Social Media Strategy

Social media! This should be a cakewalk, right? After all, I am not only a Millennial (I cringe at the use of that word, I know) but also a former social media intern (yes, it’s a real job, Mom!) for two organizations. But now I have to craft my very own social media strategy identify target audiences for my project. Suddenly this isn’t as fun as simply sharing my favorite Mental Floss article. (In case you’re wondering, it’s this one.)

My project targets three groups: residents of my hometown, art lovers, and World War II history students.

I’ve probably explained it before, but I intend my project to be a gallery of photographs of artwork created by Gordon C. Mannix, a soldier from my hometown of Plainville, Connecticut, who died during the Battle for Normandy in 1944. Gordon was originally written about by a member of the 2013 “Price of Freedom” class, so I was unable to write a biography for him. Because my high school has an award named after him, I still wanted to learn more about him, so I’ve been in touch with his niece. I knew from Gordon’s biography that he was a very talented artist who was supposed to attend Parsons School of Design on a full scholarship–until he was drafted into the Army by Uncle Sam. Besides feeling devastated that Gordon’s life ended when he was 19–younger than I was when I took this class in 2016–I was deeply saddened by the idea that his artwork would never be seen, never be in a gallery. When I met with Gordon’s niece last year, she allowed me to see his beautiful sketches, and I knew that Gordon was even more talented than the complimentary descriptions.

The idea of showcasing his artwork germinated in my mind, but the art was old and drawn by someone who never had the chance to become famous. After going over potential ideas for final projects for this class, I decided that this project was the perfect chance to give Gordon’s work a chance to be seen by a larger audience. With his niece’s permission, I have taken photographs of his drawings. While they are not of the best quality, I do have documentation of almost all of his work, which provides multiple perspectives on Gordon’s interests.

I am the kind of person who would spam everyone’s inbox with a link to this online gallery, but I need to use a strategy that doesn’t get me blocked by everybody. Let me break it down group by group.

Group 1: Citizens of Plainville, CT

Since the reason Gordon and I are connected is because of my hometown of Plainville, I think it’s an appropriate target audience. While nobody in Washington, DC, knows what or where Plainville is, it’s not small enough for me to go door-to-door and tell everyone about this project. (Besides, that would kind of go against the spirit of the project, right?) Fortunately, that means it’s sizable enough where I might be able to get more traffic than expected, even if I just pull in 10% of the town. (I’m being very optimistic, but oh well.)

Group 2: Art Lovers

Very broad, but since Gordon is an artist, and since most of my descriptions will focus around what’s in the drawings/watercolors/comics, I’m sure there will be an audience out there who is just focused on the aesthetics.

Group 3: World War II Historians/Enthusiasts

Though Gordon and I both lived in Plainville and went to the same high school, I discovered his story because of my class on the Battle for Normandy. Other students in my class would be interested in such a gallery, but I dare to dream a bit bigger and maybe get people who are just interested in World War II history in general, specifically on providing biographical details for the names in every WWII ABMC cemetery.

To reach these audiences, I plan to create new Facebook, Twitter, and Instagram pages. I don’t think a blog is necessary in this medium: the gallery serves the purpose of what a blog would. I could feature some of his art on this blog here, but I feel like that would do Gordon a disservice, to have his beautiful drawings next to my terrible puns and rambling thoughts. So no blog. No Pinterest, because I’m not selling his artwork–that would just be wrong.

Facebook would give me the most reach. It is also the easiest way to target people that I know personally who might be inclined to share this page with people who belong to my target audiences (as well as others!). Facebook has the most users, and of the three platforms I plan to use, it allows for the longest posts, allowing me to go into detail explaining his art.

Twitter may have the character limit, but it allows me to reach organizations that I would have no hope of reaching otherwise. Our local newspaper, The Plainville Citizen, is more likely to see a Tweet mentioning them than if I tagged them in a Facebook or Instagram post. It also has the farthest reach potential. Since most Facebook users have some privacy settings, the posts could just end up bouncing around the Plainville network, not reaching any other groups. On Twitter, things can go far because of the power of the Retweet. It is also easy to accumulate followers on Twitter, since users are quick to follow you back on this platform more so than Facebook.

