E-cigarettes(vape) are now the most commonly used tobacco product among youth in the United States. Ads are claiming e-cigarettes help smokers quit, but most of them contain nicotine, which can cause addiction and harm the developing adolescent brain. Therefore national, state and local health organizations have proposed anti-vaping campaigns to warn the potential risks of e-cigarettes. Since there is not definitive evidence that e-cigarettes cause long-term harm, these campaigns received pro-vapors' fight back, and collected a high volume of opponent messages in social media. Thus when we analyze the feedback of anti-vaping campaigns, it is crucial to partition audience into different clusters according to their attitude. Motivated by this, in this paper, we propose the “Community Detection on Anti-vaping Campaign Audience (CodeVan)” problem and design the Sorento method to solve it. Sorento computes users' intimacy scores based on their social connections, repost relations and content similarities. Extensive experiments show the effectiveness of the Sorento algorithm.

In almost any application of social media analysis, the user is interested in studying a particular topic or research question. Collecting posts or messages relevant to the topic from a social media source is a necessary step. Due to the huge size of social media sources (e.g., Twitter and Facebook), one has to use some topic keywords to search for possibly relevant posts. However, gathering a good set of keywords is a very tedious and time-consuming task. It often involves a lengthy iterative process of searching and manual reading. In this paper, we propose a novel technique to help the user identify topical search keywords. Our experiments are carried out on identifying such keywords for five (5) real-life application topics to be used for searching relevant tweets from the Twitter API. The results show that the proposed method is highly effective.

Anti-tobacco mass media campaigns are designed to influence tobacco users. It has been proved campaigns will produce their changes in awareness, knowledge, and attitudes, and also produce meaningful behavior change of audience. Anti-smoking television advertising is the most important part in the campaign. Meanwhile nowadays successful online social networks are creating new media environment, however little is known about the relation between social conversations and anti-tobacco campaigns. This paper aims to infer social influence of these campaigns, and the problem is formally referred to as the “Social Influence inference of anti-Tobacco mass mEdia campaigns” (SITE) problem. To address the SITE problem, a novel influence inference framework, “TV Advertising Social Influence Estimation” (ASIE), is proposed based on our analysis of two anti-tobacco campaigns. ASIE divides audience attitudes towards TV ads into three distinct stages: (1) Cognitive, (2) Affective and (3) Conative. Audience online reactions at each of these three stages are depicted by ASIE with specific probabilistic models based on the synergistic influences from both online social friends and offline TV ads. Extensive experiments demonstrate the effectiveness of ASIE.

One of the overarching tasks of document analysis is to find what topics people talk about. One of the main techniques for this purpose is topic modeling. So far many models have been proposed. However, the existing models typically perform full analysis on the whole data to find all topics. This is certainly useful, but in practice we found that the user almost always also wants to perform more detailed analyses on some specific aspects, which we refer to as targets (or targeted aspects). Current full-analysis models are not suitable for such analyses as their generated topics are often too coarse and may not even be on target. For example, given a set of tweets about e-cigarette, one may want to find out what topics under discussion are specifically related to children. Likewise, given a collection of online reviews about a camera, a consumer or camera manufacturer may be interested in finding out all topics about the camera's screen, the targeted aspect. As we will see in our experiments, current full topic models are ineffective for such targeted analyses. This paper studies this problem and proposes a novel targeted topic model (TTM) to enable focused analyses on any specific aspect of interest. Our experimental results demonstrate the effectiveness of the TTM.

As social media is becoming an increasingly important source of public information, companies, organizations and individuals are actively using social media platforms to promote their products, services, ideas and ideologies. Unlike promotional campaigns on TV or other traditional mass media platforms, campaigns on social media often appear in stealth modes. Campaign promoters often try to influence people's behaviors/opinions/decisions in a latent manner such that the readers are not aware that the messages they see are strategic campaign posts aimed at persuading them to buy target products/services. Readers take such campaign posts as just organic posts from the general public. It is thus important to discover such campaigns, their promoter accounts and how the campaigns are organized and executed as it can uncover the dynamics of Internet marketing. This discovery is clearly useful for competitors and also the general public. However, so far little work has been done to solve this problem. In this paper, we study this important problem in the context of the Twitter platform. Given a set of tweets streamed from Twitter based on a set of keywords representing a particular topic, the proposed technique aims to identify user accounts that are involved in promotion. We formulate the problem as a relational classification problem and solve it using typed Markov Random Fields (T-MRF), which is proposed as a generalization of the classic Markov Random Fields. Our experiments are carried out using three real-life datasets from the health science domain related to smoking. Such campaigns are interesting to health scientists, government health agencies and related businesses for obvious reasons. Our results show that the proposed method is highly effective.