Predicting customer's gender and age depending on mobile phone data

In the age of data driven solution, the customer demographic attributes, such as gender and age, play a core role that may enable companies to enhance the offers of their services and target the right customer in the right time and place. In the marketing campaign, the companies want to target the real user of the GSM (global system for mobile communications), not the line owner. Where sometimes they may not be the same. This work proposes a method that predicts users' gender and age based on their behavior, services and contract information. We used call detail records (CDRs), customer relationship management (CRM) and billing information as a data source to analyze telecom customer behavior, and applied different types of machine learning algorithms to provide marketing campaigns with more accurate information about customer demographic attributes. This model is built using reliable data set of 18,000 users provided by SyriaTel Telecom Company, for training and testing. The model applied by using big data technology and achieved 85.6% accuracy in terms of user gender prediction and 65.5% of user age prediction. The main contribution of this work is the improvement in the accuracy in terms of user gender prediction and user age prediction based on mobile phone data and end-to-end solution that approaches customer data from multiple aspects in the telecom domain

Gender detection of Twitter users based on multiple information sources

Twitter provides a simple way for users to express feelings, ideas and opinions, makes the user generated content and associated metadata, available to the community, and provides easy-to-use web and application programming interfaces to access data. The user profile information is important for many studies, but essential information, such as gender and age, is not provided when accessing a Twitter account. However, clues about the user profile, such as the age and gender, behaviors, and preferences, can be extracted from other content provided by the user. The main focus of this paper is to infer the gender of the user from unstructured information, including the username, screen name, description and picture, or by the user generated content. We have performed experiments using an English labelled dataset containing 6.5 M tweets from 65 K users, and a Portuguese labelled dataset containing 5.8 M tweets from 58 K users. We have created four distinct classifiers, trained using a supervised approach, each one considering a group of features extracted from four different sources: user name and screen name, user description, content of the tweets, and profile picture. Features related with the activity, such as number of following and number of followers, were discarded, since these features were found not indicative of gender. A final classifier that combines the prediction of each one of the four previous individual classifiers achieves the best performance, corresponding to 93.2% accuracy for English and 96.9% accuracy for Portuguese data.info:eu-repo/semantics/acceptedVersio

Detecting user demographics in twitter to inform health trends in social media

The widespread and popular use of social media and social networking applications offer a promising opportunity for gaining knowledge and insights regarding population health conditions thanks to the diversity and abundance of online user-generated information (UGHI) relating to healthcare and well-being. However, users on social media and social networking sites often do not supply their complete demographic information, which greatly undermines the value of the aforementioned information for health 2.0 research, e.g., for discerning disparities across population groups in certain health conditions. To recover the missing user demographic information, existing methods observe a limited scope of user behaviors, such as word frequencies exhibited in a user’s messages, leading to sub-optimal results. To address the above limitation and improve the performance of inferring missing user demographic information for health 2.0 research, this work proposes a new algorithmic method for extracting a social media user’s gender by exploring and exploiting a comprehensive set of a user’s behaviors on Twitter, including the user’s conversational topic choices, account profile information, and personal information. In addition, this work explores the usage of synonym expansion for detecting social media users’ ethnicities. To better capture a user’s conversational topic choices using standardized hashtags for consistent comparison, this work additionally introduces a new method that automatically generates standardized hashtags for tweets. Even though Twitter is selected as the experimental platform in this study due to its leading position among today’s social networking sites, the proposed method is in principle generically applicable to other social media sites and applications as long as there is a way to access user-generated content on those platforms. When comparing the multi-perspective learning method with the state-of-the-art approaches for gender classification, a gender classification accuracy is observed of 88.6% for the proposed approach compared with 63.4% performance for bag-of-words and 61.4% for the peer method. Additionally, the topical approach introduced in this work outperforms vocabulary-based approach with a smaller dimensionality at 69.4% accuracy. Furthermore, observable usage patterns of the cancer terms are analyzed across the ethnic groups inferred by the proposed algorithmic approaches. Variations among demographic groups are seen in the frequency of term usage during months known to be labeled as cancer awareness months. This work introduces methods that have the potential to serve as a very powerful and important tool in disseminating critical prevention, screening, and treatment messages to the community in real time. Study findings highlight the potential benefits of social media as a tool for detecting demographic differences in cancer-related discussions on social media

ANALYZING IMAGE TWEETS IN MICROBLOGS

Ph.DDOCTOR OF PHILOSOPH