Learning Analytics Dashboard for Teaching with Twitter

As social media takes root in our society, more University instructors are incorporating platforms like Twitter into their classroom. However, few of the current Learning Analytics (LA) systems process social media data for instructional interventions and evaluation. As a result, instructors who are using social media cannot easily assess their students’ learning progress or use the data to adjust their lessons in real time. We surveyed 54 university instructors to better understand how they use social media in the classroom; we then used these results to design and evaluate our own Twitter-centric LA dashboard. The overarching goals for this project were to 1) assist instructors in determining whether their particular use of Twitter met their teaching objectives, and 2) help system designers navigate the nuance of designing LA dashboards for social media platforms

Assessing gender inequality from large scale online student reviews

Career growth in academia is often dependent on student reviews of university profes- sors. A growing concern is how evaluation of teaching has been affected by gender biases throughout the reviewing process. However, pinpointing the exact causes and consequen- tial effects of this form of gender inequality has been a hard task. Current work focusses on university-wide student reviewing system, that depends on objective responses on a Likert scale to measure various aspects of an instructor’s qual- ity. Through our work, we access online student review data which are not limited by geographies, universities, or disciplines. Thereafter, we come up with a systematic approach to assess the various ways in which gender inequality is apparent from the student reviews. We also suggest a possible way in which bias related to the gender of a professor could be detected from both objective numerical measures and subjective opinions in reviews. Finally, we assess a logistic re- gression learning algorithm to find the most important factors that can help in identifying gender inequality

Conceptualizing the Electronic Word-of-Mouth Process: What We Know and Need to Know About eWOM Creation, Exposure, and Evaluation

Electronic word of mouth (eWOM) is a prevalent consumer practice that has undeniable effects on the company bottom line, yet it remains an over-labeled and under-theorized concept. Thus, marketers could benefit from a practical, science-based roadmap to maximize its business value. Building on the consumer motivation–opportunity–ability framework, this study conceptualizes three distinct stages in the eWOM process: eWOM creation, eWOM exposure, and eWOM evaluation. For each stage, we adopt a dual lens—from the perspective of the consumer (who sends and receives eWOM) and that of the marketer (who amplifies and manages eWOM for business results)—to synthesize key research insights and propose a research agenda based on a multidisciplinary systematic review of 1050 academic publications on eWOM published between 1996 and 2019. We conclude with a discussion of the future of eWOM research and practice

Role of Social Media in Technology Adoption for Sustainable Agriculture Practices: Evidence from Twitter Analytics

Social networking sites provide a new means of communication for disseminating cutting-edge agricultural technologies. These are unmediated interaction channels that enable a user to communicate their experience with technology and generate negative or positive attitudes that impact technology adoption decisions. We employ a machine learning approach to analyse users\u27 existing semantic predisposition for technology adoption in agriculture at various operational levels. While developing attitudes toward technology adoption, these semantic tendencies become an important aspect of users\u27 cognitive decision making. The study scrapes user comments and conversations about agritech on Twitter through data mining. The research also explains the important characteristics that enhance attitude building on Twitter and are responsible for reinforcing decision making among information seekers using four machine learning models. Based on the results, the research recommends strategies to managers for better communication with agriculturists and enhancement of users\u27 decision making

THE USE AND EFFECTIVENESS OF LAW ENFORCEMENT SOCIAL MEDIA SITES

There is ample evidence that social media is an effective tool during time of crises, as noted by events such as the Boston Marathon bombing or the Las Vegas mass shooting when police used their social media to communicate directly with the public. However, little research has been conducted on how social media can enhance the toolbox of police agencies to help with non-emergency issues, such as building community relations. Previous research offers a glimpse into ways that police agencies typically use social media. This paper will take a step further to determine if law enforcement is accomplishing its goals with social media. Residents and law enforcement officers were asked to evaluate their department’s social media sites, make assessments on what it appears the agency is attempting to achieve, and evaluate whether the site accomplishes that goal. Further, both audiences made suggestions on what they believe social media should be used for. This research allows police agencies insight into how to use social media sites to accomplish their goals and offers perspectives on what the law enforcement and non-law enforcement audience wants or expects to see

