Instructions: We have moved on to our final Quantitative Research Method: Content Analysis. In this activity, I have collected 10 tweets that discuss Earth Day. Please use the codebook created for this activity to analyze the 10 tweets. What trends do you see just from these 10 tweets about Earth Day? What was this process like to you? Attached are the requirement for this assignment, and materials you will need to answer the questions. 5 attachments Slide 1 of 5 attachment_1 attachment_1 attachment_2 attachment_2 attachment_3 attachment_3 attachment_4 attachment_4 attachment_5 attachment_5 Take Home Activity #6 Content Analysis Research Due: April 28th @ 3:40pm Instructions: We have moved on to our final Quantitative Research Method: Content Analysis. In this activity, I have collected 10 tweets that discuss Earth Day. Please use the codebook created for this activity to analyze the 10 tweets. What trends do you see just from these 10 tweets about Earth Day? What was this process like to you? Please upload the following on iLearn: 1. Your analysis of the messages (including the codebook for each message) 2. What trends you saw from your analysis 3. An in-depth explanation of your reaction to this process. Please connect it to relevant concepts discussed in class You can earn up to 15 points for this take-home activity. Please upload the completed assignment in either a Word document or PDF, where Dr. Hinck can give you feedback and score. Please upload the file as: Lastname.firstname_THA6. Codebook: Twitter Message #: _______________ Date Broadcast: _________________ # of likes in the post: _____________ # of re-tweets in the post: _________ Unit of Analysis Coding Categories Message tone ____celebratory ____informative Emotional tone ____happy ____sad ____anger Visual present: ____yes (If visual present) type of ____not present image ____Person/persons ____other (explain) ____persuasive ____surprise ____fear ____disgust ____no ____nature ____architectural structure Codebook: Twitter Message #: _____one__________ Date Broadcast: _______April 22nd__________ # of likes in the post: ____1K_________ # of re-tweets in the post: ___544______ Unit of Analysis ?Find this one on the Photo I sent you from our textbook) Message tone _?_celebratory ____informative __?__persuasive Emotional tone __?__happy ____surprise ____sad ____fear ____anger ____disgust Visual present: __?__yes ____no (If visual present) type of ____not present _?___nature image ____Person/persons ____architectural structure ____other (explain) + Chapter 8 Content Analysis: Explaining and Interpreting Message Categories Table 8.1 Textual Units of Analysis Unit type Examples Definition Spatial location on a website; lead versus Physical distinctions Amount of space or time devoted to content nonlead position in a newscast (e.g., Druckman, 2004) Syntactical distinctions Individual words, phrases, sentences, or images Hashtags (e.g., #BCAM, Thackeray et al. 2013) or mentions of wealth in newspaper stories about political candidates (Conway, 2006) Referential distinctions Segments of text that share some important aspect (e.g., mention a certain thing or place) Pro/anti-gun control advocacy messages on organizational Facebook pages (Auger, 2014) Sentence clauses Propositional distinctions Mentions of gubernatorial candidates’ Hispanic ethnicity (Conway, 2006) Thematic distinctions Topics based on subjective meanings Types (policy vs. character) and functions (acclaims, attacks, defenses) of presidential candidate talk (Benoit, Pier, Brazeal, McHale, Klyukovksi, & Aime, 2002) Note. These units are organized from least to most difficult for achieving valid, reliable unitizing and categorizing decisions. From Content Analysis: An Introduction to Its Methodology (2nd ed.), by K. Krippendorff, 2004, Thousand Oaks, CA: Sage. individually, each viewing different news progra divide the content into progressively more specifi of analysis (broadcast source, characters, crim tions, and crime types). After checking to see that their unitizing de were reliable, the coders then categorized a smal to eaningful data, it is much harder to achieve consis- at unitizing and categorizing decisions for them. For ample, if you analyzed teacher evaluations on Rate- Professor.com, you could categorize all references to eacher, including he or she, Dr. X, Professor X, the tructor, and so on. That would allow you compare e relative frequency of different forms of reference and mpare what praise and blame is associated with par- ular references to an instructor. Depending on your claim, you may need to divide ar data into progressively smaller, more specific units analysis. Let’s consider an example from published earch. In a study of ‘racial representations of perpe- cors, victims, and officers’ on Los Angeles television vs programs, Dixon (2017) randomly selected 117 vision news programs fro of stories and checked to see whether their cate_ decisions were reliable. Only then did they pro code all 117 stories. The coders could watch each many times as they wished to make their unitiz categorizing decisions. Figure 8.4 shows what th book, a form used to record decisions about whi belong in which categories, might look like forth Coding data is th Purchase answer to see full attachment Tags: Quantitative Research emotional tone analysis of the messages Syntactical and Referential Distinctions type of image User generated content is uploaded by users for the purposes of learning and should be used following our company’shonor code & terms of service.