Context. Social media marketing is constantly gaining interest as a powerful tool, for advertisement campaigns, in order to maximize their audience to reach potential new customers. To efficiently target customers, the knowledge of social network structure and user behavior is of crucial importance. Among these online social networks, Twitter’s popularity is rapidly increasing. Its key feature is to link different topics and posts by using the hashtag symbol. This particular characteristic is one of the principal causes that direct users to specific topics, and lead them to expand their network. Objectives. In this study we investigate a correlation between hashtags and increase of followers motivated by a specific research question. The question is whether the addition of hashtags to tweets produces new followers. Methods. We designed a controlled experiment in which we gather tweets from two types of users: users tweeting with hashtags and users tweeting without hashtags. Users tweeting with hashtags will belong to the experimental group and users tweeting without hashtags will form part of the control group. Their statistical behavior is analyzed by conducting the non-parametrical Mann-Whitney U-test. Results. The results of the Mann-Whitney U-Test show that the null hypothesis is rejected at confidence level 0.05. Based on that, a correlation is shown between hashtags and followers, therefore tweets that contain hashtags are more likely to lead to a higher increase in the number of followers than tweets without hashtags. Conclusions. This thesis contributes to describe the functionality of hahstags in the online social network Twitter. It provides an original correlational study on the use of hashtags and increase of followers. We discover that users tweeting with hashtags are more likely to increase their number of followers than users that tweet without hashtags. This discovery opens a new research direction regarding hashtags and followers, specifically to discover which hashtags increase the number of followers and which do not.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:bth-4269 |
| Date | January 2013 |
| Creators | Martin, Eva Garcia |
| Publisher | Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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