Instagram is the most specific platform for this project. A platform that is photo-based seems to be a natural platform to showcase artwork. The only downside to Instagram is that links within posts aren’t available (yet), so users would have to go to the profile page and click the link in the bio, which can be a lot of steps for a person on the internet.

Since a lot of Gordon’s art consists of fashion sketches, I’m thinking–especially on Instagram–of using the hashtag #OOTD as the template for my messages. I can describe the outfits that Gordon has designed, much as an Instagram celebrity touts what she is wearing that day, from hat to shoes to everything in between. Twitter will probably follow in the same vein, but messages cannot go into detail. They’ll contain only the barebones of details of an outfit: hat, jacket, pants, heels. It’s better than having me list out the color and style of everything: no one wants to read a 7-part Tweet if I’m not a celebrity. Remember: Twitter is more about reaching out to people rather than curating exceptional content. On Facebook, I’ll pitch the message of remembrance: this is how we can remember Gordon. Gordon was more than a mortarman; these drawings more accurately display who he was, what he was interested in. The end goal of all these messages is one thing: share, share, share! I’m not doing this to boost my SEO; I want Gordon’s drawings to be seen, to be appreciated. They have always been appreciated by friends and family, but I want to show new audiences his work, since that’s what he deserves.

I can’t possibly run these social media accounts forever; interest dies down eventually, especially when there is a limited amount of content to post. I think doing the bulk of the posting for a month after the gallery’s launch date will be the best time to draw people’s attention. As much as I’d like everyone to see these drawings, I don’t want to crash my website, and I’d also not like to set my expectations too high, as I know internet apathy can be just as strong as internet rage. If I can get to the point of having 50 followers on each platform, I would be very happy. (I have no idea if I’m setting these goals too low or too high, Dr. Robertson!) I don’t know if I can make these profiles into business accounts without, you know, being a business. Otherwise, I could use fancy analytics to measure my reach, but I think it’s best to stick with standard accounts and measure reach through followers. Another good indicator is if each post is shared at least twice. (I say twice since I know my mom will probably share it, and I feel like saying “once” would be cheating.)

I’m trying to combine what I’ve read recently with what I’ve learned in the past year and a half, but I hope this social media strategy works, as I think the goal of getting as many people as possible to see Gordon’s artwork is worth sending out a Tweet, posting a photo, or pasting a link–and much more.

What Can You Do With Crowdsourced Digitization?

At first, crowdsourcing seems like handing off tasks to people and not paying them, which doesn’t sound great at all. However, crowdsourcing is best described as a “it takes a village” effort. These tasks would be monotonous for an hourly worker, and mistakes are bound to pop up when the same person is doing the same thing every day. Add hundreds of others and suddenly you have proofreaders and fact-checkers, all doing this because it involves minimal effort. Of course, some users love to get into the weeds with various crowdsourcing projects–all for digital badges or silly stickers–but crowdsourcing works best when it’s portrayed as a quick activity.

The easiest crowdsourcing activity is corrections. I tried this out with NYPL’s Building Inspector, which involves a user checking building shapes or fixing them himself/herself. I like this project because you’re double-checking a computer, which is less daunting than some other crowdsourcing projects. The stakes aren’t high, and you can do a lot in a small amount of time. The motto “Kill Time; Make History” makes that point best. Now, this crowdsourcing is map- and history-focused, but if you want to contribute to modern-day maps, Google Maps also uses crowdsourcing to provide phone numbers for businesses, to add new locations when a new store opens up, to show you pictures of what a building or park looks like–all because circumspect (re: picky) people provide this information.

