SlideShare a Scribd company logo
1 of 58
Download to read offline
Techniques	for	Automating	Quality	
Assessment	of	Context-specific	Content	
on	Social	Media	Services
Prateek Dewan
PhD	Thesis	Defense
November	14,	2017
prateekd@iiitd.ac.in
Committee	members
Dr.	Alessandra	Sala
Dr.	Sanasam Ranbir Singh
Dr.	Aditya	Telang
Dr.	Ponnurangam Kumaraguru (Advisor)
Who	am	I?
• Data	Scientist	at	Apple
• PhD	student	since	February,	2012	– IIIT-Delhi
• Masters	(2010	– 2012),	IIIT-Delhi
• Collaborations
• IBM	IRL	(Delhi	and	Bengaluru),	 Symantec	Research	Labs	(Pune),	 Dublin	City	
University	(Ireland),	UFMG	(Brazil)
• Worked	in	Privacy	and	Security	on	Online	Social	Media
• Research	interests
• Applied	Machine	Learning
• Natural	Language	Processing
• Web	Security
2
Online	Social	Media:	The	Big	Picture
3
“With	great	power	comes	great	responsibility”
4
Thesis	statement
• To	design	and	evaluate	automated	techniques	for	quality	
assessment	of	context-specific	content	on	social	media	
services	in	real	time
• Focus:	Facebook
• Biggest	Online	Social	Media	service
• 2.01	billion	monthly	active	users
• Every	2	out	of	7	human	beings	on	the	planet	uses	Facebook
• Most	sought-after	OSN	for	news
5
Proposed	Solution
6
Identify Characterize Model
PrototypeDeployEvaluate
Facebook	Inspector:	Demo
7
Scope
• Establishing	the	definition	of	poor	quality	content
• What	all	content	is	poor	in	quality?
• Untrustworthy
• Child	unsafe
• Misleading	information
• Hoaxes,	scams,	clickbait
• Violence,	hate	speech
• Definition	conforming	to
• Facebook’s	community	standards	1
• Definitions	of	page	spam
8
1	
https://www.facebook.com/communitystandards
Approach
•Poor	quality	posts published	 on	Facebook
•Facebook pages publishing	 poor	quality	content
•Misinformation	spread	on	Facebook	through	images
Characterize
•Ground	truth	extraction	using	URL	blacklists,	and	human	annotation
•Experiments	with	multiple	supervised	learning	techniques
•Two-fold	model	to	identify	malicious	content	in	real	time
Model
•Facebook	Inspector (FbI)	Architecture
•Live	deployment	via	REST	API	and	browser	plug-ins	for	Chrome	and	
Firefox
•3,000+	downloads,	 180+	daily	active	users,	1 million+	posts	analyzed
•Evaluation in	terms	of	response	time,	performance,	and	usability
Implement
9
Approach
• Poor	quality	posts published	on	Facebook
•Facebook pages publishing	 poor	quality	content
•Misinformation	spread	on	Facebook	through	images
Characterize
•Ground	truth	extraction	using	URL	blacklists,	and	human	annotation
•Experiments	with	multiple	supervised	learning	techniques
•Two-fold	model	to	identify	malicious	content	in	real	time
Model
•Facebook	Inspector (FbI)	Architecture
•Live	deployment	via	REST	API	and	browser	plug-ins	for	Chrome	and	
Firefox
•3,000+	downloads,	 180+	daily	active	users,	1 million+	posts	analyzed
•Evaluation in	terms	of	response	time,	performance,	and	usability
Implement
10
Dataset
Data	Type Quantity
Unique	posts 4,465,371
Unique	entities 3,373,953
Unique	users 2,983,707
Unique	pages 390,246
Unique	URLs 480,407
Unique	posts	with	one	or	more URLs 1,222,137
Unique	entities	posting	URLs 856,758
Unique	posts	with	one	or	more	malicious	URLs 11,217
Unique	entities	posting	one or	more	malicious	URLs 7,962
Unique malicious	URLs 4,622
11
Establishing	Ground	Truth
• Extracted	posts	containing	one	or	more	URLs
• 1.2	million	out	of	4.4	million	posts	in	total
• 480k	unique	URLs
• Used	six	URL	blacklists	
• Google	Safebrowsing(malware	/	phishing)
• VirusTotal (spam	/	malware	/	phishing)
• Surbl (spam)
• Web	of	Trust	(trust	score)*
• SpamHaus (spam)
• Phishtank(phishing)
• Post	containing	one	or	more	blacklisted	URL	marked	as	poor	
quality	posts (11,217	in	all)
12
Web	of	Trust
13
Reputation:	Unsatisfactory	/	Poor	/	Very	poor	(less	than	60)
Confidence:	High	(greater	than	10)	
OR
Category:	Negative
Malicious
http://www.domain.com
Findings
• Facebook’s	current	techniques	do	not	suffice
• 65%	of	all	poor	quality	posts	existed	on	Facebook	after	4	(or	more)	
months
• Gathered	likes from	52,169	unique	users;	comments from	8,784	unique	users
• Facebook’s	partnership	with	Web	of	Trust?
• 88%	of	all	malicious	URLs	had	poor	reputation	on	WOT
• No	warning	pages
14
Platforms	used	to	post
15
Distribution	of	poor	quality	posts
16
Pages Users
Entities Posts
Approach
•Poor	quality	posts published	 on	Facebook
• Facebook pages publishing	poor	quality	content
•Misinformation	spread	on	Facebook	through	images
Characterize
•Ground	truth	extraction	using	URL	blacklists,	and	human	annotation
•Experiments	with	multiple	supervised	learning	techniques
•Two-fold	model	to	identify	malicious	content	in	real	time
Model
•Facebook	Inspector (FbI)	Architecture
•Live	deployment	via	REST	API	and	browser	plug-ins	for	Chrome	and	
Firefox
•3,000+	downloads,	 180+	daily	active	users,	1 million+	posts	analyzed
•Evaluation in	terms	of	response	time,	performance,	and	usability
Implement
17
Facebook	Pages	posting	poor	quality	content
18
Hiding	in	Plain	Sight:	Characterizing	and	Detecting	Malicious	Facebook	Pages. Prateek Dewan,	Shrey Bagroy,	and	Ponnurangam
Kumaraguru (Short	paper).	Published	at	IEEE/ACM	Conference	on	Advances	in	Social	Networks	Analysis	and	Mining	(ASONAM),	San	
Francisco,	USA.	2016.
Ground	Truth	extraction:	Facebook	pages
4.4	million	posts
10,341	malicious	posts
(1,557	pages;	5,868	users)
627	malicious
pages
19
1	or	more	malicious	URLs	in	
