Tweeting an Apology Note with Edits

Twitter

Tweeting an Apology Note with Edits

Here is an interesting story from the Washington Post that discusses how a college student received a semester long suspension, which he is currently appealing, for editing his ex-girlfriend’s apology note and then tweeting the note.  I struggle to see how his tweet constitutes cyberbullying or violates Title IX.

WashPo.com: A student graded his ex’s apology note — ‘D minus’ — then tweeted it. He got suspended.

Judge’s Retweet Does not Lead to Recusal

Twitter

Judge’s Retweet Does not Lead to Recusal

A federal appeals court has refused to order the retroactive recusal of a federal judge accused of retweeting a news story about a case after he denied a motion…

ABA JOURNAL.com: Alleged retweet by judge doesn’t warrant retroactive recusal, 9th Circuit rules

Trademarking Hashtags

nkumar7

Naveen Kumar

University of Memphis

193X257_LiangfeiQiu_7789

Liangfei Qiu

University of Florida – Warrington College of Business Administration

 

 

subodha5

Subodha Kumar

Texas A&M University – Mays Business School

A Hashtag is Worth a Thousand Words: An Empirical Investigation of Social Media Strategies in Trademarking Hashtags

Abstract

Firms of all sizes are trying to “join the conversation” on social media platforms, such as Twitter and Facebook, and are increasingly trademarking hashtags related to their products and brands. This new practice of trademarking hashtags has produced an important opportunity for empirical investigation. Trademarking hashtags can be a double-edged sword for firms that want their posts to go viral but also want to protect their brand reputation by restricting the use of their material. This study examines the impact of trademarking a hashtag on a firm’s social media audience engagement. By adopting multiple causal identification strategies to address the issues of self-selected trademarking, we find that trademarking hashtags plays a pivotal role in increasing social media audience engagement and information dissemination. More importantly, this positive effect is stronger for firms with fewer Twitter followers. We also dig deeper into the underlying mechanisms and find that trademarking hashtags makes writing tweets with certain linguistic styles more critical. More specifically, trademarking hashtags can amplify both the contemporaneous and lagged effects of writing tweets with desirable linguistic styles on social media audience engagement. Our findings have direct managerial implications: To maximize the effectiveness of trademarking hashtags, firms should develop the right social media engagement strategies by taking specific communication and linguistic styles into account.

More Bad News for Backpage.com

backpage

WashingtonPost.com: Backpage has always claimed it doesn’t control sex-related ads. New documents show otherwise

Global Internet Forum to Counter Terrorism

cnntech

Global Internet Forum to Counter Terrorism

Twitter, Facebook, Microsoft, and YouTube have teamed up to create the Global Internet Forum to Counter Terrorism.  The tech giants plan to use this forum to work together and share tools to limit terrorism-related content on their platforms.

CNNTech.com: Tech giants bolster collaborative fight against terrorism

 

Facebook, Free Expression and the Power of a Leak

Facebook

Interesting article that compares Facebook’s content policies to U.S. law.

NYTimes.com: Facebook, Free Expression and the Power of a Leak

Taming the Golem: Challenges of Ethical Algorithmic Decision Making

Omer Tene

Omer

Jules Polonetsky

Jules

Taming the Golem: Challenges of Ethical Algorithmic Decision Making

Abstract

The prospect of digital manipulation on major online platforms has reached fever pitch in the last election cycle in the United States. Jonathan Zittrain’s concern about “digital gerrymandering” has found resonance in reports, which were resoundingly denied by Facebook, of the company’s alleged editing content to tone down conservative voices. At the start of the election cycle, critics blasted Facebook for allegedly injecting editorial bias into an apparently neutral content generator, its “Trending Topics” feature. Immediately after the election, when the extent of dissemination of “fake news” through social media became known, commentators chastised Facebook for not proactively policing user generated content to block and remove untrustworthy information. Which one is it then? Should Facebook have deployed policy directed technologies or should its content algorithm have remained policy neutral?

This article examines the potential for bias and discrimination in automated algorithmic decision making. As a group of commentators recently asserted, “The accountability mechanisms and legal standards that govern such decision processes have not kept pace with technology.” Yet the article rejects an approach that depicts every algorithmic process as a “black box,” which is inevitably plagued by bias and potential injustice. While recognizing that algorithms are manmade artifacts written and edited by humans in order to code decision making processes, the article argues that a distinction should be drawn between “policy neutral algorithms,” which lack an active editorial hand, and “policy directed algorithms,” which are intentionally framed to pursue a designer’s policy agenda.

Policy neutral algorithms could in some cases reflect existing entrenched societal biases and historic inequities. Companies, in turn, can choose to fix their results through active social engineering. For example, after facing controversy in light of an algorithmic determination to not offer same-day delivery in low-income neighborhoods, Amazon has nevertheless recently decided to offer the services in order to pursue an agenda of equal opportunity. Recognizing that its decision making process, which was based on logistical factors and expected demand, had the effect of accentuating prevailing social inequality, Amazon chose to level the playing field.

Policy directed algorithms are purposely engineered to correct for apparent bias and discrimination or intentionally designed to advance a predefined policy agenda. In this case, it is essential that companies provide transparency about their active pursuit of editorial policies. For example, if a search engine decides to scrub search results clean of apparent bias and discrimination, it should let users know they are seeing a manicured version of the world. If a service optimizes results for financial motives without alerting users, it risks violating FTC standards for disclosure. So too should service providers consider themselves obligated to prominently disclose important criteria that reflect an unexpected policy agenda. The transparency called for is not one based on revealing source code, but rather public accountability about the editorial nature of the algorithm.

The article addresses questions surrounding the boundaries of responsibility for algorithmic fairness, and analyzes a series of case studies under the proposed framework.

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