Extractive Adversarial Networks: High-Recall Explanations for Identifying Personal Attacks in Social Media Posts

We introduce an adversarial method for producing high-recall explanations of neural text classifier decisions. Building on an existing architecture for extractive explanations via hard attention, we add an adversarial layer which scans the residual of the attention for remaining predictive signal. Motivated by the important domain of detecting personal attacks in social media comments, we additionally demonstrate the importance of manually setting a semantically appropriate `default' behavior for the model by explicitly manipulating its bias term. We develop a validation set of human-annotated personal attacks to evaluate the impact of these changes.Comment: Accepted to EMNLP 2018 Code and data available at https://github.com/shcarton/rcn

The Internet and Democratic Debate

Presents findings from a survey conducted in June 2004. Looks at the role of the Internet in providing a wider awareness of political views during the 2004 campaign season

Lifetime Adherence to Physical Activity Recommendations and Fall Occurrence in Community-dwelling Older Adults: a Retrospective Cohort Study

Falling is a major health concern for community-dwelling older adults. Regular physical activity has been proposed to prevent falls. The aim of this study was to assess whether the achievement of the 2004 UK Department of Health physical activity recommendations over a lifetime had a protective effect against falling in older people. 313 community-dwelling older adults completed a questionnaire about lifetime physical activity and fall occurrence. There were significantly fewer falls in those who had led an active lifestyle compared to those who had not (χ2Yates=4.568, p=0.033), with a lower relative risk of fall occurrence for the active respondents (RR=0.671) compared to the inactive (RR=1.210). Of those who were sufficiently active in their early adulthood, the decade where there was the biggest decrease in remaining active enough was in the 60s. It is concluded that an active lifestyle may have decreased the likelihood of having a fall in older ag

Zoning Speech on the Internet: A Legal and Technical Model

Speech, it is said, divides into three sorts - (1) speech that everyone has a right to (political speech, speech about public affairs); (2) speech that no one has a right to (obscene speech, child porn); and (3) speech that some have a right to but others do not (in the United States, Ginsberg speech, or speech that is harmful to minors, to which adults have a right but kids do not). Speech-protective regimes, on this view, are those where category (1) speech predominates; speech-repressive regimes are those where categories (2) and (3) prevail. This divide has meaning for speech and regulation within a single jurisdiction, but it makes less sense across jurisdictions. For when viewed across jurisdictions, most controversial speech falls into category (3) - speech that is permitted to some in some places, but not to others in other places. What constitutes political speech in the United States (Nazi speech) is banned in Germany; what constitutes obscene speech in Tennessee is permitted in Holland; what constitutes porn in Japan is child porn in the United States; what is harmful to minors in Bavaria is Disney in New York. Every jurisdiction controls access to some speech - what we call mandatory access controls - but what that speech is differs from jurisdiction to jurisdiction. This diversity creates a problem (for governments at least) when we consider speech within cyberspace. Within cyberspace, mandated access controls are extremely difficult. If access control requires knowing (a) the identities of the speaker and receiver, (b) the jurisdictions of the speaker and receiver, and (c) the content of the speech at issue, then as cyberspace was initially designed, none of these data are easily determined. As a result, real space laws do not readily translate into the context of cyberspace

Cross-Partisan Discussions on YouTube: Conservatives Talk to Liberals but Liberals Don't Talk to Conservatives

We present the first large-scale measurement study of cross-partisan discussions between liberals and conservatives on YouTube, based on a dataset of 274,241 political videos from 973 channels of US partisan media and 134M comments from 9.3M users over eight months in 2020. Contrary to a simple narrative of echo chambers, we find a surprising amount of cross-talk: most users with at least 10 comments posted at least once on both left-leaning and right-leaning YouTube channels. Cross-talk, however, was not symmetric. Based on the user leaning predicted by a hierarchical attention model, we find that conservatives were much more likely to comment on left-leaning videos than liberals on right-leaning videos. Secondly, YouTube's comment sorting algorithm made cross-partisan comments modestly less visible; for example, comments from conservatives made up 26.3% of all comments on left-leaning videos but just over 20% of the comments were in the top 20 positions. Lastly, using Perspective API's toxicity score as a measure of quality, we find that conservatives were not significantly more toxic than liberals when users directly commented on the content of videos. However, when users replied to comments from other users, we find that cross-partisan replies were more toxic than co-partisan replies on both left-leaning and right-leaning videos, with cross-partisan replies being especially toxic on the replier's home turf.Comment: Accepted into ICWSM 2021, the code and datasets are publicly available at https://github.com/avalanchesiqi/youtube-crosstal

