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4150	8290	All right. Hello, everybody. My name is Master Chen, and you are watching or listening to
8290	14830	Twitter Word Frequency. Welcome to my talk. All right. So, let's start with who I am,
14830	19630	or who am I. I'm a prior B-Side speaker, where I spoke about being a con man and
20210	27950	working in Vegas surveillance. I spoke at DEF CON on VoIP security, as well as DC Sky Talks
27950	31710	on automate your stocking, which we'll kind of cover a little bit about today.
32930	37690	And most recently, the Recon Village in the last few years, where we talked about
37690	42170	stalker in a haystack, URL shortened by any other name, which was last year,
42170	47110	and of course, this year and this talk. It's virtual now.
48370	55470	You can find me at ChenBox. That is my handle on most social media. So, feel free to follow.
56730	60170	All right. So, let's get into this, and let's start from
60170	66090	where we are at this point in time. And I think that'll help us understand
67090	74730	where my mindset is when it comes to this talk. So, of course, I started with automate your
74730	80330	stocking, where I was digging into my Twitter research. This is bad. And we learned that
80330	86650	during the DC Sky Talks a couple of years ago. So, the following year, I did stalker in a haystack,
86650	92910	where we figure out how to not be stalked, or at least we can detect cyber stalking.
93610	100690	That was good. And now I've gotten recently into political sports betting, or, you know,
100690	107970	market speculation, which is part of the story. And profiling. I mean, profiling like social media
107970	114570	profiling on different accounts. And of course, I do have a degree in psychology. So, all this
114570	121010	wraps up into where we are at this point. So, these five pieces are what kind of has
122130	130120	gotten around this research here. So, about this talk. I'm gonna start with
130120	134760	the original goal and how I came up with what I was researching at that moment.
135460	143180	Why that failed and why we move on to what it is currently now as I'm presenting this.
143180	147020	And what I would like my current research to be in the future.
148720	154440	So, before we get into all of that, though, let's talk about caveats and disclaimers and warnings.
154560	163480	Oh, my. I'm bad at that. Anyway, first caveat or first disclaimer is for those who have followed
163480	168820	my work before, you know that I've said I anal or I anus, which is I am not a lawyer and I am
168820	173740	not a stalker. I'm gonna add something new to that, because I'm very new to the whole data
173740	181640	science thing. I've only recently gotten into using Pandas and Scikit-learn and NumPy and all
181640	190000	these different data analytics tools. So, I've added INADs. I am not a data scientist. I'm not
190120	195280	a lawyer, I'm not a stalker, and I am definitely not a data scientist, but I'll try my best.
196240	202180	So, this project is a work in progress, and I'm hoping that after I explain kind of what we're
202180	206840	doing here today, that maybe I can get some help from the community to further this project,
206840	212280	or at least now that I've defined the basics, I can continue and make this something very strong.
213660	220600	This is a neutral tool. I'm not building what I'm building today for stalking purposes,
220600	224460	you know, this is just a tool. You use it how you want, but of course,
224460	230140	as always, I'm not going to be held responsible for what you do with my research.
231740	236920	And I did not stand on the shoulders of giants for this research. What I mean by that is,
236920	240360	I know that there's a lot of research already in the space when it comes to
240360	247820	frequency analysis, word frequency analysis, you know, sentiment analysis, NLP,
247820	251840	not neuro-linguistic programming, but natural language processing.
252480	259440	I know that there's all that research out there, and I barely used any of it. So,
260140	263940	I would like to integrate that later, but I just want to let you know that for this
263940	271220	particular talk, that was really not used. So, it is definitely a work in progress.
272840	278860	All right. So, again, for those of you who know me, I'm not a sports guy. I've only recently
278860	283040	gotten into hockey, and I've always been a UFC fan, but other than that, I'm not a sports person.