Transcriptions involve (much) more work. I’m not going to lie: transcriptions involve time and effort. A lot of their tutorials don’t involve 1920’s ragtime piano music and cheeky text: they’re full of rules, guidelines, instructions, etc. I reviewed Papers of the War Department, admittedly because I took a class with Professor Hamner this semester. (Ask me about civil-military relations sometime!) I also love the idea of recovering information that was thought to be lost, as well as the idea of this project being open to the public. I knew the interface wouldn’t be as fun as Building Inspector, but I did expect to be able to zoom in on the letter on the same webpage as the transcription text box. However, zooming in the letter involved opening the image in another tab and zooming in with the web browser’s zoom tool rather than one built into the interface. I hate switching back and forth between tabs when transcribing, so that piece irked me. There also isn’t instant gratification with transcription like with crowdsourced corrections: 18th-century cursive script is very hard to decipher, and the spelling of even erudite men was non-standardized and absolutely appalling to any English minor. However, that is not to say that I don’t like transcribing. While I need to practice reading 18th-century handwriting, I really enjoy transcribing videos on YouTube. Several YouTube communities invite viewers to transcribe their videos and upload captions. Transcription even goes a step farther with communities who also translate videos. This past summer, I transcribed and uploaded captions for several videos for the American Veterans Center’s YouTube channel. This allows people who are hard-of-hearing (which, let’s face it, is the channel’s main demographic) to understand interviews or narration, and it also allows me to improve the metadata, if there is an interesting subject I missed and need to tag. The AVC YouTube videos could benefit from crowdsourcing–I certainly wasn’t going to caption over a thousand videos, some of which are an hour long. So if you want to release your inner court stenographer, audio transcription could be the way to go.

I enjoy crowdsourcing, but I do fear that it contributes to the “gig economy.” If someone is doing a significant amount of work, I think that they should be paid. At the very least, they should be named as a contributor, especially if the work is published. Crowdsourcing does make “ownership” a bit blurry, and if the project creators and managers aren’t circumspect, crowdsourcing can do more harm than good if someone has to go through all the mistakes and fix them all by hand. All in all, crowdsourcing needs to be monitored; things usually will not run smoothly on their own.

How to Read a Wikipedia Article

Of course, the answer to “How to Read a Wikipedia Article” seems fairly obvious: just read it! However, as I discovered when I came across an article that described Rupert Grint from the Harry Potter movies as “totally going out with Emma Watson in real life,” the information on Wikipedia isn’t always very accurate. Fortunately, there is some accountability, and it’s easy to basically CTRL+Z a page when someone goes rogue on it, which is why you don’t often see profanity-laden articles about digital humanities. If you click “View history” in the upper-righthand corner of the page, you can suddenly see a log for all the changes made to a page. Time, date, and contributor are all listed, as well as the significance of the change. Was the contributor fixing a typo? Is the contributor a general Wikipedia editor who religiously monitors the pages, a person who knows a lot about the page he/she is editing (like the majority of the editors of the “Digital humanities” page), or is it a bot or a random person looking to stir up trouble (usernames like Cheryl27 seem an easy target here)? This log isn’t very intuitive, so the information listed first and foremost is most recent. If you want to see when the page was created, you have to click on the “oldest” link above the list. (That’s how I found out that the “Digital humanities” page was, in fact, created by a digital humanist, Elijah Meeks.) If you’re more of a visual person, check out the “Revision history statistics” to see tables and pie charts, as the bland list isn’t very compelling or easy to understand.  If you like comparisons, you can compare how the page looked yesterday with how it looked a few months ago, and see where the changes are. (Unfortunately, you can’t really compare a page today with how the page was when it was created–at least as far as I can see.) When you’re done trawling through edit history, it’s worth checking out the “Talk” link next to the “Article” link on the upper-lefthand corner of the article page. This page documents the controversies (and there can be surprisingly many), from misquotes to arguments over what quotations mean. You have editors justifying their actions (including Elijah Meeks’s “meh” approach to creating the “Digital humanities” page). People can submit ideas for page changes here, as merely changing a page yourself tends to lead to wrath from the misogynistic Wikipedia editors. (Internet editors hate when newbies encroach on their turf, and this bullying has been well-documented by reporters and insiders.) I see the Talk page as a comments or reviews section underneath a online newspaper article or product advertisement. The discourse does tend to take itself too seriously, but it serves as a warning before someone reads an article and blindly takes in the information. Of course, even I wouldn’t look at the Talk page every single time I checked out a Wikipedia page–especially when I’m just checking for plot summaries of musicals, since that’s what I do in my spare time–but for academic perusals, you definitely should. Citing Wikipedia is always a dangerous game; the librarians at my high school would remind of us this by telling the story of a student who wrote a paper on the faked moon landing based on faulty internet sources. However, the “References” and “Further reading” can lead you to primary and secondary sources that are scientifically or historically robust. For the “Digital humanities” page, the list of institutions can show you where to find well-known scholars and projects. In general, Wikipedia is good for summaries and overviews–when you have no idea what game theory is and only have the patience to read one sentence about it. But as with anything free and just lying around, be sure to figure out where it came from. 🙂