What Airbnb Reviews can Tell us? An Advanced Latent Aspect Rating Analysis Approach

There is no doubt that the rapid growth of Airbnb has changed the lodging industry and tourists’ behaviors dramatically since the advent of the sharing economy. Airbnb welcomes customers and engages them by creating and providing unique travel experiences to “live like a local” through the delivery of lodging services. With the special experiences that Airbnb customers pursue, more investigation is needed to systematically examine the Airbnb customer lodging experience. Online reviews offer a representative look at individual customers’ personal and unique lodging experiences. Moreover, the overall ratings given by customers are reflections of their experiences with a product or service. Since customers take overall ratings into account in their purchase decisions, a study that bridges the customer lodging experience and the overall rating is needed. In contrast to traditional research methods, mining customer reviews has become a useful method to study customers’ opinions about products and services. User-generated reviews are a form of evaluation generated by peers that users post on business or other (e.g., third-party) websites (Mudambi & Schuff, 2010). The main purpose of this study is to identify the weights of latent lodging experience aspects that customers consider in order to form their overall ratings based on the eight basic emotions. This study applied both aspect-based sentiment analysis and the latent aspect rating analysis (LARA) model to predict the aspect ratings and determine the latent aspect weights. Specifically, this study extracted the innovative lodging experience aspects that Airbnb customers care about most by mining a total of 248,693 customer reviews from 6,946 Airbnb accommodations. Then, the NRC Emotion Lexicon with eight emotions was employed to assess the sentiments associated with each lodging aspect. By applying latent rating regression, the predicted aspect ratings were generated. With the aspect ratings, , the aspect weights, and the predicted overall ratings were calculated. It was suggested that the overall rating be assessed based on the sentiment words of five lodging aspects: communication, experience, location, product/service, and value. It was found that, compared with the aspects of location, product/service, and value, customers expressed less joy and more surprise than they did over the aspects of communication and experience. The LRR results demonstrate that Airbnb customers care most about a listing location, followed by experience, value, communication, and product/service. The results also revealed that even listings with the same overall rating may have different predicted aspect ratings based on the different aspect weights. Finally, the LARA model demonstrated the different preferences between customers seeking expensive versus cheap accommodations. Understanding customer experience and its role in forming customer rating behavior is important. This study empirically confirms and expands the usefulness of LARA as the prediction model in deconstructing overall ratings into aspect ratings, and then further predicting aspect level weights. This study makes meaningful academic contributions to the evolving customer behavior and customer experience research. It also benefits the shared-lodging industry through its development of pragmatic methods to establish effective marketing strategies for improving customer perceptions and create personalized review filter systems

Micro Agenda Setters: The Effect of Social Media on Young Adults’ Exposure to and Attitude Toward News

Social media services like Facebook and Twitter are playing an increasingly large role as sources of news. This article investigates the ways the composition of social media networks affects people’s exposure to and attitude toward news. Focus groups (N=31) and in-depth interviews (N=15) with young adults of varying ethnicity and country of origin showed that people’s networks on social media function as micro agenda setters. The characteristics of people in one’s network can facilitate negative effects such as echo chambers and spirals of silence but can also unfold new perspectives and create awareness of topics not covered by legacy media

Student attitudes on social media and perception of instructor social media use.

