Internet social networks have become a ubiquitous application allowing people to easily share text, pictures, and audio and video files. Popular networks include WhatsApp, Facebook, Reddit and LinkedIn. We present an extensive study of the usage of the WhatsApp social network, an Internet messaging application that is quickly replacing SMS messaging. In order to better understand people's use of the network, we provide an analysis of over 6 million messages from over 100 users, with the objective of building demographic prediction models using activity data. We performed extensive statistical and numerical analysis of the data and found significant differences in WhatsApp usage across people of different genders and ages. We also inputted the data into the Weka data mining package and studied models created from decision tree and Bayesian network algorithms. We found that different genders and age demographics had significantly different usage habits in almost all message and group attributes. We also noted differences in users' group behavior and created prediction models, including the likelihood a given group would have relatively more file attachments, if a group would contain a larger number of participants, a higher frequency of activity, quicker response times and shorter messages. We were successful in quantifying and predicting a user's gender and age demographic. Similarly, we were able to predict different types of group usage. All models were built without analyzing message content. We present a detailed discussion about the specific attributes that were contained in all predictive models and suggest possible applications based on these results.

BACKGROUND Social networks on the Internet have become ubiquitous applications that allow people to easily share text, pictures, and audio and video files. Popular networks include WhatsApp, Facebook, Reddit, and LinkedIn. OBJECTIVE We present an extensive study of the usage of the WhatsApp social network, an Internet messaging application that is quickly replacing SMS (short message service) messaging. To better understand people's use of the network, we provide an analysis of over 6 million encrypted messages from over 100 users, with the objective of building demographic prediction models that use activity data but not the content of these messages. METHODS We performed extensive statistical and numerical analysis of the data and found significant differences in WhatsApp usage across people of different genders and ages. We also entered the data into the Weka and pROC data mining packages and studied models created from decision trees, Bayesian networks, and logistic regression algorithms. RESULTS We found that different gender and age demographics had significantly different usage habits in almost all message and group attributes. We also noted differences in users' group behavior and created prediction models, including the likelihood that a given group would have relatively more file attachments and if a group would contain a larger number of participants, a higher frequency of activity, quicker response times, and shorter messages. CONCLUSIONS We were successful in quantifying and predicting a user's gender and age demographic. Similarly, we were able to predict different types of group usage. All models were built without analyzing message content. CONTRIBUTION The main contribution of this paper is the ability to predict user demographics without having access to users' text content. We present a detailed discussion about the specific attributes that were contained in all predictive models and suggest possible applications based on these results.

An important area of social network research is identifying missing information which is not explicitly represented in the network or is not visible to all. In this paper, we propose a novel Hybrid Approach of Classifier and Clustering,a which we refer to as HACC, to solve the missing node identification problem in social networks. HACC utilizes a classifier as a preprocessing step in order to integrate all known information into one similarity measure and then uses a clustering algorithm to identify missing nodes. Specifically, we used the information on the network structure, attributes about known users (nodes) and pictorial information to evaluate HACC and found that it performs significantly better than other missing node algorithms. We also argue that HACC is a general approach and domain independent and can be easily applied to other domains. We support this claim by evaluating HACC on a second authorship identification domain as well.

We investigate the effect a social network could have on voting outcomes. We consider a group of self-interested agents where each agent has a strict preference order over a set of outcomes. Each agent votes strategically, taking into consideration both her preferences, and her (limited) information about the preferences of other voters. We assume that the information the agent has comes from her friends in the social network and from a public opinion poll. If agents were not strategic at all, the social network (and the poll) would not matter, since they would just vote according to their preferences. However, if the agents deviate and vote strategically the network plays a great effect. To measure this effect, we focus on iterative voting with Plurality voting rule. We show, both in theory and in simulations, that for many networks, adding a linear number of edges can make any outcome the winner. We view our results as yet another indication to the effect that a central organizer, such as a company who controls social media, could have on our lives - by introducing us to certain people it can affect our information and our decisions.

An important area of social network research is identifying missing information which is not visible or explicitly represented in the network. Recently, the missing node identification problem was introduced where missing members in the social network structure must be identified. However, previous works did not consider the possibility that information about specific users (nodes) within the network may be known and could be useful in solving this problem. Assuming such information such as user demographic information and users’ historical behavior in the network is known, more effective algorithms for the missing node identification problem could potentially be developed. In this paper, we present three algorithms, SAMI-A, SAMI-C and SAMI-N, which leverage this type of information to perform significantly better than previous missing node algorithms. However, as each of these algorithms and the parameters within these algorithms often perform better in specific problem instances, a mechanism is needed to select the best algorithm and the best variation within that algorithm. Towards this challenge, we also present OASCA, a novel online selection algorithm. We present results that detail the success of the algorithms presented within this paper.

In recent years, social networks have surged in popularity. One key aspect of social network research is identifying important missing information that is not explicitly represented in the network, or is not visible to all. To date, this line of research typically focused on finding the connections that are missing between nodes, a challenge typically termed as the link prediction problem. This article introduces the missing node identification problem, where missing members in the social network structure must be identified. In this problem, indications of missing nodes are assumed to exist. Given these indications and a partial network, we must assess which indications originate from the same missing node and determine the full network structure. Toward solving this problem, we present the missing node identification by spectral clustering algorithm (MISC), an approach based on a spectral clustering algorithm, combined with nodes’ pairwise affinity measures that were adopted from link prediction research. We evaluate the performance of our approach in different problem settings and scenarios, using real-life data from Facebook. The results show that our approach has beneficial results and can be effective in solving the missing node identification problem. In addition, this article also presents R-MISC, which uses a sparse matrix representation, efficient algorithms for calculating the nodes’ pairwise affinity, and a proprietary dimension reduction technique to enable scaling the MISC algorithm to large networks of more than 100,000 nodes. Last, we consider problem settings where some of the indications are unknown. Two algorithms are suggested for this problem: speculative MISC, based on MISC, and missing link completion, based on classical link prediction literature. We show that speculative MISC outperforms missing link completion.

Scenario-based serious-games have become an important tool for teaching new skills and capabilities. An important factor in the development of such systems is reducing the time and cost overheads in manually creating content for these scenarios. To address this challenge, we present ScenarioGen, an automatic method for generating content about everyday activities through combining computer science techniques with the crowd. ScenarioGen uses the crowd in three different ways: to capture a database of scenarios of everyday activities, to generate a database of likely replacements for specific events within that scenario, and to evaluate the resulting scenarios. We evaluated ScenarioGen in 6 different content domains and found that it was consistently rated as coherent and consistent as the originally captured content. We also compared ScenarioGen's content to that created by traditional planning techniques. We found that both methods were equally effective in generating coherent and consistent scenarios, yet ScenarioGen's content was found to be more varied and easier to create.

In this paper we present SNACS, a novel method for creating Social Narratives that can be Adapted using information from Crowdsourcing. Previous methods for automatic narrative generation require that the primary author explicitly detail nearly all parts of the story, including details about the narrative. This is also the case for narratives within computer games, educational tools and Embodied Conversational Agents (ECA). While such narratives are well written, they clearly require significant time and cost overheads. SNACS is a hybrid narrative generation method that merges partially formed preexisting narratives with new input from crowdsourcing techniques. We compared the automatically generated narratives with those that were created solely by people, and with those that were generated semi-automatically by a state-of-the-art narrative planner. We empirically found that SNACS was effective as people found narratives generated by SNACS to be as realistic and consistent as those manually created by the people or the narrative planner. Yet, the automatically generated narratives were created with much lower time overheads and were significantly more diversified, making them more suitable for many applications.