the	most	recent	100	posts
Dataset	of	pages posting	poor	quality	content
WOT	response No.	of	pages No. of	posts
Child	unsafe 387 10,891
Untrustworthy 317 8,057
Questionable 312 8,859
Negative 266 5,863
Adult content 162 3,290
Spam 124 4,985
Phishing 39 495
Total 627	(31) 20,999
20
• Numbers	in	brackets	are	Verified	pages
Content	analysis	(page	names)
21
• Sentence	Tokenization	à Word	Tokenization	à Case	normalization	à
Stemming	à Stopword removal
• N-gram	analysis	(n	=	1,	2,	3)
• Politically	polarized	entities	amongst	poor	quality	pages
• British	National	Party	(BNP),	The	Tea	Party,	English	Defense	League,	
American	Defense	League,	American	Conservatives,	Geert	Wilders	
supporters…
Network	analysis
22
• Collusive	behavior	within	pages posting	poor	quality	content
Shares LikesComments
Temporal	activity
• Activity	ratio:		
"#.#%	&'()	*"'&+	,-&'.)
&#&,/	"#.#%	&'()	*"'&+
during	complete	observation	period
• Malicious	pages	are	more	active	than	benign	pages
23
Approach
•Poor	quality	posts published	 on	Facebook
•Facebook pages publishing	 poor	quality	content
• Misinformation	spread	on	Facebook	through	images
Characterize
•Ground	truth	extraction	using	URL	blacklists,	and	human	annotation
•Experiments	with	multiple	supervised	learning	techniques
•Two-fold	model	to	identify	malicious	content	in	real	time
Model
•Facebook	Inspector (FbI)	Architecture
•Live	deployment	via	REST	API	and	browser	plug-ins	for	Chrome	and	
Firefox
•3,000+	downloads,	 180+	daily	active	users,	1 million+	posts	analyzed
•Evaluation in	terms	of	response	time,	performance,	and	usability
Implement
24
Why?:	The	Human	Brain	- Images	versus	text
• Human	brain	processes	images	60,000	times	faster	than	
text
25
Are	we	doing	enough	to	"understand" images?
• Most	research	to	analyze	social	media	content	focuses	on	text
• Topic	modelling
• Sentiment	analysis
• Does	it	capture	everything?
• Studies	related	to	images	are	limited	to	small	scale
• Few	hundred	 images	manually	annotated	and	analyzed
• What	can	be	done?
• Automated	techniques	for	image	summarization;	Deep	Learning	and	
Convolutional	Neural	Networks	(CNNs)	to	scale	across	large	no.	of	images
• Domain	transfer	learning
• Optical	Character	Recognition
26
Methodology
• Images	posted	on	Facebook	during	the	Paris	Attacks,	
November	2015
• 3-tier	pipeline	for	extracting	high	level	image	descriptors	
from	images
27
Uniqueposts 131,548
Unique users 106,275
Posts	with	images 75,277
Totalimages	extracted 57,748
Total	unique	images 15,123
Images
Themes
(Inception	v3)
Image	Sentiment
(DeCAF trained	on	
SentiBank)
Optical	
Character	
Recognition
Human	
understandable	
descriptors
Text	Sentiment	
(LIWC) +
Topics	(TF)
Manual	
calibration
Tier	1:	Visual	Themes
Tier	2:	Image	Sentiment
Tier	3:	Text	embedded	in	images
Tier	I:	Visual	Themes
• ImageNet	Large	Scale	Visual	Recognition	Challenge	
(ILSVRC),	2012
• 1.2	million	images,	1,000	categories
• Winner:	Google’s	Inception-v3	(top-1	error:	17.2%)
• 48-layer	Deep	Convolutional	Neural	Network
28
Tier	I:	Visual	Themes	contd.
• All	images	labeled	using	Inception-v3
• Validation:	
• Random	sample	of	2,545	images	annotated	by	3	human	annotators
• 38.87%	accuracy	(majority	voting)
• Manual	calibration
• Renamed	7	out	of	the	top	30	(most	frequently	occurring)	labels	
• New	accuracy:	51.3%
• Why	rename?								à
29
Bolo	Tie
(Inception-v3)
PeaceForParis
(Our	dataset)
Tier	II:	Image	Sentiment
• Domain	Transfer	Learning
• Inception-v3’s	last	layer	retrained	using	SentiBank
• SentiBank
• Images	collected	from	Flickr	using	Adjective	Noun	Pairs	(ANPs)	as	search	
query
• ANPs:	happy	dog,	adorable	baby,	abandoned	house
• Weakly	labeled	dataset	of	images	carrying	emotion
• Final	training	set	– 133,108	negative	+	305,100	positive	sentiment	images
• 10-fold	random	subsampling
• 69.8% accuracy
30
Tier	III:	Text	embedded	in	images
• Optical	Character	
Recognition	(OCR)
• Tesseract	OCR	(Python)
• 31,689	images	had	text
• Manually	extracted	text	
from	a	random	sample	of	
1,000	images
• Compared	with	OCR	
output	using	string	
similarity	metrics
• ~62%	accuracy
31
Tesseract	output:
No-one	thinks	that	
these	people	are		
representative	of	
Christians.					So	why	
do	so	many	think	
that	these	people	
are	representative	
of	Muslims?
Image	and	post	text	had	different	topics
• Text	embedded	in	images	depicted	more	negative	
sentiment	than	user	generated	textual	content
32
Text	embedded	in	images User	generated	text
Sentiment:	Images	versus	text
• Image	sentiment	was	more	positive	than	text	sentiment
33
0
0.1
0.2
0.3
0.4
0.5
0.6
8 24 40 56 72 88 104 120 136 152 168 184 200 216 232 248 264 280
Sentiment	Value	/	Volume	Fraction
No.	of	hours	after	the	attacks
Post	Text Image	Text
Image Volume	Fraction
Poor	quality	image	content popular	on	Facebook
34
Approach
•Poor	quality	posts published	 on	Facebook
•Facebook pages publishing	 poor	quality	content
•Misinformation	spread	on	Facebook	through	images
Characterize
•Ground	truth	extraction	using	URL	blacklists,	and	human	annotation
•Experiments	with	multiple	supervised	learning	techniques
•Two-fold	model	to	identify	malicious	content	in	real	time
Model
•Facebook	Inspector (FbI)	Architecture
•Live	deployment	via	REST	API	and	browser	plug-ins	for	Chrome	and	
Firefox
•3,000+	downloads,	 180+	daily	active	users,	1 million+	posts	analyzed
•Evaluation in	terms	of	response	time,	performance,	and	usability
