Aggregating Content and Network Information to Curate Twitter User Lists

Twitter introduced user lists in late 2009, allowing users to be grouped according to meaningful topics or themes. Lists have since been adopted by media outlets as a means of organising content around news stories. Thus the curation of these lists is important - they should contain the key information gatekeepers and present a balanced perspective on a story. Here we address this list curation process from a recommender systems perspective. We propose a variety of criteria for generating user list recommendations, based on content analysis, network analysis, and the "crowdsourcing" of existing user lists. We demonstrate that these types of criteria are often only successful for datasets with certain characteristics. To resolve this issue, we propose the aggregation of these different "views" of a news story on Twitter to produce more accurate user recommendations to support the curation process

Harnessing Collaborative Technologies: Helping Funders Work Together Better

This report was produced through a joint research project of the Monitor Institute and the Foundation Center. The research included an extensive literature review on collaboration in philanthropy, detailed analysis of trends from a recent Foundation Center survey of the largest U.S. foundations, interviews with 37 leading philanthropy professionals and technology experts, and a review of over 170 online tools.The report is a story about how new tools are changing the way funders collaborate. It includes three primary sections: an introduction to emerging technologies and the changing context for philanthropic collaboration; an overview of collaborative needs and tools; and recommendations for improving the collaborative technology landscapeA "Key Findings" executive summary serves as a companion piece to this full report

Online social media in the Syria conflict: encompassing the extremes and the in-betweens

The Syria conflict has been described as the most socially mediated in history, with online social media playing a particularly important role. At the same time, the ever-changing landscape of the conflict leads to difficulties in applying analytical approaches taken by other studies of online political activism. Therefore, in this paper, we use an approach that does not require strong prior assumptions or the proposal of an advance hypothesis to analyze Twitter and YouTube activity of a range of protagonists to the conflict, in an attempt to reveal additional insights into the relationships between them. By means of a network representation that combines multiple data views, we uncover communities of accounts falling into four categories that broadly reflect the situation on the ground in Syria. A detailed analysis of selected communities within the anti-regime categories is provided, focusing on their central actors, preferred online platforms, and activity surrounding “real world” events. Our findings indicate that social media activity in Syria is considerably more convoluted than reported in many other studies of online political activism, suggesting that alternative analytical approaches can play an important role in this type of scenario

Social Turing Tests: Crowdsourcing Sybil Detection

As popular tools for spreading spam and malware, Sybils (or fake accounts) pose a serious threat to online communities such as Online Social Networks (OSNs). Today, sophisticated attackers are creating realistic Sybils that effectively befriend legitimate users, rendering most automated Sybil detection techniques ineffective. In this paper, we explore the feasibility of a crowdsourced Sybil detection system for OSNs. We conduct a large user study on the ability of humans to detect today's Sybil accounts, using a large corpus of ground-truth Sybil accounts from the Facebook and Renren networks. We analyze detection accuracy by both "experts" and "turkers" under a variety of conditions, and find that while turkers vary significantly in their effectiveness, experts consistently produce near-optimal results. We use these results to drive the design of a multi-tier crowdsourcing Sybil detection system. Using our user study data, we show that this system is scalable, and can be highly effective either as a standalone system or as a complementary technique to current tools

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

An Open-Source Strategy for Documenting Events: The Case Study of the 42nd Canadian Federal Election on Twitter

This work is licensed and made available under Creative Commons Attribution 3.0 United States license. Article first appeared in Code4Lib Journal, issue 32, 2016-04-25, Original available here http://journal.code4lib.org/articles/11358This article examines the tools, approaches, collaboration, and findings of the Web Archives for Historical Research Group around the capture and analysis of about 4 million tweets during the 2015 Canadian Federal Election. We hope that national libraries and other heritage institutions will find our model useful as they consider how to capture, preserve, and analyze ongoing events using Twitter. While Twitter is not a representative sample of broader society – Pew research shows in their study of US users that it skews young, college-educated, and affluent (above $50,000 household income) – Twitter still represents an exponential increase in the amount of information generated, retained, and preserved from 'everyday' people. Therefore, when historians study the 2015 federal election, Twitter will be a prime source. On August 3, 2015, the team initiated both a Search API and Stream API collection with twarc, a tool developed by Ed Summers, using the hashtag #elxn42. The hashtag referred to the election being Canada's 42nd general federal election (hence 'election 42' or elxn42). Data collection ceased on November 5, 2015, the day after Justin Trudeau was sworn in as the 42nd Prime Minister of Canada. We collected for a total of 102 days, 13 hours and 50 minutes. To analyze the data set, we took advantage of a number of command line tools, utilities that are available within twarc, twarc-report, and jq. In accordance with the Twitter Developer Agreement & Policy, and after ethical deliberations discussed below, we made the tweet IDs and other derivative data available in a data repository. This allows other people to use our dataset, cite our dataset, and enhance their own research projects by drawing on #elxn42 tweets. Our analytics included: breaking tweet text down by day to track change over time; client analysis, allowing us to see how the scale of mobile devices affected medium interactions; URL analysis, comparing both to Archive-It collections and the Wayback Availability API to add to our understanding of crawl completeness; and image analysis, using an archive of extracted images. Our article introduces our collecting work, ethical considerations, the analysis we have done, and provides a framework for other collecting institutions to do similar work with our off-the-shelf open-source tools. We conclude by ruminating about connecting Twitter archiving with a broader web archiving strategy.Social Sciences and Humanities Research Council of Canada || Insight Grant (435-2015-0011