Andrea Titolo

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73 Following
156 Posts
Landscape Archaeologist, PhD, currently Research Fellow in Rome. Interested in all the things #Archaeology, #LandscapeArchaeology, #AncientNearEast, #RemoteSensing, #GIS, #QGIS, #RStats, #Rstudio, #CulturalHeritage, #HistoricLandscapeCharacterization, and #FOSS in #Academia.
@anthroyeti I’ve used your series of videos both for my personal self-learning during PhD and as reference for my students this year. Thank you for those, it’s great to have you here and welcome!

Now on FOSSAcademic: a critique of yet another Mastodon #scraping academic study, Nobre et al's "More of the Same?: A Study of Images Shared on Mastodon’s Federated Timeline"

https://fossacademic.tech/2022/10/18/notesOnNobreEtAl.html

I'd appreciate comments/critiques of my comments and critiques. Note that the comment system on my blog is broken, so reply to this 'toot', if you would.

More Mastodon Scraping Without Consent (Notes on Nobre et al 2022)

There’s a new paper out about Mastodon! But unfortunately, it’s a deeply problematic one. Nobre et al’s “More of the Same? A Study of Images Shared on Mastodon’s Federated Timeline” is a paper that is now published in proceedings from International Conference on Social Informatics. (Unfortunately, it’s not open access.) Because I’m currently researching the fediverse and blogging about that process, I thought I’d write up notes on this paper. Why this paper? Frankly, because I’m pretty certain it violates the community norms, as well as terms of service, of many Mastodon instances. It instantly reminded me of the controversial paper from Zignani et al, “Mastodon Content Warnings: Inappropriate Contents on a Microblogging Platform”, which resulted in a scathing open letter and the retraction of a dataset from the Harvard Dataverse. Nobre et al’s “More of the Same” is a study of image-sharing. The authors claim that it is about image-sharing on Mastodon, but really their focus is on images they culled from Mastodon.social’s federated timeline. They pulled 4M posts from 103K active users, of which 1M had images. Since they pulled posts from Mastodon.social’s federated timeline, they saw posts from 4K separate instances. The authors state that a “relevant number” of the images they found are “explicit.” They categorize the images as such after running them through Google’s Vision AI Safe Search system. They also run the images they find through Google’s image search to trace where the images came from and how they are shared on Mastodon. Ultimately, the authors don’t really make an argument, other than stating in passing that Mastodon needs better moderation, since people share explicit images. In some ways, “More of the Same” lives up to its title: it’s more of the same poor scholarship that can be seen in Zignani et al (in fact, Nobre et al cite that controversial paper). Here are my critiques:

FOSS Academic

Haven't seen this here yet and maybe others are interested too: @benmarwick (thank you) created a bot that tweets about #archaeology papers that include #rstats code, available at: https://twitter.com/archpaperscode

source code: https://github.com/benmarwick/archaepaperswithcode

archaepaperswithcode (@archpaperscode) | Twitter

Die neuesten Tweets von archaepaperswithcode (@archpaperscode). I'm a bot that tweets updates to this list of archaeology papers that include #rstats code: https://t.co/wcjWzlzfZv…

I have to say, after weeks of trying to stay on top of deadlines, dedicating a full day to digitising features from historical maps felt like the most relaxing thing ever.

Noted:

Fradley, Michael. “British Inter-War Aerial Photogrammetric Mapping in the MENA Region: Archives, Access and Research Potential.” Levant 53, no. 3 (September 2, 2021): 336–46. https://doi.org/10.1080/00758914.2021.1992879.

"... missions in the period from the First World War through to the start of the Second World War... survival and archiving of these collections... current issues of access... overall archaeological potential ..."

MENA: Middle East and North Africa

#archaeology
#HGIS
#AncientGeography

I just added an article to the #PleiadesGazetteer entry for the ancient site of Nineveh:

Campana, Stefano, Matteo Sordini, Stefania Berlioz, Massimo Vidale, Rowaed Al-Lyla, Ammar Abbo al-Araj, and Alessandro Bianchi. “Remote Sensing and Ground Survey of Archaeological Damage and Destruction at Nineveh during the ISIS Occupation.” Antiquity 96.386 (April 2022): 436–54. https://doi.org/10.15184/aqy.2022.14.

https://pleiades.stoa.org/places/874621

#AncientPlace #archaeology

@ArchaeoBasti congrats!

#OutNow: My first co-authored article in a peer-reviewed journal. We investigate how different computational approaches fare with complex literary texts: https://doi.org/10.3389/fdata.2022.886362 (open access).

Summary in thread 🧵

@sociology @oneabstractaday #socialscience #methods #textanalysis #dictionary #wordembedding #scaling #machinelearning #sentiment #complex #literature

Examining Sentiment in Complex Texts. A Comparison of Different Computational Approaches

Can we rely on computational methods to accurately analyze complex texts? To answer this question, we compared different dictionary and scaling methods used in predicting the sentiment of German literature reviews to the “gold standard” of human-coded sentiments. Literature reviews constitute a challenging text corpus for computational analysis as they not only contain different text levels—for example, a summary of the work and the reviewer's appraisal—but are also characterized by subtle and ambiguous language elements. To take the nuanced sentiments of literature reviews into account, we worked with a metric rather than a dichotomous scale for sentiment analysis. The results of our analyses show that the predicted sentiments of prefabricated dictionaries, which are computationally efficient and require minimal adaption, have a low to medium correlation with the human-coded sentiments (r between 0.32 and 0.39). The accuracy of self-created dictionaries using word embeddings (both pre-trained and self-trained) was considerably lower (r between 0.10 and 0.28). Given the high coding intensity and contingency on seed selection as well as the degree of data pre-processing of word embeddings that we found with our data, we would not recommend them for complex texts without further adaptation. While fully automated approaches appear not to work in accurately predicting text sentiments with complex texts such as ours, we found relatively high correlations with a semiautomated ap...

Frontiers
@ericireland you’re right my mistake, I was thinking about the visual markdown editor introduced in 1.4, but it indeed works only there, otherwise you have to do everything manually as you mentioned. Citr instead allows you to do it in the regular editor, guess I should definitely try it 😊

Throwing myself at the mercy of the fediverse here, looking for help with #umap (https://umap.openstreetmap.fr/en/).

I'm trying to generate a map with icons, including description and image.

I can do all that via the umap interface directly, but I am struggling (more than 1hr searching online) with improving my workflow to enable simple export from JOSM (or csv) and import to umap. I can't get the additional (to lat/lon) info to import.

Any help gratefully received! #osm

uMap - Online map creator

uMap lets you create maps with OpenStreetMap layers in a minute and embed them in your site.