How to Train Your YouTube Recommender to Avoid Unwanted Videos

YouTube provides features for users to indicate disinterest when presented with unwanted recommendations, such as the "Not interested" and "Don't recommend channel" buttons. These buttons are purported to allow the user to correct "mistakes" made by the recommendation system. Yet, relatively little is known about the empirical efficacy of these buttons. Neither is much known about users' awareness of and confidence in them. To address these gaps, we simulated YouTube users with sock puppet agents. Each agent first executed a "stain phase", where it watched many videos of one assigned topic; it then executed a "scrub phase", where it tried to remove recommendations of the assigned topic. Each agent repeatedly applied a single scrubbing strategy, either indicating disinterest in one of the videos visited in the stain phase (disliking it or deleting it from the watch history), or indicating disinterest in a video recommended on the homepage (clicking the "not interested" or "don't recommend channel" button or opening the video and clicking the dislike button). We found that the stain phase significantly increased the fraction of the recommended videos dedicated to the assigned topic on the user's homepage. For the scrub phase, using the "Not interested" button worked best, significantly reducing such recommendations in all topics tested, on average removing 88% of them. Neither the stain phase nor the scrub phase, however, had much effect on videopage recommendations. We also ran a survey (N = 300) asking adult YouTube users in the US whether they were aware of and used these buttons before, as well as how effective they found these buttons to be. We found that 44% of participants were not aware that the "Not interested" button existed. However, those who were aware of this button often used it to remove unwanted recommendations (82.8%) and found it to be modestly effective (3.42 out of 5).Comment: Accepted into ICWSM 2024, the code is publicly available at https://github.com/avliu-um/youtube-disinteres

When Do People Trust Their Social Groups?

Trust facilitates cooperation and supports positive outcomes in social groups, including member satisfaction, information sharing, and task performance. Extensive prior research has examined individuals' general propensity to trust, as well as the factors that contribute to their trust in specific groups. Here, we build on past work to present a comprehensive framework for predicting trust in groups. By surveying 6,383 Facebook Groups users about their trust attitudes and examining aggregated behavioral and demographic data for these individuals, we show that (1) an individual's propensity to trust is associated with how they trust their groups, (2) smaller, closed, older, more exclusive, or more homogeneous groups are trusted more, and (3) a group's overall friendship-network structure and an individual's position within that structure can also predict trust. Last, we demonstrate how group trust predicts outcomes at both individual and group level such as the formation of new friendship ties.Comment: CHI 201

The Influence Limiter: Provably Manipulation-Resistant Recommender Systems (Appendix)

Appendix containing proofs omitted from Resnick and Sami,"The Influence Limiter: Provably Manipulation-Resistant Recommender Systems", ACM Recommender Systems Conference 2007.http://deepblue.lib.umich.edu/bitstream/2027.42/55415/1/recsys-appendix.pd

Protocols for automated negotiations with buyer anonymity and seller reputations

In many Internet commerce applications buyers can easily achieve anonymity, limiting what a seller can learn about any buyer individually. However, because sellers need to keep a fixed web address, buyers can probe them repeatedly or pool their information about sellers with the information obtained by other buyers; hence, sellers' strategies become public knowledge. Under assumptions of buyer anonymity, publicly‐known seller strategies, and no negotiation transaction costs for buyers, we find that take‐it‐or‐leave‐it offers will yield at least as much seller profit as any attempt at price discrimination could yield. As we relax those assumptions, however, we find that sellers, and in some cases buyers as well, may benefit from a more general bargaining protocol.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45434/1/11066_2004_Article_329139.pd