283040	288560	So, I've never been into the sports book, but one thing I know about being in Vegas for my
288560	294220	entire life is that there's a sports book for every market. It doesn't matter what the market
294220	300180	or what the speculation is, there's a sports book for it or a betting market for it. And that's
300180	306340	where we start our story. So, if anybody's ever heard of predicted.org, it's a market for
306340	311040	political sports betting. So, you can bet on who you think the next president's going to be,
311040	317200	not just in America, but globally. Pick a country, pick a state, pick a, you know,
317200	323540	impeachment hearings. Who will run the White House? Who's going to be the VP pick? All of
323540	328520	these different markets are on predicted.org. And so, you can make a fair amount of money
328520	333600	if you know what you're doing, or you can just have a casual bet and just be right with a few
333600	339640	dollars and cents. And so, and specifically, we're going to be talking about President Donald
339640	347940	Trump's tweets as that is kind of a famous thing here and how it pertains to the political sports
347940	353760	betting market. Okay. And well, let's get into that now, because right now we're talking about
353760	359640	piecount.com, which is a tweet counter that's out there on the internet. And it keeps track
359640	364820	on a weekly basis of the president's tweets, as well as the VP, the White House, and the POTUS
364820	373040	accounts. And this is all geared towards the tweet market that predicted offers or used to offer,
373040	380580	and we'll get into that in a second. But we're talking about Twitter, you know, Twitter markets.
380580	387300	Again, this is on a weekly basis, the question or the market is, how many tweets will the president
387300	391980	tweet out for this particular week, starting from Wednesday of this week and going on to
392620	398500	Wednesday of the next week, it could be anywhere from, you know, 50, which is, which is highly
398500	403780	unlikely, all the way up to, you know, 300, 400, you know, and, and that's, that's what I was
403780	411540	analyzing beforehand. Now, I wanted to know if the profits could be based off of analytics. And,
411540	418280	you know, could the, could I anticipate what he would be tweeting based off of current events,
418280	425100	news, drama, anything that's out there in the global space. And the goal, the goal with this
425100	431380	original research was to get the president to pay for my energy bill. So if I made enough money
431380	436520	on this predicted.org market, could I pay the energy, my energy bill at the house
437120	441400	with, with Donald Trump's tweets, that's, that's where I was going with this whole thing.
443160	450340	So again, prior research is the frequency of the tweets, how often he would tweet in that week. And,
450340	461360	and I was capturing, as you'll see here, in the CSV file on the right side of the screen here,
461840	471200	well, we see 68 in the week of, of April of 2019. We see 137, 140, 141. So these are all
472360	478080	a rolling tally or running tally of, of his tweets. And of course, you graph it using,
478080	483880	using Jupyter, a Jupyter notebook, you know, you, you graph it and see the frequency here.
483880	490160	And then you ask yourself, you know, was this peak here due to impeachment drama? Was this due
490160	497040	to some sort of scandal or emergency in the Middle East? What caused these peaks of tweets?
497040	501440	Was it just somebody getting on the president's nerves? These were all the questions that I was
501440	507300	asking at the time, but there's a problem. Predicted.org actually stopped their, their
507300	515920	tweet markets. So while, while Predicted is still around, they no longer offer you the ability to,
515920	521600	to bet on, on, on tweets. And so all of this research has kind of just stopped, you know,
521600	525600	so they, they changed their offerings. They, they started to offer other markets that
526160	529720	just weren't working fast enough. It, you know, these other markets aren't
529720	534180	resolving on a weekly basis. And so my profits slowed to a halt.
535200	539720	And so because of that, this whole project that I was working on, as far as tweet frequency,
540700	545680	it, it kind of took a back, a back burner and I, I kind of forgot about it for a couple of months.
546440	554020	Um, but, um, it got resurrected. And so, uh, the project lives and, and I'll tell you why.
554280	563080	So I tweet on a regular basis and I tweet very vague, vague things. Um, and, and that's on purpose,
563080	568640	I would say. Um, it could be a Zen of the day quote. It could be, uh, something obscure or
568640	575900	just something that's on my mind, right? Because that's exactly what Twitter is used for. You,
575900	580960	and then you just let it out there. It's not always political. It's not always socially driven.
580960	587780	It's just a thought that's on your mind. Well, I had one of my followers, um, kind of get a little
587780	592960	bit offended or had a little bit of ruffled feathers here, um, with one of my tweets.
593320	601720	And so, uh, he kind of got mad at me and, you know, I wasn't being political, but the
601720	606360	vagueness of my tweet allowed, uh, this particular person to,
606360	614060	uh, project their political ideals on, on my tweet. So, um, I thought about that for a while
614060	618440	and I could have gotten mad and, and, and, you know, gotten back and forth with him,
618440	624000	um, but I didn't. And instead, like, I don't get mad, but I will turn you into research.
