7 A. Methodology V. USING SOCIAL INFORMATION TO PRIORITIZE DATA COLLECTION Similar perplexity reductions were achieved by the com- bination LMs for Bengali, Tamil, and Zulu. (See Table V.) Tamil, whose 15% perplexity improvement is the least of all the languages, is still twice the improvement obtained in the initial experiment from Table II. For Zulu, the initial exper- iments failed to give much of an improvement in perplexity but using the class LMs a strong perplexity reduction of 19% was obtained. This is in spite of the relatively small amount of Twitter data available in these two languages. Due to the strong relationship between the amount of available Twitter data and the LM perplexity (see Figure 1), it is desirable to have as much useful data as possible. After obtaining an initial download of Twitter data in response to the selected queries, it is possible to expand our search across social connections. Adding the social connections of the already downloaded users to the download queue will allow us to discover new content when our source of seed queries is exhausted. However, any attempt at crawling the Internet will quickly run into the problem that the rate of growth of the download queue far exceeds the rate at which it can be processed. For example, after processing the first 4,000 Tamil speaking Twitter users there were 200,000 additional users in the queue. Internet search engines deal with this problem by prioritizing the queue such that the most important or useful pages are visited first [19]. If such a strategy were not employed, many useful pages would be so far back in the queue that they would effectively never be reached. Such concerns are even more relevant when the data is accessed through an API that places limits or costs on each request. In this section, we look at the feasibility of applying queue ranking methods to our task. In order to rank the Twitter users, we need to define some measure of quality or usefulness. We will use the in- vocabulary hit rate as our measure of usefulness since we know from previous experiments that this leads to reduction in language model perplexity. The task is to predict the utility of a user’s data using information gleaned from that user’s social connections. The social connections are grouped into three categories: mentions (a direct reference to a user in a public Tweet), followers (people who subscribe to the user’s Twitter feed) and reverse followers (people who the user subscribes to). Our features will consist of statistics aggregated over each social connection type. For example, the bigram hit rate statistic will produce three features—namely, the average bigram hit rate for people who mention, follow, or reverse follow the user. The statistics that we consider are unigram and bigram hit rates, perplexity, character set matching and the number of sentences after de-duplication and filtering. The post-filtering sentence count is a rough measure of how much data per user ends up actually being used to train the language model. In addition, we add the degree of each user in the social graph as a feature. This is computed separately for each of the three types of edges. We use random forest regression [20] to predict the user priority from the features. An alternative ranking scheme, Edge Count Ranking, prioritizes the download queue based on the cardinality of the set of edges that connect the seed data to the users in the queue. In this ranking, the next user to be visited will be the one who has the most social connections to the set of users who have already been visited. These two ranking methods are compared against a random baseline that processes the queue in random order.