@staggerlee420 was identified as an influential twitter account for the hashtag #firerosenstein in early February 2018 (see my previous posts where we prepared Twitter data for use in network visualisation and where we identified this account as suspicious here). In this post I will look at the account in more detail to identify bot-like behaviour or any indications that the account may be run by multiple people, a company, or other ‘non-genuine’ users. Collecting and preparing tweets I first collected 3133 tweets on 20 Feb…Continue Reading “FireRosenstein hashtag analysis – hunting for Twitter bots, fakes, or suspicious accounts with rtweet. Part 3 – Investigating user staggerlee420”

Identifying suspicious accounts using rtweet In my last post I showed you how to get Twitter data for a specific hashtag using rtweet, then how to prepare it for a network analysis program such as Gephi. You can find the data files I am using in this example here on Github. From our 'firerosenstein' hashtag dataset we are now going to identify the accounts that seem suspicious, i.e. those that may be bots, or may be 'sockpuppet' accounts who may not really be the person…Continue Reading “FireRosenstein hashtag analysis – hunting for Twitter bots, fakes, or suspicious accounts with rtweet. Part 2 – Identifying suspicious Twitter bots / fake accounts for further investigation”

Hunting Twitter bots / sockpuppets with rtweet and Gephi rtweet and Gephi are two useful (and free) options to investigate Twitter data through their API. In this series of articles, I will show you what can be done with this data to try to identify Twitter bots or sockpuppets (i.e. ‘fake’ accounts run by someone pretending to be someone else). I will use the hashtag #firerosenstein as the subject of this case study. As found by the excellent @conspirator0 and friends, this hashtag was used…Continue Reading “FireRosenstein hashtag analysis – hunting for Twitter bots, fakes, or suspicious accounts with rtweet. Part 1 – Getting the Twitter data and preparing it for Gephi”

Introduction A few weeks ago, Wisconsin state election commissioners voted unanimously voted to end the use of the Optech Eagle optical scanner. This was due to reports during the recount and shortly afterwards that this machine appeared to be more unreliable than others. The above suggests that at least some optical scanners used in Wisconsin were faulty, but what is not clear is whether these faults could have been favouring one side over another. This would not be the case in counties recounted by hand;…Continue Reading “The effect of optical scanners on reducing turnout in the 2016 Wisconsin election”

P.S. Interactive online Carto maps of the vote in 2016 and the change in vote from 2012 to 2016 accompany these posts (municipality level). Go play with the data! Also, the code underlying the analysis described here can be found at Github. The .csv file used in the models on this page are named modeldfward.repunit.r.csv (original data). Introduction A previous analysis suggested that the Wisconsin 2016 vote may have been affected by the extent of the use of the AVC Edge voting machine (also known…Continue Reading “Republican vote 2016 with voting machines at reporting unit level”

Introduction My previous analysis suggested that the Wisconsin 2016 vote may have been affected by the extent of the use of voting machines (also known as touchscreen voting systems, or direct recording electronic voting, DRE, machines). This post shows that the use of Sequoia Dominion AVC Edge voting machines in Wisconsin municipalities in the 2016 Republican primary and 2016 Presidential election was strongly and positively correlated with higher votes for Trump, even when considered alongside a large number of municipality-level demographic, income, educational, and election-related…Continue Reading “Influence of Sequoia AVC Edge touchscreen use on the Wisconsin Trump vote”