624220	630540	And, and that's what's bringing us here today. So again, the target projected their beliefs on me
630540	636220	and the meaning of my tweet, even though there was no correlation there, there was nothing to,
636220	644280	to draw conclusions from. Okay. And so blindsided by rage, meaning that I had no idea that I,
644280	649880	that this was going to be happening. Um, but I asked myself, could I have seen this coming?
650100	657400	Could I have predicted that maybe what I tweeted, um, would result in a certain outrage from not
657400	664580	just a certain demographic, but from maybe specific people. And this is still the work in
664580	672740	progress. So, um, who are we targeting, um, with, with what we're working with today? Well, as,
672740	677120	as stated in the last slide, we're working with political opponents or, uh, we're talking about
677120	681820	political opponents and not because we're trying to be political, but just because, um, this is a
681820	687240	use case, uh, for, for what we're doing here. This is a scenario. Um, corporate marks, we could
687240	692260	use what we're going to do today for, you know, spear phishing or, uh, maybe password lists or
692260	700440	some sort of targeted, um, execution of, of an attack. Um, cyber bullies, you know, there are
700440	706080	plenty out there and wouldn't you want a leg up if you knew the type of vocabulary that they were
706080	712380	using, uh, the, uh, way in which they tweet, the sentiment behind their tweets, and maybe you could,
712380	717800	uh, you know, work for a better outcome, uh, against your cyber bullies because yes, they are
717800	725160	definitely out there. Now, sadly, I have to address this. Um, this also has some stalker interests.
725160	730980	Um, but remember what I said before, and in my previous research, stalker interest, this is bad.
730980	737440	It can be used for stalking. I do not condone it. I, I think we've, we've crossed that already. Yes.
738060	744820	Okay. So what are we talking about? Well, everybody knows Twitter is a gold mine for OSINT and recon.
744820	751440	Okay. Um, and, and, and it's not a secret. It's, it's very well known and it's, it's that way by
751440	756980	design. Um, not everybody has a private account and, and it kind of ruins the fun if you are on
757080	764000	a private account. So, um, Twitter, we know as a publicly shared sentiment. If you're going to put
764000	769820	it on Twitter, you have to expect that, uh, it can be analyzed. It can be scraped. It could be mined.
769820	777900	It could be, um, used for research such as today's research. So what we're doing though is we're
777900	785500	making an at-a-glance picture of what this profile, a target profile, um, is about, what it's all about.
785500	792860	So, um, you know, the term at-a-glance, what I'm talking about here is instead of, instead of
792860	797640	having to scroll through an entire timeline to understand what this particular profile is all
797640	803820	about, what if we could just take a look at a dashboard or, or just one screen and understand
803820	809600	the sentiment of that, uh, Twitter profile? That's, that's something that, uh, I think would be very
809600	816860	valuable. Why? Why are we doing this? Well, um, because I can, because we can. We're hackers, uh,
816860	821020	and, and this is what we do. Um, we look at something, we look at a problem, we look at
821020	826040	maybe a scenario that has recently happened to us, and we say, okay, I know how to fix that, or
826040	831840	I know what I can do about that. So yes, because I can, because we can. Remember, we're hackers.
833500	838280	All right, and we also want to do this because, um, this gives us an insight into tendency of
838280	845340	the profile. Okay. Uh, again, and the goal is here to, to find maybe quicker red flags, maybe catch
845340	853120	that outrage, um, before, uh, it bubbles up and, and, and blindsides us. Okay. And of course, uh, as with
853120	857340	anything, we could use this for later weaponization. Uh, we could turn this into something that could
857340	863300	be an offensive tool if needed, of course. Uh, we're talking about password lists, uh, further social
863300	869400	profiling, uh, maybe psychological profiling, uh, and troll bots, you know, um, maybe they could
869400	874920	have a conversation with themselves. Now, how are we going to do this? Well, we're going to look at a
874920	882440	couple of things. Uh, we're going to track a small sample size of the, uh, recent tweets in the
882440	887680	timeline and an ongoing sample, of course, as we add that programmatically. And we're going to be
887680	895600	doing our analysis on, uh, word frequency, word choice, uh, retweet frequency, um, and hashtag usage.
