User profiling with geo-located social media and demographic data

User profiling is the task of inferring attributes, such as gender or age, of social media users based on the content they produce or their behaviours on-line. Approaches for user profiling typically use machine learning techniques to train user profiling systems capable of inferring the attributes of unseen users, having been provided with a training set of users labelled with their attributes. Classic approaches to user attribute labelling for such a training set may be manual or automated, examples include: direct solicitation through surveys, manual assignment based on outward characteristics, and extraction of attribute key-phrases from user description fields. Social media platforms, such as Twitter, often provide users with the ability to attach their geographic location to their posts, known as geo-location. In addition, government organisations release demographic data aggregated at a variety of geographic scales. The combination of these two data sources is currently under-explored in the user profiling literature. To combine these sources, a method is proposed for geo-location-driven user attribute labelling in which a coordinate level prediction is made for a user's 'home location', which in turn is used to 'look up' corresponding demographic variables that are assigned to the user. Strong baseline components for user profiling systems are investigated and validated in experiments on existing user profiling datasets, and a corpus of geo-located Tweets is used to derive a complementary resource. An evaluation of current methods for assigning fine-grained home location to social media users is performed, and two improved methods are proposed based on clustering and majority voting across arbitrary geographic regions. The proposed geo-location-driven user attribute labelling approach is applied across three demographic variables within the UK: Output Area Classification (OAC), Local Authority Classification (LAC), and National Statistics Socio-economic Classification (NS-SEC). User profiling systems are trained and evaluated on each of the derived datasets, and NS-SEC is additionally validated against a dataset derived through a different method. Promising results are achieved for LAC and NS-SEC, however characteristics of the underlying geographic and demographic data can lead to poor quality datasets, as displayed for OAC

GeoIntelligence: Data Mining Locational Social Media Content for Profiling and Information Gathering

The current social media landscape has resulted in a situation where people are encouraged to share a greater amount of information about their day-to-day lives than ever before. In this environment a large amount of personal data is disclosed in a public forum with little to no regard for the potential privacy impacts. This paper focuses on the presence of geographic data within images, metadata and individual postings. The GeoIntelligence project aims to aggregate this information to educate users on the possible implications of the utilisation of these services as well as providing service to law enforcement and business. This paper demonstrates the ability to profile users on an individual and group basis from data posted openly to social networking services

Analysis of Home Location Estimation with Iteration on Twitter Following Relationship

User's home locations are used by numerous social media applications, such as social media analysis. However, since the user's home location is not generally open to the public, many researchers have been attempting to develop a more accurate home location estimation. A social network that expresses relationships between users is used to estimate the users' home locations. The network-based home location estimation method with iteration, which propagates the estimated locations, is used to estimate more users' home locations. In this study, we analyze the function of network-based home location estimation with iteration while using the social network based on following relationships on Twitter. The results indicate that the function that selects the most frequent location among the friends' location has the best accuracy. Our analysis also shows that the 88% of users, who are in the social network based on following relationships, has at least one correct home location within one-hop (friends and friends of friends). According to this characteristic of the social network, we indicate that twice is sufficient for iteration.Comment: The 2016 International Conference on Advanced Informatics: Concepts, Theory and Application (ICAICTA2016

A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur

DeepCity: A Feature Learning Framework for Mining Location Check-ins

Online social networks being extended to geographical space has resulted in large amount of user check-in data. Understanding check-ins can help to build appealing applications, such as location recommendation. In this paper, we propose DeepCity, a feature learning framework based on deep learning, to profile users and locations, with respect to user demographic and location category prediction. Both of the predictions are essential for social network companies to increase user engagement. The key contribution of DeepCity is the proposal of task-specific random walk which uses the location and user properties to guide the feature learning to be specific to each prediction task. Experiments conducted on 42M check-ins in three cities collected from Instagram have shown that DeepCity achieves a superior performance and outperforms other baseline models significantly

Who's Feeding the Kids Online? Digital food marketing to children in Ireland: Advertisers’ tactics, children’s exposure and parents’ awareness

Obesity in children and young people is a global health challenge. The widespread marketing of unhealthy foods (food and non-alcoholic drinks high in fat, sugar and salt, or HFSS) plays a causal role in unhealthy eating and obesity. Food and eating is typically presented as an issue of ‘choice’. However, this disregards the fact that current obesogenic environments use many tactics to promote unhealthy foods, interfering with people’s ability to make good choices. This study examined: 1. Content appealing to children and young people on websites of top food and drink retail brands in Ireland 2. Marketing techniques on Facebook: Pages of food brands that have the highest reach among young teens, the first such study of which we are aware 3. Parents’ awareness of digital food marketing to their children in an online, two-stage survey with digital marketing examples and open-ended response options

Digital Food Marketing to Children and Adolescents: Problematic Practices and Policy Interventions

Examines trends in digital marketing to youth that uses "immersive" techniques, social media, behavioral profiling, location targeting and mobile marketing, and neuroscience methods. Recommends principles for regulating inappropriate advertising to youth

Bottom-up radio: creating a new media format using living lab research

This study resulted in the creation of a new media format for urban youth, adopting a living lab-approach, as current studies have shown that this group is currently not reached with the contemporary media offer. Living lab research is a state-of-the art methodology that aims at involving end-users in the innovation process over a longer time span, combining both quantitative and qualitative research techniques and tools. In a first phase, a panel of urban youngsters was created using an intake survey (N=290). These data were analyzed resulting in three distinct types of urban youngsters. In a second phase, a qualitative research trajectory was organized in order to refine the three profiles and get an insight in their media use, digital skills, media preferences and needs with regards to the current media offer. Research methods during this phase included diary studies, participatory observation during workshops and probe research. In a third phase, co-creation sessions were organized with youngsters from the urban panel in order to get feedback on a concept that was iteratively developed during the first two phases of the project. Results show that mobile devices and social media are important for these urban youngsters and that most of these youngsters have quite some creative skills. Radio seems to be a less popular medium, although they spend a significant amount of time listening to music. Further, results show that these youngsters are in need of a platform which stimulates community building and offers a space to express their creativity. A third requirement for the development of a new media format that would meet the needs of these youngers is a format that provides space for local elements and niche markets. This all resulted in the launch of Chase, an urban, crowdsourced radio station