Implement
35
Revisiting	-- Establishing	Ground	Truth
• Extracted	posts	containing	one	or	more	URLs
• 1.2	million	out	of	4.4	million	posts	in	total
• 480k	unique	URLs
• Used	six	URL	blacklists	
• Google	Safebrowsing(malware	/	phishing)
• VirusTotal (spam	/	malware	/	phishing)
• Surbl (spam)
• Web	of	Trust	(trust	score)*
• SpamHaus (spam)
• Phishtank(phishing)
• Post	containing	one	or	more	blacklisted	URL	marked	as	poor	
quality	posts (11,217	in	all)
36
Ground	Truth	extraction	– Dataset	II
•What	if	a	post	does	not	have	a	URL?
• 500	random	Facebook	posts	x	17	events	x	3	annotators
• Definition	of	malicious	post
• “Any	irrelevant	or	unsolicited	messages	sent	over	the	Internet,	typically	to	large	
numbers	of	users,	for	the	purposes	of	advertising,	phishing,	spreading	malware,	etc.	
are	categorized	as	spam.	In	terms	of	online	social	media,	social	spam	is	any	content	
which	is	irrelevant	/	unrelated	to	the	event	under	consideration,	and	/	or	aimed	at	
spreading	phishing,	malware,	advertisements,	self	promotion	etc.,	including	bulk	
messages,	profanity,	insults,	hate	speech,	malicious	links,	fraudulent	reviews,	scams,	
fake	information	etc.”
• Final	dataset	(all	3	annotators	agreed	on	the	same	label)
• 571	malicious	posts
• 3,841	benign	posts
37
Feature	set:	Facebook	Posts
Source Features
Entity (9) isPage, gender,	pageCategory,	hasUsername,	usernameLength,	
nameLength,	numWordsInName,	locale,	pageLikes
Textual	content	
(18)
Presence	of	!,?,!!,??, emoticons	(smile,	frown),	numWords,	
avgWordLength,	numSentences,	avgSentenceLength,	
numDictionaryWords,	numHashtags,	hashtagsPerWord,	numCharacters,	
numURLs,	URLsPerWord,	numUppercaseCharacters,	numWords /	
numUniqueWords
Metadata	(10) Application,	Presence of	facebook.com URL,	Presence	of	
apps.facebook.com URL,	Presence	of	Facebook	event	URL,	hasMessage,	
hasStory,	hasPicture,	hasLink,	type,	linkLength
Link	(7) http	/	https,	numHyphens,	numParameters,	avgParameterLength,
numSubdomains,	pathLength
38
Supervised	learning:	Dataset	I
Classifier	/	
Features
Entity Text Metadata Link All Top 7
Naïve	Bayes 54.79 52.41 71.60 69.25 56.15 74.72
Decision	Tree 63.02 64.78 80.56 82.34 84.67 86.17
Random	Forest 63.47 66.25 80.67 82.56 85.05 86.62
SVMrbf 61.77 64.89 78.75 81.45 75.89 83.66
39
Supervised	learning:	Dataset	II
Classifier	/	
Features
Entity Text Metadata Link All
Naïve	Bayes 51.67 51.60 72.45 77.58 67.63
Decision	Tree 51.66 73.16 79.01 81.04 76.17
Random	Forest 52.86 76.56 79.87 81.49 80.56
SVMrbf 53.16 76.52 78.18 80.37 73.79
40
Feature	set:	Facebook	Pages
Page	features Likes,	talking about,	description	length,	bio,	category,	name,	location,	check-ins,	…
Posting
behavior
Daily	activity	ratio,	post	types,	post	likes,	post	comments,	post	shares,	post	engagement
ratio,	post	language,	average	post	length,	no.	of	unique	URLs	in	posts,	no.	of	unique	
domains	in	posts,	etc.
41
• Supervised	learning
• Page	+	post	features
• 55	features	from	page	information
• 41	features	from	posting	behavior
• Bag	of	words
• Content	generated	by	pages
Supervised	learning:	Page	+	post	features
Classifier Feature	set Accuracy	(%) ROC	AUC
Naïve	Bayesian
Page 63.95 0.685	
Post 69.61 0.753	
Page	+	Post 70.81 0.776	
Logistic	Regression
Page 67.38 0.745	
Post 76.55 0.825	
Page	+	Post 76.71 0.846	
Decision	Trees
Page 65.55 0.668	
Post 71.37 0.720	
Page	+	Post 70.81 0.758	
Random Forest
Page 67.86 0.750	
Post 74.95 0.829	
Page	+	Post 75.27 0.837	
42
Supervised	learning:	Bag	of	words
Classifier Feature	set Accuracy (%) ROC	AUC
Naïve	Bayesian
Unigrams 68.27 0.682
Bigrams 69.06 0.690	
Trigrams 69.77 0.697	
Logistic	Regression
Unigrams 74.18 0.795	
Bigrams 74.34 0.791	
Trigrams 73.93 0.789
Decision Trees
Unigrams 68.12 0.678	
Bigrams 67.05 0.678	
Trigrams 66.63 0.672	
Random	Forest
Unigrams 72.26 0.794	
Bigrams 71.80 0.802	
Trigrams 72.18 0.794	
Sparse NN
Unigrams 81.74 0.862	
Bigrams 84.12 0.872	
Trigrams 84.13 0.900	
43
Model	for	real	time	detection
• Model	for	pages	depends	on	posts	published	by	pages
• Can’t	be	used	for	detection	in	real	time
• Two	fold	supervised	learning	based	model	using	post	
features
• Utilizing	class	probabilities	for	decision	making
44
Decision	boundary
45
Classifier	1
Classifier	2
1
10
High
High
Low
Malicious
Benign
Approach
•Poor quality	posts published	 on	Facebook
•Facebook pages publishing	 poor	quality	content
•Misinformation	spread	on	Facebook	through	images
Characterize
•Ground	truth	extraction	using	URL	blacklists,	and	human	annotation
•Experiments	with	multiple	supervised	learning	techniques
•Two-fold	model	to	identify	malicious	content	in	real	time
Model
•Facebook	Inspector (FbI)	Architecture
•Live	deployment	via	REST	API	and	browser	plug-ins	for	Chrome	and	
Firefox
•3,000+	downloads,	 180+	daily	active	users,	1 million+	posts	analyzed
•Evaluation in	terms	of	response	time,	performance,	and	usability
Implement
46
Facebook	Inspector	(FbI):	Architecture
47
FbI stats
Date	of	public launch August	23,	2015
Total Incoming	Requests 9	million	+
Total public	posts	analyzed 3.5	million	+
Total	downloads 5,000+
Daily	active	users 250+
Total	unique	browsers 1,250+
Posts marked	as	malicious 615,000+
Posts	marked	as	benign 2.9	million+
48
FbI evaluation:	Response	time
49
• ~80%	posts	processed	within	3	seconds
• Average	time	per	post:	2.635	seconds
FbI evaluation:	Usability
• Usability	study	with	53	participants
