Twitter is a social network that offers a rich and interesting source of information challenging to retrieve and analyze. Twitter data can be accessed using a REST API. The available operations allow retrieving tweets on the basis of a set of keywords but with limitations such as the number of calls per minute and the size of results. Besides, there is no control on retrieved results and finding tweets which are relevant to a specific topic is a big issue. Given these limitations, it is important that the query keywords cover unambiguously the topic of interest in order to both reach the relevant answers and decrease the number of API calls. In this paper, we introduce a new crawling algorithm called "SmartTwitter Crawling" (STiC) that retrieves a set of tweets related to a target topic. In this algorithm, we take an initial keyword query and enrich it using a set of additional keywords that come from different data sources. STiC algorithm relies on a DFS search in Twittergraph where each reached tweet is considered if it is relevant with the query keywords using a scoring, updated throughout the whole crawling process. This scoring takes into account the tweet text, hashtags and the users who have posted the tweet, replied to the tweet, been mentioned in the tweet or retweeted the tweet. Given this score, STiC is able to select relevant tweets in each iteration and continue by adding the related valuable tweets. Several experiments have been achieved for different kinds of queries, the results showedthat the precision increases compared to a simple BFS search.
翻译:社交网络Twitter是一个社交网络,提供丰富而有趣的信息源,难以检索和分析。Twitter数据可以使用REST API访问。可用的操作允许根据一组关键词检索推特,但有一定的限制,如每分钟的通话次数和结果大小等。此外,对于检索的结果没有控制,找到与特定主题相关的推特是一个大问题。鉴于这些限制,查询关键词必须明确覆盖感兴趣的主题,以便达到相关答案并减少API调用的次数。在本文中,我们引入了名为“SmartTwitter搜索”(STiC)的新的爬行算法,根据一组关键词检索推特,但有一定的限制,例如每分钟调用每分钟的电话数量。此外,我们使用一组来自不同数据来源的额外关键字来进行初始关键字查询和补充。STiC的算法依赖于外勤部在Twitter上的搜索,如果每到的推文都与调用的查询关键字相关,则在整个递增过程中更新。这一评分在推特中的推算中,每个推算中,每个推算都考虑到推算中的推算结果。