communication have changed how corporations manage their reputation. Particularly CEOs face novel responsibilities, as they deal with their personal image, which at the same time affects the reputation of their firm. Whereas CEO and corporate reputation have been researched isolated from each other, little is known about how a CEO’s social media reputation management affects corporate reputation. This research in progress paper aims to emphasise this research gap with a literature review on the current status of reputation management and measurement by means of social media. We further propose a research design that combines sentiment analysis, frequency detection, and content analysis and discuss further research prospects