• SUS	score:	81.36	(A	grade)
• Higher	perceived	usability	that	>	90%	of	all	systems	evaluated	using	
SUS	scale
• 98.1%	participants	found	FbI “easy	to	use”
• 67.9%	participants	would	like	use	FbI frequently
• Quotes	from	users:
• “Saves	your	time	spent	on	spam	links	and	hence	enhances	user	
experience.”	
• “[Facebook	Inspector]	Can	be	useful	for	minors	and	people	who	lack	
the	judgement	to	decide	how	the	post	is.”	
50
Contributions	summary
• Identified	and	characterizedpoor	quality	content	spread	on	
Facebook,	with	the	purpose	of	identifying	poor	quality	
posts	published	during	news-making	events	in	real	time
• Evaluated supervised	learning	approaches	for	identifying	
poor	quality	posts	on	Facebook	in	real	time,	using	entity,	
textual,	metadata,	and	URL	features
• Deployed	and	evaluated a	novel	framework	and	system	for	
real	time	detection	of	poor	quality	posts	on	Facebook	
during	news-making	events
51
How	does	it	help?
• Social	media	services	are	the	primary	source	of	information	for	
majority	of	Internet	users
• Content	is	unmoderated	and	crowd-sourced;	everything	you	see	may	not	be	
true
• Facebook	Inspector	provides	a	useful	and	usable	real	world	solution	 to	
assist	users
• Methodology	for	fast	and	accurate	summarization	of	image	datasets	
pertaining	to	a	given	topic
• Government	agencies	/	brands	can	use	this	methodology	 to	quickly	produce	
high-level	summaries	of	events	/	products	and	gauge	the	pulse	of	the	
masses
52
Real	world	impact
• Real	time	system	Facebook	Inspector	built	to	identify	poor	
quality	content	is	used	by	250+	Facebook	users,	and	has	
processed	over	9	million	requests
• A	unique	dataset	of	Facebook	posts	containing	malicious	
URLs,	pages	posting	malicious	content,	and	images	
depicting	misinformation	from	20+	news-making	events
53
Limitations	and	future	work
• Current	system	does	not	incorporate	user	feedback
• We	would	like	to	enable	users	to	provide	feedback	to	make	a	more	
personalized	detection	model
• Computer	vision	techniques	have	limited	accuracy	on	social	
media	content
• Object	detection,	sentiment	analysis,	and	optical	character	
recognition	techniques	we	used	are	not	tested	thoroughly	on	social	
media	content
• Identify	and	rank	users	on	the	basis	of	degree	of	malice
• More	malicious	content	generated,	higher	the	ranking
54
Acknowledgements
• NIXI	for	travel	support	(eCRS,	2014)
• IIIT-Delhi	for	travel	support	(ASONAM,	2017)
• Govt.	of	India	for	funding	during	PhD
• Collaborators	and	co-authors:	Dr.	Anand Kashyap,	Shrey Bagroy,	
Anshuman Suri,	Varun	Bharadhwaj,	Aditi	Mithal
• Monitoring	committee:	Dr.	Vinayak and	Dr.	Sambuddho
• Peers:	Dr.	Niharika Sachdeva,	Anupama Aggarwal,	Dr.	Paridhi Jain,	
Dr.	Aditi	Gupta,	Srishti Gupta,	Rishabh Kaushal
• Members	of	Precog@IIITD and	CERC
• Everyone	else	who	has	been	part	of	my	journey…
55
Publications	– Part	of	thesis
• Dewan,	P.,	Bagroy,	S.,	and	Kumaraguru,	P.
Hiding	in	Plain	Sight:	The	Anatomy	of	Malicious	Pages	on	Facebook.
Book	chapter,	Lecture	Notes	in	Social	Networks,	Springer	2017	(To	appear)
• Dewan,	P.,	Suri,	A.,	Bharadhwaj,	V.,	Mithal,	A.,	and	Kumaraguru,	P.
Towards	Understanding	Crisis	Events	On	Online	Social	Networks	Through	Pictures.
IEEE/ACM	International	Conference	on	Advances	in	Social	Networks	Analysis	and	Mining	
(ASONAM),	2017.
• Dewan,	P.,	and	Kumaraguru,	P.
Facebook	Inspector	(FbI):	Towards	Automatic	Real	Time	Detection	of	Malicious	Content	on	
Facebook.
Social	Network	Analysis	and	Mining	Journal	(SNAM),	2017.	Volume	7,	Issue	1.
• Dewan,	P.,	Bagroy,	S.,	and	Kumaraguru,	P.
Hiding	in	Plain	Sight:	Characterizing	and	Detecting	Malicious	Facebook	Pages.
IEEE/ACM	International	Conference	on	Advances	in	Social	Networks	Analysis	and	Mining	
(ASONAM),	2016	(Short	paper)
• Dewan,	P.,	and	Kumaraguru,	P.
Towards	Automatic	Real	Time	Identification	of	Malicious	Posts	on	Facebook.
Thirteenth	Annual	Conference	on	Privacy,	Security	and	Trust	(PST),	2015
• Dewan,	P.,	Kashyap,	A.,	and	Kumaraguru,	P.
Analyzing	Social	and	Stylometric Features	to	Identify	Spear	phishing	Emails.
APWG	eCrime Research	Symposium	(eCRS),	2014
56
Publications	– Other
• Kaushal,	R.,	Chandok,	S.,	Jain	P., Dewan,	P.,	Gupta,	N.,	and	Kumaraguru,	P.
Nudging	Nemo:	Helping	Users	Control	Linkability across	Social	Networks.
9th	International	Conference	on	Social	Informatics	(SocInfo),	2017	(Short	paper).
• Deshpande,	P.,	Joshi,	S., Dewan,	P.,	Murthy,	K.,	Mohania,	M.,	Agrawal,	S.
The	Mask	of	ZoRRo:	preventing	information	leakage	from	documents.
Knowledge	and	Information	Systems	Journal,	2014
• Mittal,	S.,	Gupta,	N., Dewan,	P.,	Kumaraguru,	P.
Pinned	it!	A	large	scale	study	of	the	Pinterest	network.
1st	ACM	IKDD	Conference	on	Data	Sciences	(CoDS),	2014
• Dewan,	P.,	Gupta,	M.,	Goyal,	K.,	and	Kumaraguru,	P.
MultiOSN:	Realtime Monitoring	of	Real	World	Events	on	Multiple	Online	Social	Media
IBM	ICARE	2013
• Magalhães,	T., Dewan,	P.,	Kumaraguru,	P.,	Melo-Minardi,	R.,	and	Almeida,	V.
uTrack:	Track	Yourself!	Monitoring	Information	on	Online	Social	Media.
22nd	International	World	Wide	Web	Conference	(WWW)	(2013)
• Conway	M., Dewan	P.,	Kumaraguru P.,	McInerney L.
'White	Pride	Worldwide':	A	Meta- analysis	of	Stormfront.org
Internet,	Politics,	Policy	2012:	Big	Data,	Big	Challenges?,	Oxford	Internet	Institute,	
University	of	Oxford.
57
Thank	you!	
prateekd@iiitd.ac.in
http://precog.iiitd.edu.in/people/prateek

