56,857 research outputs found
Detection of Trending Topic Communities: Bridging Content Creators and Distributors
The rise of a trending topic on Twitter or Facebook leads to the temporal
emergence of a set of users currently interested in that topic. Given the
temporary nature of the links between these users, being able to dynamically
identify communities of users related to this trending topic would allow for a
rapid spread of information. Indeed, individual users inside a community might
receive recommendations of content generated by the other users, or the
community as a whole could receive group recommendations, with new content
related to that trending topic. In this paper, we tackle this challenge, by
identifying coherent topic-dependent user groups, linking those who generate
the content (creators) and those who spread this content, e.g., by
retweeting/reposting it (distributors). This is a novel problem on
group-to-group interactions in the context of recommender systems. Analysis on
real-world Twitter data compare our proposal with a baseline approach that
considers the retweeting activity, and validate it with standard metrics.
Results show the effectiveness of our approach to identify communities
interested in a topic where each includes content creators and content
distributors, facilitating users' interactions and the spread of new
information.Comment: 9 pages, 4 figures, 2 tables, Hypertext 2017 conferenc
Detecting Real-World Influence Through Twitter
In this paper, we investigate the issue of detecting the real-life influence
of people based on their Twitter account. We propose an overview of common
Twitter features used to characterize such accounts and their activity, and
show that these are inefficient in this context. In particular, retweets and
followers numbers, and Klout score are not relevant to our analysis. We thus
propose several Machine Learning approaches based on Natural Language
Processing and Social Network Analysis to label Twitter users as Influencers or
not. We also rank them according to a predicted influence level. Our proposals
are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art
ranking methods.Comment: 2nd European Network Intelligence Conference (ENIC), Sep 2015,
Karlskrona, Swede
Search Bias Quantification: Investigating Political Bias in Social Media and Web Search
Users frequently use search systems on the Web as well as online social media to learn about ongoing events and public opinion on personalities. Prior studies have shown that the top-ranked results returned by these search engines can shape user opinion about the topic (e.g., event or person) being searched. In case of polarizing topics like politics, where multiple competing perspectives exist, the political bias in the top search results can play a significant role in shaping public opinion towards (or away from) certain perspectives. Given the considerable impact that search bias can have on the user, we propose a generalizable search bias quantification framework that not only measures the political bias in ranked list output by the search system but also decouples the bias introduced by the different sources—input data and ranking system. We apply our framework to study the political bias in searches related to 2016 US Presidential primaries in Twitter social media search and find that both input data and ranking system matter in determining the final search output bias seen by the users. And finally, we use the framework to compare the relative bias for two popular search systems—Twitter social media search and Google web search—for queries related to politicians and political events. We end by discussing some potential solutions to signal the bias in the search results to make the users more aware of them.publishe
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
Measuring internet activity: a (selective) review of methods and metrics
Two Decades after the birth of the World Wide Web, more than two billion people around the world are Internet users. The digital landscape is littered with hints that the affordances of digital communications are being leveraged to transform life in profound and important ways. The reach and influence of digitally mediated activity grow by the day and touch upon all aspects of life, from health, education, and commerce to religion and governance. This trend demands that we seek answers to the biggest questions about how digitally mediated communication changes society and the role of different policies in helping or hindering the beneficial aspects of these changes. Yet despite the profusion of data the digital age has brought upon us—we now have access to a flood of information about the movements, relationships, purchasing decisions, interests, and intimate thoughts of people around the world—the distance between the great questions of the digital age and our understanding of the impact of digital communications on society remains large. A number of ongoing policy questions have emerged that beg for better empirical data and analyses upon which to base wider and more insightful perspectives on the mechanics of social, economic, and political life online. This paper seeks to describe the conceptual and practical impediments to measuring and understanding digital activity and highlights a sample of the many efforts to fill the gap between our incomplete understanding of digital life and the formidable policy questions related to developing a vibrant and healthy Internet that serves the public interest and contributes to human wellbeing. Our primary focus is on efforts to measure Internet activity, as we believe obtaining robust, accurate data is a necessary and valuable first step that will lead us closer to answering the vitally important questions of the digital realm. Even this step is challenging: the Internet is difficult to measure and monitor, and there is no simple aggregate measure of Internet activity—no GDP, no HDI. In the following section we present a framework for assessing efforts to document digital activity. The next three sections offer a summary and description of many of the ongoing projects that document digital activity, with two final sections devoted to discussion and conclusions
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