895600	901220	That's actually, uh, of course, as we know, it's pretty important. Okay. All right. So let's take a
901220	906420	look at the code. But before we do a couple of notes here, um, and you can play the game at home
906420	911100	actually. So now by the time that you're watching this, um, this should be available and public for
911100	915220	you and your consumption. So you can follow along with what I'm doing here, uh, in the next few
915220	922460	slides. So, uh, I'll go ahead and give you a minute to, to take that, uh, link down. Um, and we'll go
922460	927920	from there. Actually, there's a couple more, uh, points here. So, uh, it's just basically my GitHub
927920	934620	handle and, uh, you know, the, the name of the project, Twitter word frequency. Um, now we scraped, I
934620	941240	scraped, um, this, uh, information with Python and it's all Python. All of this is Python. Um, but I
941240	948700	scraped with, uh, an actual Python file. Um, and then I use a Jupyter notebook for more, um, analysis.
948700	953800	And there's a reason for that as well. And we'll see that in the next couple of slides. Uh, in the
953800	959320	background here, you'll see your code snippet of the actual script, uh, which pulls the timeline.
959320	964320	Um, now there's a couple of things here. It either, um, checks to see if the timeline has already been
964320	969400	saved on your machine. And if it hasn't been saved on your machine, it then goes out, uh, using the
969400	976440	Twitter API, um, to grab, uh, the latest, uh, timeline, uh, of your subject or of your target. Okay.
976560	981600	Now in the foreground here, you're going to be seeing, um, uh, a snippet from the Jupyter notebook.
981720	987660	And I used the Jupyter notebook because, um, maybe I wanted to run a little snippet of the code
987660	993560	without running the entire script. So a lot of the code is going to be the same. Um, but with, uh,
993560	997400	with Jupyter notebook, you know, and I'm sure everybody who's in data science already remember
997400	1003380	I'm not, um, you, you already understand that you can take these pieces of, uh, or snippets of code
1003380	1009200	and just run those individually and change things as new data becomes available to you.
1010020	1015100	All right. So let's talk now about the analysis. Okay. So this first analysis is actually,
1015100	1022680	um, myself. This is my own, um, analysis on, on my handle at Chenbox. Um, so on the left side,
1022680	1027260	uh, you'll see a couple of the words here that I've used on a regular basis, of course,
1027260	1033940	with, with the most current recent events, you see mask is used there six times, uh, Twitter.
1033940	1039960	Okay. Cause I've, I've had opinions about Twitter on Twitter. Okay. So that's, that's there. Um,
1039960	1045340	now you'll notice that these red blotched out, uh, pieces are actually, um, these are Twitter
1045340	1050660	accounts. Okay. Um, and, and how often I've referenced these Twitter accounts. Uh, so this
1050660	1056540	can be a way that we can, again, we're talking about at a glance, um, we can see, um, who we're
1056540	1063540	looking at, um, who, uh, we're associating with, uh, quite quickly and right, right up front.
1063540	1070460	Okay. Um, on the, in the middle, on the, on the top side, you'll see, um, I have a red dot right
1070460	1077740	next to RT, RT, medium course retweet. And you'll see that my retweet frequency is about 54 times
1077740	1082760	in a 200 tweets sample. That's what that means. Remember this is out of, out of 200 tweets, um,
1082760	1089160	200, uh, tweets, uh, 54 of those were retweets of other people. And of course, uh, all these
1089160	1093980	Twitter accounts, um, that are blotched out. Um, I think it would be safe to assume that if I'm
1093980	1099180	not mentioning somebody, um, I'm definitely retweeting them. Um, at the bottom, you'll see,
1099180	1105020	uh, 2638, which is the word count associated with the last 200 tweets sample of my own personal
1105020	1112740	account. Uh, I'm using mine as a base, as a baseline. And on the right side, um, you'll see,
1112740	1118720	uh, hashtags and of course it's frequency. And at the bottom is the, um, association with the
1118720	1124960	histograms, right? So it's a histogram, uh, associated with, uh, with the hashtags, uh,
1124960	1133060	there at the top. And there's, there's my handle. So you know that this particular, uh, slide was
1133060	1140320	for, for my, uh, own profile analysis. Uh, okay. So, um, remember that person that I said got kind
1140320	1146540	of angry with my vague tweet? Well, this is, uh, this is the analysis slide of, of his account.