More Related Content

What's hot

PFN Summer Internship 2021 / Kohei Shinohara: Charge Transfer Modeling in Neu...
PFN Summer Internship 2021 / Kohei Shinohara: Charge Transfer Modeling in Neu...PFN Summer Internship 2021 / Kohei Shinohara: Charge Transfer Modeling in Neu...
PFN Summer Internship 2021 / Kohei Shinohara: Charge Transfer Modeling in Neu...Preferred Networks
 
Nedir Bu Çeviklik?
Nedir Bu Çeviklik?Nedir Bu Çeviklik?
Nedir Bu Çeviklik?ACM
 
A Gentle Introduction To Agile
A Gentle Introduction To AgileA Gentle Introduction To Agile
A Gentle Introduction To AgileMichael Sahota
 
The Naturalistic Evaluation
The Naturalistic EvaluationThe Naturalistic Evaluation
The Naturalistic Evaluationmrborup
 
カメラ位置姿勢とビュー行列
カメラ位置姿勢とビュー行列カメラ位置姿勢とビュー行列
カメラ位置姿勢とビュー行列Shohei Mori
 
メタスタディ (Vision and Language)
メタスタディ (Vision and Language)メタスタディ (Vision and Language)
メタスタディ (Vision and Language)Shintaro Yamamoto
 

What's hot (9)

PFN Summer Internship 2021 / Kohei Shinohara: Charge Transfer Modeling in Neu...
PFN Summer Internship 2021 / Kohei Shinohara: Charge Transfer Modeling in Neu...PFN Summer Internship 2021 / Kohei Shinohara: Charge Transfer Modeling in Neu...
PFN Summer Internship 2021 / Kohei Shinohara: Charge Transfer Modeling in Neu...
 
Nedir Bu Çeviklik?
Nedir Bu Çeviklik?Nedir Bu Çeviklik?
Nedir Bu Çeviklik?
 
A Gentle Introduction To Agile
A Gentle Introduction To AgileA Gentle Introduction To Agile
A Gentle Introduction To Agile
 
Agile management.pptx
Agile management.pptxAgile management.pptx
Agile management.pptx
 
Kanban VS Scrum
Kanban VS ScrumKanban VS Scrum
Kanban VS Scrum
 
The Naturalistic Evaluation
The Naturalistic EvaluationThe Naturalistic Evaluation
The Naturalistic Evaluation
 
Results based monitoring
Results based monitoringResults based monitoring
Results based monitoring
 
カメラ位置姿勢とビュー行列
カメラ位置姿勢とビュー行列カメラ位置姿勢とビュー行列
カメラ位置姿勢とビュー行列
 
メタスタディ (Vision and Language)
メタスタディ (Vision and Language)メタスタディ (Vision and Language)
メタスタディ (Vision and Language)
 

Similar to Techniques for Automating Quality Assessment of Context-specific Content on Social Media Services

User Research: Big R meets little R (a Richmond Design Lightning Talk)
User Research: Big R meets little R (a Richmond Design Lightning Talk)User Research: Big R meets little R (a Richmond Design Lightning Talk)
User Research: Big R meets little R (a Richmond Design Lightning Talk)Ashley Cook
 
University Public Driven Applications - Big Data and Organizational Design
University Public Driven Applications - Big Data and Organizational Design University Public Driven Applications - Big Data and Organizational Design
University Public Driven Applications - Big Data and Organizational Design maria chiara pettenati
 
NCME Big Data in Education
NCME Big Data  in EducationNCME Big Data  in Education
NCME Big Data in EducationPhilip Piety
 
Web tools in digital ecosystem
Web tools in digital ecosystemWeb tools in digital ecosystem
Web tools in digital ecosystemRam Nath
 
SMART: Staff are Mobile And Ready to Teach
SMART: Staff are Mobile And Ready to TeachSMART: Staff are Mobile And Ready to Teach
SMART: Staff are Mobile And Ready to TeachAndrew Middleton
 
