Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline

Recommender systems constitute the core engine of most social network platforms nowadays, aiming to maximize user satisfaction along with other key business objectives. Twitter is no exception. Despite the fact that Twitter data has been extensively used to understand socioeconomic and political phenomena and user behaviour, the implicit feedback provided by users on Tweets through their engagements on the Home Timeline has only been explored to a limited extent. At the same time, there is a lack of large-scale public social network datasets that would enable the scientific community to both benchmark and build more powerful and comprehensive models that tailor content to user interests. By releasing an original dataset of 160 million Tweets along with engagement information, Twitter aims to address exactly that. During this release, special attention is drawn on maintaining compliance with existing privacy laws. Apart from user privacy, this paper touches on the key challenges faced by researchers and professionals striving to predict user engagements. It further describes the key aspects of the RecSys 2020 Challenge that was organized by ACM RecSys in partnership with Twitter using this dataset.Comment: 16 pages, 2 table

Fact-checking Viral Trends For News Writers

Social media is a constant in day-to-day life and is often the first place news breaks. However, the likelihood of false information being spread across social media is high, and this can affect journalists trying to do their jobs, in both gathering information and trying to achieve balance. Reporters and news writers need to be able to quickly evaluate the legitimacy of social media sources for information, especially viral posts, lest they be accused of spreading “fake news.” This chapter examines how social media has disrupted traditional news reporting and caused media outlets to tackle the audience’s opinion of them as “fake news.” I then explain how posts go “viral,” particularly on Twitter, and what actually causes topics to trend on the platform. To close, there are practical methods students can use to evaluate and factcheck both Twitter accounts and individual tweets. Through the materials in this chapter, student news writers will be armed with the knowledge to evaluate social media posts for veracity, with a concluding learning activity that puts these skills into practice

Automatic User Profile Construction for a Personalized News Recommender System Using Twitter

Modern society has now grown accustomed to reading online or digital news. However, the huge corpus of information available online poses a challenge to users when trying to find relevant articles. A hybrid system “Personalized News Recommender Using Twitter’ has been developed to recommend articles to a user based on the popularity of the articles and also the profile of the user. The hybrid system is a fusion of a collaborative recommender system developed using tweets from the “Twitter” public timeline and a content recommender system based the user’s past interests summarized in their conceptual user profile. In previous work, a user’s profile was built manually by asking the user to explicitly rate his/her interest in a category by entering a score for the corresponding category. This is not a reliable approach as the user may not be able to accurately specify their interest for a category with a number. In this work, an automatic profile builder was developed that uses an implicit approach to build the user’s profile. The specificity of the user profile was also increased to incorporate fifteen categories versus seven in the previous system. We concluded with an experiment to study the impact of automatic profile builder and the increased set of categories on the accuracy of the hybrid news recommender syste

Multi-label Convolution Neural Network for Personalized News Recommendation based on Social Media Mining

Prediction of user’s multi label interests and recommending the users interest based popular news articles through mining the social media are difficult task in Hybrid News Recommendation System (HYPNRS). To overcome this issue, this study proposes a deep learning approach - Multi-label Convolution Neural Network for predicting users' diversified interest in 15 labels using the binary relevance method. Based on labels of user’s interest, the most popular news articles are determined and their labels were clustered by mining social media feeds Facebook and Twitter along with current trends. The reliability of retrieved popular news articles also verified for recommendation. Eventually, the latest news articles catered from news feeds integrated along popular news articles and current trends together provide a recommendation list with respect to user interest. Experimental results show the proposed method diversified users interest labels prediction performance improved 5.87%, 12.09%, and 18.49% with the following state of art Support Vector Machine (SVM), Decision Tree and Naïve Bayes. The recommendation performance concerning users’ interest achieved 90%, 93.3%, 90% with social media feeds Facebook, Twitter and News Feeds accordingly

Opinion Detection, Sentiment Analysis and User Attribute Detection from Online Text Data

