Sitaram Asur

Predicting the Future with Social Media

In recent years, social media has become ubiquitous and important for social networking and content sharing. And yet, the content that is generated from these websites remains largely untapped. In this paper, we demonstrate how social media content can be used to predict real-world outcomes. In particular, we use the chatter from Twitter.com to forecast box-office revenues for movies. We show that a simple model built from the rate at which tweets are created about particular topics can outperform market-based predictors. We further demonstrate how sentiments extracted from Twitter can be utilized to improve the forecasting power of social media.

Influence and passivity in social media

The ever-increasing amount of information flowing through Social Media forces the members of these networks to compete for attention and influence by relying on other people to spread their message. A large study of information propagation within Twitter reveals that the majority of users act as passive information consumers and do not forward the content to the network. Therefore, in order for individuals to become influential they must not only obtain attention and thus be popular, but also overcome user passivity. We propose an algorithm that determines the influence and passivity of users based on their information forwarding activity. An evaluation performed with a 2.5 million user dataset shows that our influence measure is a good predictor of URL clicks, outperforming several other measures that do not explicitly take user passivity into account. We demonstrate that high popularity does not necessarily imply high influence and vice-versa.

Trends in Social Media: Persistence and Decay

Social media generates a prodigious wealth of real-time content at an incessant rate. From all the content that people create and share, only a few topics manage to attract enough attention to rise to the top and become temporal trends which are displayed to users. The question of what factors cause the formation and persistence of trends is an important one that has not been answered yet. In this paper, we conduct an intensive study of trending topics on Twitter and provide a theoretical basis for the formation, persistence and decay of trends. We also demonstrate empirically how factors such as user activity and number of followers do not contribute strongly to trend creation and its propagation. In fact, we find that the resonance of the content with the users of the social network plays a major role in causing trends.

Artificial Inflation: The Real Story of Trends and Trend-Setters in Sina Weibo

There has been a tremendous rise in the growth of online social networks all over the world in recent years. This has resulted in a large amount of content created and propagated at an incessant rate, all competing with each other to attract enough attention and become trends. In this paper, we analyze the temporal aspect of trends and trend-setters in Sina Weibo, contrasting it with earlier observations on Twitter. First, we look at the formation, persistence and decay of trends and examine the key topics that trend in Sina Weibo. We find that retweeting activity is very predominant in Sina Weibo. Furthermore we discover that many of the trends in Sina Weibo are due to continuous retweets by a small percentage of fraudulent accounts set up for the purpose of artificially inflating certain posts.

Dynamics of Trends and Attention in Chinese Social Media

There has been a tremendous rise in the growth of online social networks all over the world in recent years. It has facilitated users to generate a large amount of real-time content at an incessant rate, all competing with each other to attract enough attention and become popular trends. While Western online social networks such as Twitter have been well studied, the popular Chinese microblogging network Sina Weibo has had relatively lower exposure. In this paper, we analyze in detail the temporal aspect of trends and trend-setters in Sina Weibo, contrasting it with earlier observations in Twitter. We find that there is a vast difference in the content shared in China when compared to a global social network such as Twitter. In China, the trends are created almost entirely due to the retweets of media content such as jokes, images and videos, unlike Twitter where it has been shown that the trends tend to have more to do with current global events and news stories.We take a detailed look at the formation, persistence and decay of trends and examine the key topics that trend in Sina Weibo. One of our key findings is that retweets are much more common in Sina Weibo and contribute a lot to creating trends. When we look closer, we observe that most trends in Sina Weibo are due to the continuous retweets of a small percentage of fraudulent accounts. These fake accounts are set up to artificially inflate certain posts, causing them to shoot up into Sina Weibos trending list, which are in turn displayed as the most popular topics to users.

Trend Dynamics and Attention in Chinese Social Media

We analyzed the temporal aspect of trends and trend-setters in Sina Weibo, contrasting it with earlier observations of Twitter. We found a vast difference in the content shared in China as compared with a global social network such as Twitter. In China, the trends are created almost entirely due to the retweets of media content such as jokes, images, and videos, unlike Twitter where trends have more to do with current global events and news stories. On closer inspection, we observed that most trends in Sina Weibo are due to the continuous retweets of a small percentage of fraudulent accounts, set up to artificially inflate certain posts. This reveals evidence of an “Internet Water Army,” a unique promotional method deployed by public relations companies in China to influence the popular dissemination of information in online social networks.

Race, religion or sex: what makes a superbowl ad controversial?

Advertisements that generate undue controversies can destroy an advertising campaign. However it is difficult to estimate the potential of controversies in advertisements through traditional methods such as customer surveys and market research. In this paper, we develop a controversy detection system based on initial comments on online advertisements posted on YouTube. We extract early YouTube comments on a collection of Superbowl advertisements and generate a comprehensive set of over 2500 semantic and linguistic features for automatically detecting controversies. Our results show good accuracy in early detection of controversies. The proposed data-driven approach can complement and greatly aid traditional approaches of market research.