Porting a Clinical Mobile Device Application from iPhone to Android using Onl...
Porting a Clinical Mobile Device Application from iPhone to Android using Onl...Porting a Clinical Mobile Device Application from iPhone to Android using Onl...
Porting a Clinical Mobile Device Application from iPhone to Android using Onl...Surgical Neurology International
 
#mytweet via Instagram: Exploring User Behaviour Across Multiple Social Networks
#mytweet via Instagram: Exploring User Behaviour Across Multiple Social Networks#mytweet via Instagram: Exploring User Behaviour Across Multiple Social Networks
#mytweet via Instagram: Exploring User Behaviour Across Multiple Social NetworksBang Hui Lim
 
Developing rich interactive eBooks to teach linked open data to professionals...
Developing rich interactive eBooks to teach linked open data to professionals...Developing rich interactive eBooks to teach linked open data to professionals...
Developing rich interactive eBooks to teach linked open data to professionals...John Domingue
 
SGCI - Science Gateways Bootcamp: Strategies for Developing, Operating and Su...
SGCI - Science Gateways Bootcamp: Strategies for Developing, Operating and Su...SGCI - Science Gateways Bootcamp: Strategies for Developing, Operating and Su...
SGCI - Science Gateways Bootcamp: Strategies for Developing, Operating and Su...Sandra Gesing
 
FSCI Persistent Identifiers
FSCI Persistent IdentifiersFSCI Persistent Identifiers
FSCI Persistent IdentifiersARDC
 
Google apps for pkm
Google apps for pkmGoogle apps for pkm
Google apps for pkmNAHEEM KT
 
Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
 
Infrastructure for Supporting Computational Social Science
Infrastructure for Supporting Computational Social ScienceInfrastructure for Supporting Computational Social Science
Infrastructure for Supporting Computational Social ScienceDerek Hansen
 
Enriching social media personas with personality traits
Enriching social media personas with personality traitsEnriching social media personas with personality traits
Enriching social media personas with personality traitsJoni Salminen
 
Ejis Analysis
Ejis AnalysisEjis Analysis
Ejis Analysisu3037519
 
Organizational network analysis RIGA COMM 2019
Organizational network analysis RIGA COMM 2019Organizational network analysis RIGA COMM 2019
Organizational network analysis RIGA COMM 2019Muntis Rudzitis
 

Similar to Techniques for Automating Quality Assessment of Context-specific Content on Social Media Services (20)

User Research: Big R meets little R (a Richmond Design Lightning Talk)
User Research: Big R meets little R (a Richmond Design Lightning Talk)User Research: Big R meets little R (a Richmond Design Lightning Talk)
User Research: Big R meets little R (a Richmond Design Lightning Talk)
 
University Public Driven Applications - Big Data and Organizational Design
University Public Driven Applications - Big Data and Organizational Design University Public Driven Applications - Big Data and Organizational Design
University Public Driven Applications - Big Data and Organizational Design
 
NISO Plus: Data Discovery and Reuse: AI Solutions & the Human Factor
NISO Plus: Data Discovery and Reuse: AI Solutions & the Human FactorNISO Plus: Data Discovery and Reuse: AI Solutions & the Human Factor
NISO Plus: Data Discovery and Reuse: AI Solutions & the Human Factor
 
Classroom of the futurev3
Classroom of the futurev3Classroom of the futurev3
Classroom of the futurev3
 
NCME Big Data in Education
NCME Big Data  in EducationNCME Big Data  in Education
NCME Big Data in Education
 
Web tools in digital ecosystem
Web tools in digital ecosystemWeb tools in digital ecosystem
Web tools in digital ecosystem
 
SMART: Staff are Mobile And Ready to Teach
SMART: Staff are Mobile And Ready to TeachSMART: Staff are Mobile And Ready to Teach
SMART: Staff are Mobile And Ready to Teach
 
Porting a Clinical Mobile Device Application from iPhone to Android using Onl...
Porting a Clinical Mobile Device Application from iPhone to Android using Onl...Porting a Clinical Mobile Device Application from iPhone to Android using Onl...
Porting a Clinical Mobile Device Application from iPhone to Android using Onl...
 
#mytweet via Instagram: Exploring User Behaviour Across Multiple Social Networks
#mytweet via Instagram: Exploring User Behaviour Across Multiple Social Networks#mytweet via Instagram: Exploring User Behaviour Across Multiple Social Networks
#mytweet via Instagram: Exploring User Behaviour Across Multiple Social Networks
 
Developing rich interactive eBooks to teach linked open data to professionals...
Developing rich interactive eBooks to teach linked open data to professionals...Developing rich interactive eBooks to teach linked open data to professionals...
Developing rich interactive eBooks to teach linked open data to professionals...
 
SGCI - Science Gateways Bootcamp: Strategies for Developing, Operating and Su...
SGCI - Science Gateways Bootcamp: Strategies for Developing, Operating and Su...SGCI - Science Gateways Bootcamp: Strategies for Developing, Operating and Su...
SGCI - Science Gateways Bootcamp: Strategies for Developing, Operating and Su...
 
FSCI Persistent Identifiers
FSCI Persistent IdentifiersFSCI Persistent Identifiers
FSCI Persistent Identifiers
 
Google apps for pkm
Google apps for pkmGoogle apps for pkm
Google apps for pkm
 
Accessible Privacy and Security
Accessible Privacy and SecurityAccessible Privacy and Security
Accessible Privacy and Security
 
Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)
 
Infrastructure for Supporting Computational Social Science
Infrastructure for Supporting Computational Social ScienceInfrastructure for Supporting Computational Social Science
Infrastructure for Supporting Computational Social Science
 
Enriching social media personas with personality traits
Enriching social media personas with personality traitsEnriching social media personas with personality traits
Enriching social media personas with personality traits
 
John Domingue. Developing rich interactive e books to teach linked open data ...
John Domingue. Developing rich interactive e books to teach linked open data ...John Domingue. Developing rich interactive e books to teach linked open data ...
John Domingue. Developing rich interactive e books to teach linked open data ...
 