Network Analysis with Palladio

I’ve always been intrigued with mapping and networks, given my background in applied mathematics. It’s refreshing to see, instead of mapping sets of real numbers to prime numbers (cue the groaning of all the non-STEM readers) this information being put to more practical use. (I hope my Axiomatic Set Theory professor doesn’t get annoyed I said that!) I really liked using Palladio to create a network, as I find networks inherently more interesting than plain maps. Maps are great for one-dimensional representations–to show relationships, however, I think it’s necessary to use networks.

We used the same data we’ve used for the past few exercises, which is metadata from interviews conducted by government employees with former slaves from 1936-1937. I found this exercise highlighted the most interesting revelations with the data. With mapping, I said that it mostly eliminates grunt work, but it doesn’t really reveal anything new. I guess the same could be true for network analysis, but I don’t think so. Human beings are terrible at grasping relationships between two sets. It’s why we need phrases like “correlation does not equal causation” but also why people can’t even believe correlation might mean something. (Cough, data relating to climate change leading to bad things.) Sifting through metadata may allow me to grasp a basic understanding of how big the Roman Empire was, but I can’t grasp that same understanding with the Republic of Letters, no matter how many times or how long I could stare at it. Therefore, the network analysis allowed me to see things I had never seen before, even with the text mining and the mapping.

We used Palladio for this exercise, which I found to be easy and intuitive. Although many of the projects we reviewed used Gephi, we used Palladio because Dr. Robertson instructed us to. Easy enough decision, then! The setup, I’ll admit, was a bit confusing, as it involved dragging and dropping .cvs files into the Palladio webpage. Then I had no idea what everything meant, but all that confusion was easily translated into networks when I hit the “graph” button. I have no idea how it translated all the metadata into a network–and I’m sure I’d have to get a degree in computer science to grasp it fully–but it translated the data beautifully, and it allowed me to choose relationships that I wanted to highlight. Just as with comparing any two sets of data, some were more useful than others. When both the source data and the target data contained large quantities, the network was too large to make any sense of. When one group was smaller, limited to say two or three categories, the relationships were plain to see. And relationships varied greatly. You could make a graph to show the relationship between where a particular former slave had been enslaved and where that same former slave was interviewed–effectively showing you where he/she was and where he/she is (at least when the interview was conducted). So that was a relationship across time, for the same person. But you could also graph relationships between people, for instance, between the interviewers and the interviewees. Which people interviewed former slaves the most? Were any former slaves interviewed twice? Suddenly these questions had easy-to-see answers. Then, to really get into what the former slaves talked about, you could graph what males talked about vs. what females talked about. However, just as Dr. Weingart said, just because you can network a relationship doesn’t mean you should. Some graphs were more visually informative than others, and others were just complete messes to both academics and laypeople. The relationships that best benefited from network analysis didn’t involve too many categories–even sorting what slaves talked about by age was a little too much. One-to-one is usually the best, but that doesn’t mean overlap isn’t important–in fact, overlap shows where common interests/places/people exist.

In conclusion, perhaps I loved this exercise just because I like networking, but even all the love of networking and graphing couldn’t make up for terrible software. I really liked Palladio, and I would gladly use it again.

Mapping with CartoDB

I’ve toyed with the idea of mapping before, but I thought mapping would involve lots of coding and headaches. Then I used CartoDB, and my life changed! (Sorry, this sounds like an infomercial.) Seriously, though, CartoDB is much, much more user-friendly than I expected. Although I had to follow instructions to create my multiple maps in CartoDB, I marveled at how easy it all was. I have to offer the caveat that I used a data set already provided to me, so I wasn’t manually entering any data laboriously. However, we’re talking about mapping, NOT metadata (thank goodness!), so let me begin.