The purpose of this study is to understand what student attitudes are toward using social media in the classroom and if those attitudes influenced how they perceive instructors using social media in the classroom. Implementing a mixed method approach, this study conducted focus groups to gain an in-depth understanding of what student attitudes were and why they held those ideas. A survey was then distributed to the students’ in a Midwestern University to see if there was a relationship between students attitudes and their perceptions of instructors who use social media. Results showed that students do hold a positive attitude toward using social media if the participation was voluntary. The survey results showed only certain items (such as voluntariness and proficiency) moderately correlated with the perception of the instructor. Implications of the results as well as the limitations and potential for future research are discussed

On the relation between message sentiment and its virality on social media

We investigate the relation between the sentiment of a message on social media and its virality, defined as the volume and speed of message diffusion. We analyze 4.1 million messages (tweets) obtained from Twitter. Although factors affecting message diffusion on social media have been studied previously, we focus on message sentiment and reveal how the polarity of message sentiment affects its virality. The virality of a message is characterized by the number of message repostings (retweets) and the time elapsed from the original posting of a message to its Nth reposting (N-retweet time). Through extensive analysis using the 4.1 million tweets and their retweets in 1 week, we discover that negative messages are likely to be reposted more rapidly and frequently than positive and neutral messages. Specifically, the reposting volume of negative messages is 20–60% higher than that of positive and neutral messages, and negative messages spread 25% faster than positive and neutral messages when the diffusion volume is quite high. We also perform longitudinal analysis of message diffusion observed over 1 year and find that recurrent diffusion of negative messages is less frequent than that of positive and neutral messages. Moreover, we present a simple message diffusion model that can reproduce the characteristics of message diffusion observed in this paper

Exploring Cyberterrorism, Topic Models and Social Networks of Jihadists Dark Web Forums: A Computational Social Science Approach

This three-article dissertation focuses on cyber-related topics on terrorist groups, specifically Jihadists’ use of technology, the application of natural language processing, and social networks in analyzing text data derived from terrorists\u27 Dark Web forums. The first article explores cybercrime and cyberterrorism. As technology progresses, it facilitates new forms of behavior, including tech-related crimes known as cybercrime and cyberterrorism. In this article, I provide an analysis of the problems of cybercrime and cyberterrorism within the field of criminology by reviewing existing literature focusing on (a) the issues in defining terrorism, cybercrime, and cyberterrorism, (b) ways that cybercriminals commit a crime in cyberspace, and (c) ways that cyberterrorists attack critical infrastructure, including computer systems, data, websites, and servers. The second article is a methodological study examining the application of natural language processing computational techniques, specifically latent Dirichlet allocation (LDA) topic models and topic network analysis of text data. I demonstrate the potential of topic models by inductively analyzing large-scale textual data of Jihadist groups and supporters from three Dark Web forums to uncover underlying topics. The Dark Web forums are dedicated to Islam and the Islamic world discussions. Some members of these forums sympathize with and support terrorist organizations. Results indicate that topic modeling can be applied to analyze text data automatically; the most prevalent topic in all forums was religion. Forum members also discussed terrorism and terrorist attacks, supporting the Mujahideen fighters. A few of the discussions were related to relationships and marriages, advice, seeking help, health, food, selling electronics, and identity cards. LDA topic modeling is significant for finding topics from larger corpora such as the Dark Web forums. Implications for counterterrorism include the use of topic modeling in real-time classification and removal of online terrorist content and the monitoring of religious forums, as terrorist groups use religion to justify their goals and recruit in such forums for supporters. The third article builds on the second article, exploring the network structures of terrorist groups on the Dark Web forums. The two Dark Web forums\u27 interaction networks were created, and network properties were measured using social network analysis. A member is considered connected and interacting with other forum members when they post in the same threads forming an interaction network. Results reveal that the network structure is decentralized, sparse, and divided based on topics (religion, terrorism, current events, and relationships) and the members\u27 interests in participating in the threads. As participation in forums is an active process, users tend to select platforms most compatible with their views, forming a subgroup or community. However, some members are essential and influential in the information and resources flow within the networks. The key members frequently posted about religion, terrorism, and relationships in multiple threads. Identifying key members is significant for counterterrorism, as mapping network structures and key users are essential for removing and destabilizing terrorist networks. Taken together, this dissertation applies a computational social science approach to the analysis of cyberterrorism and the use of Dark Web forums by jihadists