Ejis Analysis
Ejis AnalysisEjis Analysis
Ejis Analysis
 
Organizational network analysis RIGA COMM 2019
Organizational network analysis RIGA COMM 2019Organizational network analysis RIGA COMM 2019
Organizational network analysis RIGA COMM 2019
 

More from IIIT Hyderabad

Responsible & Safe AI Systems at ACM India ROCS at IIT Bombay
Responsible & Safe AI Systems at ACM India ROCS at IIT BombayResponsible & Safe AI Systems at ACM India ROCS at IIT Bombay
Responsible & Safe AI Systems at ACM India ROCS at IIT BombayIIIT Hyderabad
 
International Collaboration: Experiences, Challenges, Success stories
International Collaboration: Experiences, Challenges, Success storiesInternational Collaboration: Experiences, Challenges, Success stories
International Collaboration: Experiences, Challenges, Success storiesIIIT Hyderabad
 
Responsible & Safe AI: #LegalBias #Inconsistency #BiasinLLMs #MultiModalBias
Responsible & Safe AI: #LegalBias #Inconsistency #BiasinLLMs #MultiModalBiasResponsible & Safe AI: #LegalBias #Inconsistency #BiasinLLMs #MultiModalBias
Responsible & Safe AI: #LegalBias #Inconsistency #BiasinLLMs #MultiModalBiasIIIT Hyderabad
 
Identify, Inspect and Intervene Multimodal Fake News
Identify, Inspect and Intervene Multimodal Fake NewsIdentify, Inspect and Intervene Multimodal Fake News
Identify, Inspect and Intervene Multimodal Fake NewsIIIT Hyderabad
 
Data Science for Social Good: #MentalHealth #CodeMix #LegalNLP #AISafety
Data Science for Social Good: #MentalHealth #CodeMix #LegalNLP #AISafetyData Science for Social Good: #MentalHealth #CodeMix #LegalNLP #AISafety
Data Science for Social Good: #MentalHealth #CodeMix #LegalNLP #AISafetyIIIT Hyderabad
 
It is our choices, Harry, that show what we truly are, far more than our abil...
It is our choices, Harry, that show what we truly are, far more than our abil...It is our choices, Harry, that show what we truly are, far more than our abil...
It is our choices, Harry, that show what we truly are, far more than our abil...IIIT Hyderabad
 
Beyond the Surface: A Computational Exploration of Linguistic Ambiguity
Beyond the Surface: A Computational Exploration of Linguistic AmbiguityBeyond the Surface: A Computational Exploration of Linguistic Ambiguity
Beyond the Surface: A Computational Exploration of Linguistic AmbiguityIIIT Hyderabad
 
Data Science for Social Good: #LegalNLP #AlgorithmicBias...
Data Science for Social Good:                      #LegalNLP #AlgorithmicBias...Data Science for Social Good:                      #LegalNLP #AlgorithmicBias...
Data Science for Social Good: #LegalNLP #AlgorithmicBias...IIIT Hyderabad
 
How to Write a (Good) Research Paper
How to Write a (Good) Research Paper How to Write a (Good) Research Paper
How to Write a (Good) Research Paper IIIT Hyderabad
 
Data Science for Social Good: #LegalNLP #AlgorithmicBias
Data Science for Social Good: #LegalNLP #AlgorithmicBiasData Science for Social Good: #LegalNLP #AlgorithmicBias
Data Science for Social Good: #LegalNLP #AlgorithmicBiasIIIT Hyderabad
 
Social Computing Research in India
Social Computing Research in IndiaSocial Computing Research in India
Social Computing Research in IndiaIIIT Hyderabad
 
Social Computing Research in India
Social Computing Research in IndiaSocial Computing Research in India
Social Computing Research in IndiaIIIT Hyderabad
 
Modeling Online User Interactions and their Offline effects on Socio-Technica...
Modeling Online User Interactions and their Offline effects on Socio-Technica...Modeling Online User Interactions and their Offline effects on Socio-Technica...
Modeling Online User Interactions and their Offline effects on Socio-Technica...IIIT Hyderabad
 
Privacy. Winter School on “Topics in Digital Trust”. IIT Bombay
Privacy. Winter School on “Topics in Digital Trust”. IIT BombayPrivacy. Winter School on “Topics in Digital Trust”. IIT Bombay
Privacy. Winter School on “Topics in Digital Trust”. IIT BombayIIIT Hyderabad
 
It is our choices, Harry, that show what we truly are, far more than our abil...
It is our choices, Harry, that show what we truly are, far more than our abil...It is our choices, Harry, that show what we truly are, far more than our abil...
It is our choices, Harry, that show what we truly are, far more than our abil...IIIT Hyderabad
 
It is our choices, Harry, that show what we truly are, far more than our abil...
It is our choices, Harry, that show what we truly are, far more than our abil...It is our choices, Harry, that show what we truly are, far more than our abil...
It is our choices, Harry, that show what we truly are, far more than our abil...IIIT Hyderabad
 
Leveraging Social Media for Financial Advice
Leveraging Social Media for Financial AdviceLeveraging Social Media for Financial Advice
Leveraging Social Media for Financial AdviceIIIT Hyderabad
 
Development of Stress Induction and Detection System to Study its Effect on B...
Development of Stress Induction and Detection System to Study its Effect on B...Development of Stress Induction and Detection System to Study its Effect on B...
Development of Stress Induction and Detection System to Study its Effect on B...IIIT Hyderabad
 
A Framework for Automatic Question Answering in Indian Languages
A Framework for Automatic Question Answering in Indian LanguagesA Framework for Automatic Question Answering in Indian Languages
A Framework for Automatic Question Answering in Indian LanguagesIIIT Hyderabad
 

More from IIIT Hyderabad (20)

Responsible & Safe AI Systems at ACM India ROCS at IIT Bombay
Responsible & Safe AI Systems at ACM India ROCS at IIT BombayResponsible & Safe AI Systems at ACM India ROCS at IIT Bombay
Responsible & Safe AI Systems at ACM India ROCS at IIT Bombay
 
International Collaboration: Experiences, Challenges, Success stories
International Collaboration: Experiences, Challenges, Success storiesInternational Collaboration: Experiences, Challenges, Success stories
International Collaboration: Experiences, Challenges, Success stories
 