The first type of map–the “dot map“–is fairly basic. There’s not much difference between this map and drawing dots on a physical map. However, you do get to select the background map, making it look slick and chic or going with an old-timey look. The background can convey a lot about what kind of data is being presented, so I liked this aspect of CartoDB. The other difference that makes this superior to a regular map and pen is the “hover” or “click” pop-ups, which, as the names suggest, allow information connected to each dot to appear whether you hover or click your cursor on it. I enjoyed the pop-ups because, when graphing in math class, I felt like legends were inadequate to convey all the information I wanted to. This solved that kind of problem perfectly.

The second type of map I tested out was the “animated map.” In this map, dots appeared and disappeared on the map to indicate when each interview was conducted. I really enjoyed this map, even if its information was very limited (i.e., the dates of the interviews). However, if this is the information you wanted, then you wouldn’t have to hover over dozens of dots to find out every date. Also, the animation provided a great insight into the frequency of the interviews across a period of almost a year, especially the months when the most interviews were conducted.

The third map I created was the “heat map,” which I definitely liked the least. I can imagine this kind of map would be useful if there are multiple data points in one town, say, as the dot map tends to let one data point cover up the others. However, since the interviews were relatively widespread, I don’t think this map was the best choice. I can see its uses, however, so don’t write it off. As always, you have to think about the information you want to convey, and the best medium for doing so.

We called the fourth map a “category map.” It’s a bit harder to describe this one, but basically this was a more interactive dot map. I was able to select widgets that allowed you to see the map through different categories: gender of the slave, name of the slave, where the slave was born, etc. True, this was all information available with the pop-ups on the dot map, but I enjoyed the idea of the visual presentation of certain information. Once again, it’s about accessibility of the information. It’s far easier to see dots of two colors and think, “Oh, there are the males and there are the females,” rather than clicking on every single dot. Plus, since I could only select a few widgets, I could focus the dots on the slaves, rather than on the interviewers. The animated map focused on when the interviews took place, which places emphasis on the project to document the slaves, not actually on the slaves themselves.

I suppose with the last map, the “layered map,” I was supposed to utilize all the information I previously learned about creating these maps. However, I couldn’t really use my two favorite types of map–animated and category–so I went the uncreative route and used two dot maps in different colors. The point of the layered map is to showcase multiple data sets on one map, so this map, for instance, shows where slave interviews were conducted and also where slaves were enslaved. However, I wished there were more of a relation between the two data sets. I did get to see that while the interviews were conducted in Alabama, the places people were enslaved were in Louisiana, Alabama, and Mississippi, to name a few. I suppose this might be a fault of mine for expecting too much of CartoDB and of the data sets (they might not have represented the same people, after all), but then I think that eliminates the spirit of a layered map. I wouldn’t put two unrelated data sets on the same map–that wouldn’t make sense. So I wanted to see more of a connection, and there wasn’t one.

Overall, CartoDB was very user-friendly, and it’s a great way to showcase data.

Text Analysis with Voyant

http://docs.voyant-tools.org/start/

Voyant is an online tool (although it requires a download as well as a Java install for that cool Voyant 2.0) that allows you to see/map the frequency of words in a given document. Thankfully, you can paste .txt documents into Voyant, sparing you the need to copy the entire Declaration of Independence in one frustrating scroll. Also, you can copy multiple documents into Voyant, allowing wonderful compare/contrast exercises between the corpus–all of the works–and the individual documents themselves. The five main tools are Cirrus, Reader, Trends, Summary, and Contexts.  Cirrus is basically a fancy word for “Word Cloud”: it gives you a very visual and colorful representation of which words appear most in a given document. Reader, as its title suggests, allows you to read through a whole document–or a whole corpus, if you’re feeling really ambitious. However, it is most useful for allowing you to scroll over certain words to see how many times they appear in that particular document. Trends appeals to my math-loving side because it is a graph of how often a selected word (from the Cirrus tool) appears in the corpus. And if you want to get really fancy with Trends, you can see a graph for how often a word appears in just one document, so you can see if there are unusual spikes within one set even if there aren’t any present in the whole corpus. Summary is also self-explanatory, but it provides the numbers for everything. There’s nothing visual about summary; it’s all about words, words, words–and numbers, I suppose. If you don’t know every document’s length or vocabulary density, the Summary tool will figure it out in a pinch. However, Summary’s most useful category is Distinctive Words, which allows you to see which words appear in one document and no others–which means you don’t have to trawl through the Cirrus or the Frequency tools to see where the gigantic spikes are. Finally, the Contexts tool appeals to my writing-loving side, as it shows you all the surrounding words when it comes to the term/word selected. For instance, Cirrus or Trends couldn’t tell the difference between the nice Mrs. Burns or a fire that burns the whole town down. You could check through the Reader, but frankly, no one wants to do that. Contexts shows you, “Okay, this use of the word means the town was razed.” It stops you from jumping to crazy conclusions, which I am all for.

