Discuss and Assess "Assessing the Strengths and Weaknesses of Hashtag Analysis"

The researchers from both articles, in the broad sense, are trying to show how discussions, social issues, discourse, and the different participatory outlets in the public domain of the digital sphere impact the rest of the social world. They are trying to show that public opinion and participatory action are evolving in the public arena. It includes this digital perspective that is legitimate and very influential on the general public and the bodies of government. The researchers from both articles use a mixed-methods/multi-methods approach. They are getting their data from a Twitter API. So they use Twitter users' metadata analyses, network mapping, qualitative content analysis, content-coding, and close textual reading techniques from the data collected from the API. Overall, the researchers are trying to help the reader understand the influence and different ways these digital technologies, such as Twitter, have framed fundamental systems and concepts in our society, such as social-political action, connective action, and digital political engagement/participation. They argue that the digital space has created new methods and avenues for understanding the logistics, dynamics, engagement, and participatory nature of digital communication systems, platforms, and technology.

As discussed earlier in how both researchers use the Twitter data, why are they employing these methods of content analysis or network mapping, for example? These multi-method/ mixed-method approaches first allow for an increase in the reliability of the final results and analysis. Also, not only is one component of the Twitter space analyzed, such as a user's profile data, but multiple components of the Twitter space are analyzed and cross analyzed against one another, which can allow for further discussion. Network analysis, for example, is a tool for tracking and mapping patterns of social interaction with certain hashtags that researchers find interesting for their field of interest; for instance, Barisione, Michailidou, and Airoldi did this with the hashtag #RefugeesWelcome. In the end, "the quantitative and qualitative analysis of users' metadata(number of followers, location, public profile description, number of retweets for a specific message, language) and message of contents" are excellent methods to identify the types of participation and engagement within a particular discourse (Barisione, Michailidou, and Airold, 1151). These methodological approaches to coding would be inductive because the researchers gather all of this Twitter data first and then analyze it and come up with broad generalizations to a particular population. They collect specific observations such as a Twitter user's data, and then the content analysis is conducted to make large generalizations and develop theories.

Some weaknesses in using this Twitter data are possible errors in the data's validity because there are many bots online that are not human but computerized systems, which would not help the final analysis of the data(Barisione, Michailidou, and Airold). Another weakness includes the ethical portion of the data you are retrieving from the Twitter API. For example, in Pond and Lewis's article, I mean, four million tweets were downloaded from the Twitter API for their research. That is a huge dataset, and I don't think you can guarantee the informed consent of all the participants they collected data from. I think something I would do differently when using specific hashtags to collect data is to analyze the same hashtag in different languages, so it includes more of the population. Not everyone speaks English on Twitter, and so to add a more diverse dataset of individuals, I would look around Twitter for hashtags that mean the exact same thing but just in a different language. For example, when Pond and Lewis talk about hashtags related to the UK riots and how they looked at hashtags such as #occupy or #resist, they could have missed an essential portion of the population since they didn't include those words in other languages as well. For example, occupy translated to Spanish is ocupar, and so some individuals may have used #ocupar (also, I speak zero Spanish, so hopefully, this is the correct Spanish translation). Looking across multiple social media platforms would have given the authors even more generalizability with their findings since they solely looked at Twitter data. On the other hand, I liked how both authors took a mixed approach and looked at different ways of analyzing the Twitter data. I would definitely do that in future research in order to increase reliability and validity. In conclusion, in Barisione, Michailidou, and Airold's article, their findings found that the hashtag #RefugeesWelcome became so popularized that it attained the status of being known as a "digital movement of opinion," and so since this hashtag was backed by celebrities, news media, institutions and those alike an influential digital and public voice was created which provided legitimacy to those conducting important work with the refugees the hashtag aimed to help. While in Pond and Lewis's article, their findings from the Twitter data found that to understand communication technologies fully, one must define, describe, and observe the systems' interactive dynamics. The authors looked at the hashtag #OperationCupOfTea, where it is arbitrary on its own but once defined, described, and observed, the significant meaning and ideology behind it is understood to where impact and legitimacy are then made.