1146540	1150520	So, um, you'll see that there's a couple of differences obviously, um, because we're not
1150520	1156540	exactly the same. Um, on the left side, we'll see, uh, a couple, he's, he's mentioning a couple of
1156540	1162580	people who, um, are blotched out, um, not as frequently as maybe I do, but you know, these,
1162580	1170080	these profiles are in, in the scan that I've seen. Uh, you'll see his retweet frequency is 178.
1170080	1178160	So, so out of 200, out of a 200 tweet sample, we're talking about 178 of those, uh, being retweets,
1178160	1186420	retweets. Um, and, um, I also hit the, uh, a red dot on the word I, because I was just kind of curious
1186420	1193740	as to see, um, how often somebody is, um, is using I, uh, in a sentence. I'm sure that there's a
1193740	1200380	psychological profile, uh, based off of, um, the use of that word, right? And at the bottom, we see,
1200380	1206520	um, the word frequency, 37, uh, 88. I know that there's math that we can do on, uh, this word pro,
1206520	1213300	or on this, um, on this word count, uh, as far as, uh, calculating, you know, the types of words that
1213300	1218300	we're using, um, how big they are, how small they are, et cetera. And, and another interesting thing
1218300	1224740	about, uh, this particular analysis is, uh, the histogram on the right side, um, indicates that, um,
1224740	1231840	this particular individual only, uh, tweet, or only uses a hashtag, uh, once, like, uh, one and done.
1231840	1239140	So, um, I found that to be interesting. Uh, again, uh, this is for the, uh, at my target. Um, this is not
1239700	1245640	the actual Twitter account, obviously, um, but I, I thought I had to identify it somehow.
1246240	1251860	Now, lastly, I'd like to, uh, you know, I used myself as a baseline. Uh, this is the target's,
1251860	1260200	um, dashboard analysis. And, uh, this next one is, uh, actually, uh, President Donald Trump. And we'll
1260200	1266980	see, of course, a couple of, uh, of, of key features here. Uh, on the left, we don't see
1266980	1273240	the word I, uh, actually we do, but it's no more than 20 times. Now, uh, for those of us who, who
1273240	1278260	might think that, uh, President Trump is a little bit of a narcissist, uh, we might be kind of
1278260	1285800	surprised, uh, to see that he's only tweeted the word I about 20 times, uh, in a, in a, uh, 200
1285800	1290460	tweet sample. At the bottom there, we see 3,400 words. So these, these are all, you know, kind of
1290460	1296600	interesting, the same stats. Um, he's retweeted only 96 times, um, in, in the past 200 tweet
1296600	1302620	sample. Of course, uh, this piece is not so, um, surprising because we know that he uses a lot of
1302620	1310620	his own, um, words, uh, in, in his tweets. Um, but, uh, back to the left side really quick. Um, the
1310620	1315580	only blotched out account there, um, I don't know if anybody wants to take a quick guess at what that
1315580	1321740	is. I know I can't, uh, wait necessarily for your answer as, as, uh, this is the closest that we can
1321740	1329780	get to, um, audience participation at the moment. Um, but, uh, yes, if, uh, if you guessed that that
1329780	1334880	was his own account, you are correct. So he's, what I'm saying is he's mentioned himself in his
1334880	1341560	own tweets about 46 times. Uh, so that might be a, uh, an indicator of narcissism, maybe tweeting in
1341560	1346840	the third person. We'll have to analyze further for that. Uh, and on the right side, we'll see,
1346840	1353460	um, again, our histogram and, and hashtag matching. So, um, uh, President Trump has maybe tweeted a
1353460	1358620	couple of things with hashtags, but of course the recurring one is MAGA. Is anybody surprised?