In conclusion, I find Voyant to be a very useful tool, and even one it could get easy to lose oneself in. However, a lot of Voyant isn’t very intuitive to use, especially features like getting not-so-frequent words to appear on the Trends graph, which involve selecting the word in various other places like a complicated game of leapfrog. Also, the exporting the information was the bane of my existence for this exercise, as exporting a Trends graph didn’t always export it as a Trends graph for “house” in Virginia, but for the whole corpus, which wasn’t selected in the first place! Oh well. Look at your work before you export, kids.

A Guide to Digitization

When digitizing an item, I think of the five senses (there are more, but whatever, let’s stick with these five): sight, sound, touch, taste, smell. With digitization, it’s pretty hard to capture taste and smell, so we can safely set those two aside.

Sight is our most valuable sense, and so one of the obvious forms of digitization is through photos of objects. However, the photo–while it presents sight well–is very one-dimensional. It is hard to determine depth, size, and texture from a photo, especially if there are no measurements around the object. Sound is completely out of the picture (heh heh). So I would reserve photos for text documents or for objects that are best viewed from one angle, like signs or labeled containers.

Another one-dimensional mode of digitization is the audio recording. While I enjoy podcasts and audiobooks as much as the next person, this means is also limited–just to sound. It is possible to capture more through creative use of sound–a car driving by, city noises, the clink of glasses–but it suffers from the same problem the photo does.

Therefore, my prescription is video.  It is the best we can do: marry the audio and the visual. With the two together, it is easy to make up for the deficits of both. Actually, the video adds more than just sight and sound: it adds movement. In a photo, you can see a car, but it’s stationary. You can only see one side; it may be a side that’s very useful to see, but it’s impossible to see another side in that one photo. In an audio recording, you can hear the squelch of the tires and even hear that white noise of a car driving down a road, but that’s it. In a video, although it’s impossible to touch the car or feel the motion of driving in one, you can see how fast it drives by. The combination of sight, sound, and movement almost lets you imagine that you can feel: feel the movement of driving when you see one whiz by, feel the texture of the seats when you hear the sound and see someone else touching it. Video doesn’t just combine: it adds.

Digitization is great. Digitization allows you to tour the Louvre without ever leaving the United States, or to hear Dr. Martin Luther King, Jr., deliver a speech that was given before you were born. However, digitization, like other forms of disseminating information, is reliant on point of view. Does the uploader have an agenda? Has this digital photo of this painting been altered in any way? Even if you have full trust in the source of the digitization, there are still limitations. You are still restricted to how the the photographer/recorder wants to present the object. Maybe you would flip that bowl over and realize it’s actually a hat, but the person who photographed it just left it upside-down. There can even be defects on the technical end–the example that immediately comes to mind is Neil Armstrong’s “one small step for man” quote. Armstrong has stated that he actually said, “One small step for a man.” If he weren’t there to correct the recording, we all believe what we hear–in fact, we still quote it as “one small step for man.” There will always be limitations. We could go over the surprisingly intense debate over whether there is anything inherently wrong with a copy of the Mona Lisa versus the actual Mona Lisa, but it must be acknowledged that most people feel something is different in their gut. Maybe, in some ways, we’ll never be satisfied with digitization.

Or maybe we’re all living in a simulation, as Elon Musk believes. Who knows? ¯\_(ツ)_/¯