With the growing increase in the use of the internet in most parts of the world today, users generate significant amounts of online text on different platforms such as online social networks, product review websites, travel blogs, to name just a few. The variety of content on these platforms has made them an important resource for researchers to gauge user activity, determine their opinions and analyze their behavior, without having to perform monetarily and temporally expensive surveys. Gaining insights into user behavior enables us to better understand their likes and dislikes, which in turn is helpful for economic purposes such as marketing, advertising and recommendations. Further, owing to the fact that online social networks have recently been instrumental in socio-political revolutions such as the Arab Spring, and for awareness-generation campaigns by MoveOn.org and Avaaz.org, analysis of online data can uncover user preferences. The overarching goal of this Ph.D. thesis is to pose some research questions and propose solutions, mostly pertaining to user opinions and attributes, keeping in mind the large quantities of noise present in online textual data. This thesis illustrates that with the extraction of informative textual features and the use of robust NLP and machine learning techniques, it is possible to perform efficient signal extraction from online text data, and use it to better understand user behavior. The first research problem addressed is that of opinion detection and sentiment analysis of users on a given topic, from their self-generated tweets. The key idea is to select relevant hashtags and n-grams using an $l_1$-regularized logistic regression model for opinion detection. The second research problem deals with temporal opinion detection from tweets, i.e., detecting user opinions on a topic in which the conversation evolves over time. For instance, on the widely-discussed topic of Obamacare (the Affordable Care Act in the U.S.), various issues became the focal points of discussion among users over time, as corresponding socio-political events and occurrences took place in real-time. We propose a machine-learning model based on seminal work from the sociological literature that is based on the premise that most opinion changes occur slowly over time. Our model is able to successfully capture opinions over time using publicly available tweets, as well as to uncover the key points of discussion as time progresses. In the third research problem, we utilize distributed representation of words in a method that determines, from user reviews, aspects of products and services that users like and dislike. We harness the contextual similarity between words and effectively build meta-features that capture user sentiment at a granular level. Finally in the fourth research problem, we propose a method to detect the age of users from their publicly available tweets. Using a method based on distributed representation of words and clustering, we are able to achieve high accuracies in age detection, as well as to simultaneously discover topics of conversation in which users of different age groups engage

Personnalisation du contenu et tendances dans les médias sociaux

Fluctuating along user connections, some content succeeds at capturing the attention of a large amount of users and suddenly becomes trending. Understanding trending content and its dynamics is crucial to the explanation of opinion spreading, and to the design of social marketing strategies. While previous research has mostly focused on trending content and on the network structure of individuals in social media, this work complements these studies by exploring in depth the human factors behind the generation of this content. We build upon this analysis to investigate new personalization tools helping individuals to discover interesting social media content. This work contributes to the literature on the following aspects: an in depth analysis on individuals who create trending content in social media that uncovers their distinguishing characteristics; a novel means to identify trending content by relying on the ability of special individuals who create them; a mechanism to build a recommender system to personalize trending content; and techniques to improve the quality of recommendations beyond the core theme of accuracy. Our studies underline the vital role of special users in the creation of trending content in social media. Thanks to such special users and their ``wisdom'', individuals may discover the trending content distilled to their tastes. Our work brings insights in two main research directions - trending content in social media and recommender systems.En fonction des connexions entre utilisateurs de ces réseaux, certains contenus peuvent bénéficier d’une large audience et tout d’un coup se transformer en tendance. Comprendre comment du contenu peut se transformer en tendance est donc crucial pour pouvoir expliquer la propagation des opinions ainsi que pour établir des stratégies de marketing sociale. Les précédentes études se sont concentrées sur les caractéristiques du contenu pouvant se transformer en tendance et sur la structure du réseau d’individus dans les médias sociaux. Ce travail complète ces études en explorant les facteurs humains derrières la génération du contenu tendance. Nous nous appuyons sur cette analyse pour définir de nouveaux outils de personnalisation permettant aux individus de repérer le contenu qui les intéresse dans les médias sociaux. Les contributions de ce travail sont les suivantes:une analyse approfondie des individus créant du contenu tendance dans les médias sociaux ce qui permet de découvrir leurs caractéristiques distinctives; un nouveau moyen d’identifier le contenu tendance en s’appuyant sur la capacité des individus spéciaux qui le créent; un mécanisme d’élaboration de système de recommandation afin de personnaliser le contenu tendance.; et des techniques d’amélioration de la qualité des recommandations allant au-delà de la seule évaluation de la précision. Nos études montrent le rôle vital de certains utilisateurs spéciaux dans la création de contenu tendance dans les médias sociaux. Ces utilisateurs avec leur sagesse permettent aux autres individus de découvrir du contenu tendance à leur goût