1358620	1363380	I don't think anybody is. I'm not. Uh, and actually that would have been three if he didn't, uh,
1363380	1370640	add emojis to that, uh, third MAGA there at the, at the bottom. Uh, so again, my point is, um, all
1370640	1375880	of this was programmatically, programmatically, excuse me, um, all this was programmatically,
1375880	1383020	um, scraped and, and put together so that we can at a glance, take a look at what, um, a target is
1383020	1387260	talking about. Uh, and, and this is done, I understand that, you know, this could be easily
1387260	1394840	done through APIs and, uh, other research. Uh, this is my own, um, basic whiteboard. This is my
1394840	1400940	own starting from scratch, um, analysis of these targets. Myself, uh, President Trump because of
1400940	1407720	the sports, uh, betting. And of course, uh, somebody who got really mad at me for, um,
1407720	1414380	something that was really vague. Uh, okay. So now that that's, uh, an overview of the data
1414380	1420500	really quickly, let's take a look at the insights here. Uh, so some of the insights, um, it seems
1420500	1426060	like, of course, um, retweets indicate shared sentiment. Well, duh, but the question is, uh,
1426060	1431620	from who, who are we sharing a sentiment with? Uh, this might give us insights, of course, into,
1431620	1438420	um, who a person is associating with or how frequently or, um, how, how closely related
1438420	1444240	these ideals may be that they share, depending on the retweet, of course. Um, and of course,
1444240	1452920	we see narcissism in hashtags. So, uh, you may see, uh, a lot of, um, a lot of your, your own,
1452920	1459020	actually, okay, so there's a couple of things here. Um, I did forget to, uh, include, um,
1459020	1465840	uh, a screenshot that I have of my target, uh, tweeting himself, like, his own handle, um,
1465840	1472580	in, uh, in the hashtags. So, it's hashtag his own handle, um, and I, I found that to be very
1472940	1477500	narcissistic, um, kind of interesting, um, but that's something that I wanted to, uh, piece in
1477500	1481220	there. So, I do apologize for not having that screen cap in there, uh, but I found that to be
1481220	1487540	interesting, and that's why I put that in, in, in here in the insights. Um, and, uh, repeating
1487540	1491640	hashtags to me, of course, again, this is, this is kind of obvious when it comes to somebody who's
1491760	1497580	a regular Twitter user. Um, it depicts brand or focus, you know, and of course, we see that with,
1497580	1504640	with, with repeating hashtags. Um, now, I've noticed that word frequency, um, the word
1504640	1510760	frequency count significantly drops off after stop words. Um, so, you know, I have a rudimentary,
1510760	1515140	uh, introduction to, uh, natural language processing, so I understand a little bit about
1515140	1522500	tokenization and, um, what stop words are. Stop words being, like, uh, in the very small, um, words
1522500	1528080	that kind of glue the English letter, uh, language together, right? Um, and so, again, after, after
1528080	1533640	those words are scraped off the top, the, the frequency of those words, of other words, really
1533640	1541280	drop. And, of course, uh, for those who are linguists, may already know that. Um, now, um,
1541280	1545240	obviously, with what we've, uh, with what I've presented today, we know that descriptive
1545240	1552160	statistics are as far as we can get, um, without any sort of, uh, further, um, analysis with better
1552160	1558260	tools. Better tools being, of course, uh, neurolinguistic, uh, natural language processing,
1558260	1564540	maybe some other, uh, types of, uh, data science tools and, and, uh, techniques. And so, that is
1564540	1571500	where this work in progress is going to be heading next. Okay, and so, speaking of which, uh, where
1571500	1576420	do we go from here? Okay, well, uh, of course, I want to clean up the data collection, data
1576420	1580560	collection methods right now. It is pretty rough, and I'd like to turn that into something that is,
1580560	1587760	uh, uh, easier to read and, and followed very, uh, very easily. Now, better data visualization.
1587800	1592720	Um, I quickly grabbed screenshots together, um, but, of course, I'd like to incorporate word
1592720	1598980	clouds and other charting, maybe better charting, right? Um, dashboard of data. I'd like to clean
1598980	1607000	that up so that, again, the, the whole goal is an at-a-glance, uh, for, um, for a Twitter analysis.
1607180	1612080	Okay, uh, now, what can we do with this? Well, we could talk about, uh, profile scoring. So, for
1612080	1618240	instance, um, leanings, whether it's a sentimental leaning, a, uh, political leaning, a corporate
1618240	1625180	leaning, uh, I'd like to see if we can, uh, we can understand that, um, at, at a quick look. Um,
1625180	1631740	the potential for extremism, um, if, if somebody is getting mad at you for, uh, a tweet that was
1631740	1639780	vague enough for them to project, um, meaning onto it, um, then, then what is the potential for
1639780	1643320	somebody to go kind of a little bit more off the rails? That's something else that I'd like to look
1643320	1649760	at. Um, and, of course, um, ongoing, uh, me being a psychology buff, um, you know, ongoing psychological
1649760	1654640	profiles and red flag notifications. If there's a red flag with anything that we see here in, in
1654640	1660200	their timeline, um, can we detect that? And, of course, um, I am, I'm almost certain that there's
1660200	1665360	already research out there. I'd like to continue, uh, looking at that myself and, and, and building
1665360	1672240	from there. So, features to incorporate. I've said it a lot already. Um, natural language processing.
