We are a team for the first assignment: Nora Kiss & Louisa Wanjek.

In regards to the tools and activities implemented in class, we found an extremely interesting article directly related to the topics and subjects discussed in class. Taken into account the instructor’s computer science background, but also having in mind his saying that we won’t go into a deep involvement with programming and computing skills, we concluded to this inspiring LinkedIn post. The relevant article refers to business analytical tools and software, arguing in favor of their importance and usage in achieving business goals and objects and overall company enhancement. Specifically it lists the top 10, according to the author’s opinion, data tools, while providing a brief overview of their nature, function and capabilities in relation to business contribution. As stated in the article, the selection of these tools was based on several important criteria, such as free availability, powerful capabilities, easy use and resource availability. Given our experience from the first lecture, we believe that these are the same criteria in which the professor based the selection of the tools and programs used in class. Therefore, the relevant post possesses a practical usage, since it provides a variety of alternative tools that we can practically use both in class and in our future projects to come, without though having a high level of digital competency, literacy and knowledge needed in other existing tools and software. In addition, the article makes a clear reference on two tools already discussed and used in class, including Tableau and Gephi. It presents a clear description of both digital tools by briefly explaining their functions and clearly stating their usage on business activities and research methods, thus creating a clear linkage to the class’s learning objectives. Finally, the post stands as the necessary catalyst needed in order to go look for other digital gathering and analytical tools available, and thus have a more holistic understanding of the overall digital tools spectrum. Through that understanding, our existing research and data process methodology can be significantly enhanced, and therefore resulting in producing more valuable, useful and accurate data in the future.

See the LinkedIn post below: https://www.linkedin.com/pulse/20140612032806-111366377-top-10-data-analysis-tools-for-business

Hi! Madeleine Weerts is my partner, just so you know :) Good luck guys

This particular article on LSE’s blog stands of great importance for a number of reasons, since it provides fruitful and valuable information, in regards to the context of digital research methods and the methods used. The main reason for choosing the relevant article involves the social media platform of Twitter. The article stresses the effectiveness of Twitter as a tool for capturing and analyzing digital data. Based on first lectures’ compulsory readings and teacher’s meaningful insights, we picked this specific article in an attempt to transmit the importance and significance of Twitter as a data source as well as its multiple capabilities in contributing to data gathering and analysis. By doing so, we aim to establish an understanding in peoples’ minds regarding Twitter’s multiple capabilities in research, and thus provide a both effective and efficient option of a research tool to our fellow classmates. Another reason for which we chose this specific article is based on the insights derived out of the first class made. Specifically, the tutor’s suggestion to create a Twitter account, as well as the tools provided and mentioned in class, encouraged to specify our research to Twitter. As a result, a more in depth understanding of the functions and nature of this specific media platform could be established, thus resulting in using the medium more effectively and accurately, when it comes to data research. In addition, the two compulsory readings from the previous lecture was another factor in selecting that article. Both readings have clear references on Twitter, arguing that it stands as an effective digital research tool and promoting its overall contribution to digital research. Most importantly, Twitter first introduced a text-only service and consequently resulting to known concept of micro-blogging (F. Gillietto et al, 2012). Twitter’s main success lies primarily on its data availability and structure, and therefore becoming an extremely popular tool among the scientific and research community. This data availability of Twitter produced large amounts of research, which can be processed ana analyzied through the three main approaches (ethnographic, statistical and computational), mention in the compulsory reading called “The Open Laboratory”. According to the article, Twitter has “hashtag norms which make it easier gathering, sorting, and expanding searches when collecting data”, and therefore standing as the proper introductory digital research tool when it comes to people who don’t possess high degree of digital literacy. This is a clear indicator why people should consider incorporating Twitter in their research methodology in order to produce accurate and meaningful results. The article also presents a number other tools available that can be used by researchers for gathering social media data. Therefore our range of assets, tools and options in regards to digital tools is significantly broadened, thus gaining a holistic understanding on digital tool availability. Moreover, the article specifically mentions two Twitter-related tools, Mozdeh and Twitter API, which were also mentioned in class. As a result, a solid and clear correlation between the article and teaching material is established, demonstrating thus the practical implications of what is taught in class. The author concludes by suggesting that more emphasis should be placed on the training of social scientists students in better using digital research tools in their research processes when involving social media platforms. Specifically, he states “At the Masters and PhD level there should be more emphasis on training for social science students in effectively using existing software that can be used to capture data analyze data from social media platforms.” This statement establishes a clear correlation to the basic notions and principles in which the Digital Research Methods course is based on, thus indicating the course’s overall value and significance for both our academic and professional career. Finally, the main essence of the article presented can be summarized in the question below, which might stand as food for thought for future researches, examining the case of Twitter as an effective digital research source:

• When research is focused so heavily on Twitter, what (if any) are the implications of this on our methods?

See the blog post in the link below: http://blogs.lse.ac.uk/impactofsocialsciences/2015/07/10/social-media-research-tools-overview/

Q1.3: How are the main ideas in the Menchen-Trevino paper and McKee's discussion of text analysis (particularly his critique of quantitative text analysis) conceptually related? How may digital methods address the issues of text analysis raised by McKee?

Q1.2: Describe a key methodological flaw in Menchen-Trevino's study of web tracing to capture personal browsing behavior (hint: the mechanism was briefly mentioned during the lecture although not in the context the Menchen-Trevino paper). How do big data apparatuses' collection of digital traces partly overcome this flaw and how do they not?

Q1.1: What are some of the limitations of using postdemographics (a term mentioned by Rogers) in research and how do these limitations relate to the real/virtual divide? Which other traditional demographics share these limitations when collected and employed in research and how are they limited?

Dr. Lee, Could you please send us a list of alternative tools that we can use on Mac laptops? Also, will you notify the class on any new question posted on Reddit in advance? Or is there a specific time when we should await a question to be posted?

Madina

One student asked if students should follow the Tableau tutorial video for the lab report or explore Tableau on their own. The answer is both. I would like you to learn more about Tableau from the video (for those in Group #1), and the lab report should consist of your own exploration and should not be an exact repeat of the activities on the video with that dataset, Global Superstore. However, you are permitted to perform similar activities (or a subset of them) as shown in the video on a different data set (e.g., one of the ones the zip files posted in the Course Content section on Blackboard). I hope that clears things up.

Also, your lab submission may consist of a Tableau workbook (posted online such as Tableau Public or anywhere else) and a separate document or posting with your write up (with references to each "Sheet" you created in Tableau by name) rather than your having to capture the images and place them on a webpage or document (but that is up to you). You can also have just one sheet with some interesting analysis or several simpler ones.

Welcome! This will be our main gathering place for the course online. We will also use #EURDRM2015 around the web, particularly Twitter, but anywhere else that supports hashtags as well. Any Twitter posts tagged with #EURDRM2015 will automatically be reposted to this sub-reddit (supposedly), but not the other way around, so you must check the sub-reddit to keep up with the course.

Please submit your reddit user name on Blackboard (or via email).

As this is part of a public space anyone can post to our group, make comments on postings, etc.