Dancing to the Partisan Beat: A First Analysis of Political Communication on TikTok

TikTok is a video-sharing social networking service, whose popularity is increasing rapidly. It was the world's second-most downloaded app in 2019. Although the platform is known for having users posting videos of themselves dancing, lip-syncing, or showcasing other talents, user-videos expressing political views have seen a recent spurt. This study aims to perform a primary evaluation of political communication on TikTok. We collect a set of US partisan Republican and Democratic videos to investigate how users communicated with each other about political issues. With the help of computer vision, natural language processing, and statistical tools, we illustrate that political communication on TikTok is much more interactive in comparison to other social media platforms, with users combining multiple information channels to spread their messages. We show that political communication takes place in the form of communication trees since users generate branches of responses to existing content. In terms of user demographics, we find that users belonging to both the US parties are young and behave similarly on the platform. However, Republican users generated more political content and their videos received more responses; on the other hand, Democratic users engaged significantly more in cross-partisan discussions.Comment: Accepted as a full paper at the 12th International ACM Web Science Conference (WebSci 2020). Please cite the WebSci version; Second version includes corrected typo

Ventures in Social Media

Academic libraries are actively involved in social media platforms as part of their campus communities. They have moved past the debate of whether to participate in social media and are focusing on strategies to develop engaging content and assessment of their efforts. Social media use in the campus classroom continues to grow with more faculty using social media in academic context. Given the widespread adoption of social media on the University of San Diego campus Copley Library formed a Social Media Committee (SMC) to manage the library’s social media presence with a mission to promoting the library’s services and events. After establishing Facebook and Twitter accounts the committee looked to expand their presence on other platforms. To determine which social media platforms undergraduates were using, the committee designed and administered a survey in the fall of 2013. The survey confirmed that USD undergraduates were still using Facebook and showed 56% now use multiple social media sites: Twitter, Pinterest, Tumblr, and Instagram. The SMC diversified onto Instagram and Pinterest platforms to interact with students on visual platforms.Ye

Your click decides your fate: Inferring Information Processing and Attrition Behavior from MOOC Video Clickstream Interactions

In this work, we explore video lecture interaction in Massive Open Online Courses (MOOCs), which is central to student learning experience on these educational platforms. As a research contribution, we operationalize video lecture clickstreams of students into cognitively plausible higher level behaviors, and construct a quantitative information processing index, which can aid instructors to better understand MOOC hurdles and reason about unsatisfactory learning outcomes. Our results illustrate how such a metric inspired by cognitive psychology can help answer critical questions regarding students' engagement, their future click interactions and participation trajectories that lead to in-video & course dropouts. Implications for research and practice are discusse

Reactions of Generation Y to Luxury Hotel Twitter Promotions

Social media refers to the means of interactions among people in which they create, share, and exchange information and ideas in virtual communities and networks (Merriam-Webster, 2013). Social media marketing refers to the process of gaining website traffic or attention through social media sites (Evans, 2008). In today’s society, social media refers mainly to websites including (but not limited to) Facebook, Twitter, LinkedIn, and Google+. The most popular and fastest growing of these social media venues is Twitter. Twitter was founded in 2006 by Jack Dorsey, Evan Williams, and Biz Stone. Since 2006, almost 200 million users across the globe have joined the site; over 140 million tweets are “tweeted” daily (Picard, 2011). Twitter took advantage of a niche in the market, allowing 140 characters to express an idea or emotion. Twitter has changed the media world as a news source, tweeting real-time information from stories that arise (Picard, 2011). In the lodging industry, methods of social media to promote hotels are becoming more popular. Twitter, in particular, has emerged as a “moment of truth” for a hotel, demonstrating how instantly and tactfully hotels react to the thoughts and opinions of former, current, and potential guests. Studies have also suggested that “online social life mirrors offline relationships in many ways” (Moore, p. 440). Therefore, Twitter accounts should be viewed as an extension of the hospitality business, in particular lodging, echoing the relationship a customer would feel upon arrival to the hotel. Hotel marketing teams have reached “great success by driving demand to hotels through increased online advertising and web optimization” (Chipkin, 2013). This has increased overall customer views of the hotel without affecting the rate strategy of the property or brand. Twitter presence could, potentially, help a patron decide between two hotels, “If a promotion, experience or package is unique, it definitely works to generate bookings and helps put you first in a consumer’s mind when they are choosing between two or three hotels,” says Rachel Harrison of Hyatt Andaz (Chipkin, 2013). Hotel companies worldwide are investing in their social media networks. Certain hotels (i.e. W Barcelona) are even hiring social media and marketing managers whose responsibilities include instant Twitter feedback (Appendix 1). The purpose behind this investment is to maximize these social media accounts, creating feedback from all potential guests, allowing them to react to both positive and negative word of mouth. Social media managers have recently encountered an opportunity; Generation Y is becoming a target demographic. As Generation Y enters the workforce and begins a career, the exposure to hotel brands and types will increase. Luxury hotel stays are becoming more financially reachable to these Generation Y guests because of their career advancements (Fields, 2013). This study will serve to evaluate the added benefits from the adoption of social media channels, particularly Twitter

Platformer level design for player believability

Player believability is often defined as the ability of a game playing character to convince an observer that it is being controlled by a human. The agent's behavior is often assumed to be the main contributor to the character's believability. In this paper we reframe this core assumption and instead focus on the impact of the game environment and aspects of game design (such as level design) on the believability of the game character. To investigate the relationship between game content and believability we crowdsource rank-based annotations from subjects that view playthrough videos of various AI and human controlled agents in platformer levels of dissimilar characteristics. For this initial study we use a variant of the well-known Super Mario Bros game. We build support vector machine models of reported believability based on gameplay and level features which are extracted from the videos. The highest performing model predicts perceived player believability of a character with an accuracy of 73.31%, on average, and implies a direct relationship between level features and player believability.We would like to thank all participants of the crowdsourcing experiment. This work has been supported in part by the FP7 Marie Curie CIG project AutoGameDesign (630665).peer-reviewe

Video advertisement mining for predicting revenue using random forest

Shaken by the threat of financial crisis in 2008, industries began to work on the topic of predictive analytics to efficiently control inventory levels and minimize revenue risks. In this third-generation age of web-connected data, organizations emphasized the importance of data science and leveraged the data mining techniques for gaining a competitive edge. Consider the features of Web 3.0, where semantic-oriented interaction between humans and computers can offer a tailored service or product to meet consumers\u27 needs by means of learning their preferences. In this study, we concentrate on the area of marketing science to demonstrate the correlation between TV commercial advertisements and sales achievement. Through different data mining and machine-learning methods, this research will come up with one concrete and complete predictive framework to clarify the effects of word of mouth by using open data sources from YouTube. The uniqueness of this predictive model is that we adopt the sentiment analysis as one of our predictors. This research offers a preliminary study on unstructured marketing data for further business use