1672240	1677580	I think that'll be a very, uh, a very good thing to add in here. Uh, sentiment analysis, also part of,
1677580	1684820	part of that space. Uh, a mirror bot. Uh, real quickly, what I want to talk about there is, is, um,
1684820	1692300	if we know how our target speaks, can we make a bot that kind of talks to themselves? I think that
1692300	1697420	would be kind of funny to watch. Um, hopefully it doesn't get anybody banned, but I think it'll be,
1697420	1702540	again, it'll be funny to see, just as a funny project. This is all just, it's just jokes, folks.
1702540	1710320	It's just jokes. Um, now, uh, I want to build off of the prior research that, uh, I, myself and, and
1710320	1715320	the community has done, uh, including my, my stalker research and, and anti-stalking research
1715320	1719620	and anything else that the community has done, I'd like to incorporate in what I'm doing now.
1720020	1725440	Um, and even though we've, even though today I've only shown, uh, three profiles, I'd like to do a
1725440	1731840	quick, uh, mass script, a mass scripted analysis of maybe everybody that is following me or
1731840	1738300	everybody that I'm following, uh, and just see, um, what kind of results, um, are in their timeline.
1738300	1743440	Remember, these are public timelines. And of course, um, I don't want to hit any rate limits,
1743440	1747980	so we'll, we'll do it slowly. Uh, some, some future questions that are on my mind when it
1747980	1754780	comes to, uh, this is, uh, can I get a target to talk to themselves, uh, and agree or disagree?
1754780	1760540	I think that'd be funny. That's, that's what I was mentioning with the, the mirror bot. Um, can a
1760540	1765420	person have a conversation with themselves and disagree on their own maybe extremist points or
1765420	1771160	non-extremist points, you know? Uh, next question is, uh, you know, can outrage be predicted? You
1771160	1776020	know, that's something that I, that's something that I've brought up, um, uh, prior or earlier in,
1776020	1780640	in this talk as well. Um, could that be predicted based off of what is in the current events in the
1780640	1786600	news, uh, and what's going on globally, uh, at the moment? Uh, next one. Of course, uh, any
1786600	1790580	questions that, uh, the audience may have for me, I would definitely want to put that into this
1790580	1799180	project. So, uh, to summarize, who said nothing came, uh, nothing good came out of sports betting?
1799420	1804360	I think, uh, I think there's, uh, some benefit here. I'm glad that I went down this rabbit hole.
1805360	1812300	Uh, now sharing vague sentiments can, can still make frenemies. Uh, you know, apparently I've,
1812300	1818520	I was not expecting that even with, uh, today's climate, but hey, it is what it is. Um, now
1818520	1823640	remember, don't get mad. Uh, you know, don't get mad. Don't feed the outrage. Don't get,
1823640	1828800	don't, you know, don't send the rage back. Just get productive. Take what you have and
1828800	1833200	turn it into research. That's been, that's been my guiding light for the past five years,
1833200	1838760	maybe longer. Um, start somewhere. Like I said, this project is definitely a work in progress,
1838760	1843960	um, but I'm going to start somewhere. And of course we built, uh, okay. So fork me,
1845200	1849760	there's the link again, the GitHub link. If you didn't get it before, um, Twitter word frequency
1850280	1858740	and, Oh, sorry. I'll give you a couple of seconds on that one. A couple more and resources. So the
1858740	1866040	top link is actually, uh, another, uh, uh, it's college speech on, um, natural language processing
1866040	1872700	and the, and how that goes. Um, and then of course, um, Oh, these next two, uh, these next
1872700	1876740	two weeks are very good. I'll let you check that one out. Uh, because, uh, those are actually,
1876740	1881980	uh, kind of in line with what I'm trying to do, but on a massive scale for myself,
1881980	1886540	like I'm trying to do this on a massive scale. Uh, what these last two weeks are doing specifically
1886540	1897480	for Donald Trump's, uh, tweet account. Uh, thank you very much. And that concludes my talk.