The Role of Country of Origin in Brand Following on Social Media Among U.S. Consumers

An understanding of how consumers interact with brands online is still in its infancy. This study will attempt to explain what motivates consumers to follow brands on social media, looking specifically at the role country and region of origin of products plays in explaining the relationship. Given the personal nature that attracts people to social media to build relationships, it is believed that the personal nature of brands originating from the social media users’ home country will heighten the likelihood that consumers track certain brands and may enhance the relationship that evolves between the brand and the consumer. A model is proposed to explain the relationship, with survey data from U.S. consumers used to begin to establish any links between product origins and brand tracking behavior through social media

"I’m Eating a Sandwich in Glasgow": Modeling locations with tweets

Social media such as Twitter generate large quantities of data about what a person is thinking and doing in a partic- ular location. We leverage this data to build models of locations to improve our understanding of a user’s geographic context. Understanding the user’s geographic context can in turn enable a variety of services that allow us to present information, recommend businesses and services, and place advertisements that are relevant at a hyper-local level. In this paper we create language models of locations using coordinates extracted from geotagged Twitter data. We model locations at varying levels of granularity, from the zip code to the country level. We measure the accuracy of these models by the degree to which we can predict the location of an individual tweet, and further by the accuracy with which we can predict the location of a user. We find that we can meet the performance of the industry standard tool for pre- dicting both the tweet and the user at the country, state and city levels, and far exceed its performance at the hyper-local level, achieving a three- to ten-fold increase in accuracy at the zip code level

Keystroke Biometrics in Response to Fake News Propagation in a Global Pandemic

This work proposes and analyzes the use of keystroke biometrics for content de-anonymization. Fake news have become a powerful tool to manipulate public opinion, especially during major events. In particular, the massive spread of fake news during the COVID-19 pandemic has forced governments and companies to fight against missinformation. In this context, the ability to link multiple accounts or profiles that spread such malicious content on the Internet while hiding in anonymity would enable proactive identification and blacklisting. Behavioral biometrics can be powerful tools in this fight. In this work, we have analyzed how the latest advances in keystroke biometric recognition can help to link behavioral typing patterns in experiments involving 100,000 users and more than 1 million typed sequences. Our proposed system is based on Recurrent Neural Networks adapted to the context of content de-anonymization. Assuming the challenge to link the typed content of a target user in a pool of candidate profiles, our results show that keystroke recognition can be used to reduce the list of candidate profiles by more than 90%. In addition, when keystroke is combined with auxiliary data (such as location), our system achieves a Rank-1 identification performance equal to 52.6% and 10.9% for a background candidate list composed of 1K and 100K profiles, respectively.Comment: arXiv admin note: text overlap with arXiv:2004.0362