7:00 – 5:00 ASRC MKT
- Online, interactive LaTex text editor!!!
- Trust and Distrust in Online Fact-Checking Services
- Debunking: A Meta-Analysis of the Psychological Efficacy of Messages Countering Misinformation
- This meta-analysis investigated the factors underlying effective messages to counter attitudes and beliefs based on misinformation. Because misinformation can lead to poor decisions about consequential matters and is persistent and difficult to correct, debunking it is an important scientific and public-policy goal. This meta-analysis (k = 52, N = 6,878) revealed large effects for presenting misinformation (ds = 2.41–3.08), debunking (ds = 1.14–1.33), and the persistence of misinformation in the face of debunking (ds = 0.75–1.06). Persistence was stronger and the debunking effect was weaker when audiences generated reasons in support of the initial misinformation. A detailed debunking message correlated positively with the debunking effect. Surprisingly, however, a detailed debunking message also correlated positively with the misinformation-persistence effect.
- The Impact of Crowds on News Engagement: A Reddit Case Study
- Today, users are reading the news through social platforms. These platforms are built to facilitate crowd engagement, but not necessarily disseminate useful news to inform the masses. Hence, the news that is highly engaged with may not be the news that best informs. While predicting news popularity has been well studied, it has not been studied in the context of crowd manipulations. In this paper, we provide some preliminary results to a longer term project on crowd and platform manipulations of news and news popularity. In particular, we choose to study known features for predicting news popularity and how those features may change on reddit.com, a social platform used commonly for news aggregation. Along with this, we explore ways in which users can alter the perception of news through changing the title of an article. We find that news on reddit is predictable using previously studied sentiment and content features and that posts with titles changed by reddit users tend to be more popular than posts with the original article title.
- fakenewsdata1
This repository contains two independent news datasets used in the 2017 study: “This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire t…
- PHEME dataset of rumours and non-rumours
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This dataset contains a collection of Twitter rumours and non-rumours posted during breaking news. The five breaking news provided with the dataset are as follows:* Charlie Hebdo: 458 rumours (22.0%) and 1,621 non-rumours (78.0%).* Ferguson: 284 rumours (24.8%) and 859 non-rumours (75.2%).* Germanwings Crash: 238 rumours (50.7%) and 231 non-rumours (49.3%).* Ottawa Shooting: 470 rumours (52.8%) and 420 non-rumours (47.2%).* Sydney Siege: 522 rumours (42.8%) and 699 non-rumours (57.2%).
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SocInfo2017 Programme. Lots of good stuff
- Christina Ting. Compression-Based Algorithms for Deception Detection
- Jennifer Golbeck. The President on Twitter: A Characterization Study of @realDonaldTrump
- Laura Wendlandt. Multimodal Analysis and Prediction of Latent User Dimensions
- Babak Heydari. Why Groups Show Different Fairness Norms? The Interaction Topology Might Explain
- Agus Sulistya. Inferring Spread of Readers’ Emotion Affected by Online News
- Juan Sabuco. ABCE: A Python Library for Economic Agent-Based Modeling
- Jaroslaw Jankowski. Seeds Buffering for Information Spreading Processes
- Arkaitz Zubiaga. Exploiting Context for Rumour Detection in Social Media
- Jisun An. Multidimensional Analysis of the News Consumption of Different Demographic Groups on a Nationwide Scale
- David Jurgens. An Analysis of Individuals’ Behavior Change in Online Groups
- Roy Lee. GitHub and Stack Overflow: Analyzing Developer Interests Across Multiple Social Collaborative Platforms
- Tu Nguyen. On Early-Stage Debunking Rumors on Twitter: Leveraging the Wisdom of Weak Learners
- Brian Pickering. Mediated Behavioural Change in Human-Machine Networks: Exploring Network Characteristics, Trust and Motivation
- Jon Roozenbeek. I Read It on Reddit: Exploring the Role of Online Communities in the 2016 US Elections News Cycle
- Nick Y. Zhang. What Can Software Tell Us About Media Coverage and Public Opinion?
- Ok, enough surfing, back to writing
- Had to install MikTex and Texstudio to get at my papers. I thought I had done this on this computer but I guess not…
- Got the major pieces copied over from PhysRevE. Methods needs a lot of work
- Good progress! Done through the lit review, I made a scary looking equation, and the methods and results sections are shaping up.