Fire Side Chats to TikTok Influencer Tags: The Evolution of White House Computer-Mediated Communication to Youngsters

This study aimed to analyze how young people understood emerging changes in White House communication, specifically that which utilized the collaboration of social media influencers. Convenience sampling was used to recruit participants at an American college. A total of 111 students participated in the study that showed two different videos of social media influencers at the White House, followed by questions that measured credibility, accommodation, and effects of messaging. The videos featured different levels of formality, as well as different aesthetics. Results revealed that these collaborative videos did not produce high levels of credibility, accommodation or gateway effects, but the less informal video prompted greater credibility, accommodation, and gateway effects than the more informal video. Collectively, these results indicate that White House and social media influencer collaborations in their current form as not incredibility persuasive and future research is needed to better understand if and how these collaborations can be altered to be more effective at reaching and influencing young adults

Investigation into the mechanism of catalysis for the kinetic resolution of a,a-Disubstituted y-Hydroxy Esters

The synthesis of enantioenriched, biologically active compounds is a vital area of interest for synthetic chemists. The lactone motif is found in several biologically important molecules, and the development of kinetic resolutions capable of producing enantioenriched lactones is important for the total synthesis of these types of compounds. The chiral Brønsted-acid catalyst, R-TRIP, has proven to be effective in producing enantioenriched a,a-disubstituted lactones with up to 94% ee. A study of the effects of varying electronic substituents on the kinetic resolution of a,a-disubstituted-?-hydroxy esters to produce enantioenriched ?-lactones is the focus of this study. The introduction of varying electron-withdrawing and electron-donating substituents in the para position of an a-Ph group is hypothesized to cause a change in the selectivity and rate of reaction through resonance, induction, hydrogen bonding and pi-stacking. This project seeks to broaden the scope and understanding of the catalyst-substrate interaction to improve upon this synthetic methodology. [This abstract has been edited to remove characters that will not display in this system. Please see the PDF for the full abstract.]]]> 2018 Esterification Esters $x Synthesis Esters$x Synthesis English http://libres.uncg.edu/ir/uncg/f/Wilson_uncg_0154M_12503.pdf oai:libres.uncg.edu/23278 2018-07-10T14:19:31Z UNCG Grabbing him by the tweets: presidential parody as political activism NC DOCKS at The University of North Carolina at Greensboro Wood, Olivia <![CDATA[It has never been easier for presidents to communicate directly with voters. Social media allows world leaders to post messages to their followers anytime, anywhere, without going through the traditional channels of speechwriters or public relations staff. Donald Trump in particular has become famous--and heavily criticized--for his unorthodox use of Twitter. This criticism has taken many forms, including a crop of Trump-themed parody accounts, tweeting in character as some version of the president. Political satire is nothing new, but social media platforms offer a new genre in which to do it. In this paper, I examine the parodic methods of five different Donald Trump parody accounts on Twitter and compare them to the rhetorical style of @realDonaldTrump. Methods of analysis included code frequency comparisons across accounts, code intersection patterns, word and phrase frequency comparisons, interviews with account owners, and comparative ethnography. Donald Trump parody accounts on Twitter sit at the intersections of new forms of presidential communication, new uses of digital media, and new strategies for activism. Analyzing their role at this crossroads necessitates considerations of genre, rhetorical situation, and the affordances of the platform. My research thus contributes to discussions of genre and digital rhetorical theory by examining our current political situation and how rhetors are employing digital strategies in this controversial real world setting. I approach this project with four research questions: 1) In what ways are different accounts parodying the president, and what rhetorical effects do each of these methods have? 2) What elements of the actual president’s real account do the parodies focus on? How do they differ linguistically from each other and from @realDonaldTrump? 3) How do parody accounts fit into the broader set of anti-Trump activism? 4) What political issues do the different accounts highlight, and what can readers gain from them (other than entertainment)? How do parody accounts communicate a message differently than other types of activism? My results provide a rhetorical picture of @realDonaldTrump’s Twitter activity in late May/early June of 2017 alongside the activities of his parodists, showing how the parodists view the president and which political issues the parodists find most important to discuss