Responsible & Safe AI: #LegalBias #Inconsistency #BiasinLLMs #MultiModalBias
Responsible & Safe AI: #LegalBias #Inconsistency #BiasinLLMs #MultiModalBiasResponsible & Safe AI: #LegalBias #Inconsistency #BiasinLLMs #MultiModalBias
Responsible & Safe AI: #LegalBias #Inconsistency #BiasinLLMs #MultiModalBias
 
Identify, Inspect and Intervene Multimodal Fake News
Identify, Inspect and Intervene Multimodal Fake NewsIdentify, Inspect and Intervene Multimodal Fake News
Identify, Inspect and Intervene Multimodal Fake News
 
#ChatGPT #ResponsibleAI
#ChatGPT #ResponsibleAI#ChatGPT #ResponsibleAI
#ChatGPT #ResponsibleAI
 
Data Science for Social Good: #MentalHealth #CodeMix #LegalNLP #AISafety
Data Science for Social Good: #MentalHealth #CodeMix #LegalNLP #AISafetyData Science for Social Good: #MentalHealth #CodeMix #LegalNLP #AISafety
Data Science for Social Good: #MentalHealth #CodeMix #LegalNLP #AISafety
 
It is our choices, Harry, that show what we truly are, far more than our abil...
It is our choices, Harry, that show what we truly are, far more than our abil...It is our choices, Harry, that show what we truly are, far more than our abil...
It is our choices, Harry, that show what we truly are, far more than our abil...
 
Beyond the Surface: A Computational Exploration of Linguistic Ambiguity
Beyond the Surface: A Computational Exploration of Linguistic AmbiguityBeyond the Surface: A Computational Exploration of Linguistic Ambiguity
Beyond the Surface: A Computational Exploration of Linguistic Ambiguity
 
Data Science for Social Good: #LegalNLP #AlgorithmicBias...
Data Science for Social Good:                      #LegalNLP #AlgorithmicBias...Data Science for Social Good:                      #LegalNLP #AlgorithmicBias...
Data Science for Social Good: #LegalNLP #AlgorithmicBias...
 
How to Write a (Good) Research Paper
How to Write a (Good) Research Paper How to Write a (Good) Research Paper
How to Write a (Good) Research Paper
 
Data Science for Social Good: #LegalNLP #AlgorithmicBias
Data Science for Social Good: #LegalNLP #AlgorithmicBiasData Science for Social Good: #LegalNLP #AlgorithmicBias
Data Science for Social Good: #LegalNLP #AlgorithmicBias
 
Social Computing Research in India
Social Computing Research in IndiaSocial Computing Research in India
Social Computing Research in India
 
Social Computing Research in India
Social Computing Research in IndiaSocial Computing Research in India
Social Computing Research in India
 
Modeling Online User Interactions and their Offline effects on Socio-Technica...
Modeling Online User Interactions and their Offline effects on Socio-Technica...Modeling Online User Interactions and their Offline effects on Socio-Technica...
Modeling Online User Interactions and their Offline effects on Socio-Technica...
 
Privacy. Winter School on “Topics in Digital Trust”. IIT Bombay
Privacy. Winter School on “Topics in Digital Trust”. IIT BombayPrivacy. Winter School on “Topics in Digital Trust”. IIT Bombay
Privacy. Winter School on “Topics in Digital Trust”. IIT Bombay
 
It is our choices, Harry, that show what we truly are, far more than our abil...
It is our choices, Harry, that show what we truly are, far more than our abil...It is our choices, Harry, that show what we truly are, far more than our abil...
It is our choices, Harry, that show what we truly are, far more than our abil...
 
It is our choices, Harry, that show what we truly are, far more than our abil...
It is our choices, Harry, that show what we truly are, far more than our abil...It is our choices, Harry, that show what we truly are, far more than our abil...
It is our choices, Harry, that show what we truly are, far more than our abil...
 
Leveraging Social Media for Financial Advice
Leveraging Social Media for Financial AdviceLeveraging Social Media for Financial Advice
Leveraging Social Media for Financial Advice
 
Development of Stress Induction and Detection System to Study its Effect on B...
Development of Stress Induction and Detection System to Study its Effect on B...Development of Stress Induction and Detection System to Study its Effect on B...
Development of Stress Induction and Detection System to Study its Effect on B...
 
A Framework for Automatic Question Answering in Indian Languages
A Framework for Automatic Question Answering in Indian LanguagesA Framework for Automatic Question Answering in Indian Languages
A Framework for Automatic Question Answering in Indian Languages
 

Recently uploaded

Novel 3D-Printed Soft Linear and Bending Actuators
Novel 3D-Printed Soft Linear and Bending ActuatorsNovel 3D-Printed Soft Linear and Bending Actuators
Novel 3D-Printed Soft Linear and Bending ActuatorsResearcher Researcher
 
AntColonyOptimizationManetNetworkAODV.pptx
AntColonyOptimizationManetNetworkAODV.pptxAntColonyOptimizationManetNetworkAODV.pptx
AntColonyOptimizationManetNetworkAODV.pptxLina Kadam
 
2022 AWS DNA Hackathon 장애 대응 솔루션 jarvis.
2022 AWS DNA Hackathon 장애 대응 솔루션 jarvis.2022 AWS DNA Hackathon 장애 대응 솔루션 jarvis.
2022 AWS DNA Hackathon 장애 대응 솔루션 jarvis.elesangwon
 
Guardians of E-Commerce: Harnessing NLP and Machine Learning Approaches for A...
Guardians of E-Commerce: Harnessing NLP and Machine Learning Approaches for A...Guardians of E-Commerce: Harnessing NLP and Machine Learning Approaches for A...
Guardians of E-Commerce: Harnessing NLP and Machine Learning Approaches for A...IJAEMSJORNAL
 
SOFTWARE ESTIMATION COCOMO AND FP CALCULATION
SOFTWARE ESTIMATION COCOMO AND FP CALCULATIONSOFTWARE ESTIMATION COCOMO AND FP CALCULATION
SOFTWARE ESTIMATION COCOMO AND FP CALCULATIONSneha Padhiar
 
March 2024 - Top 10 Read Articles in Artificial Intelligence and Applications...
March 2024 - Top 10 Read Articles in Artificial Intelligence and Applications...March 2024 - Top 10 Read Articles in Artificial Intelligence and Applications...
March 2024 - Top 10 Read Articles in Artificial Intelligence and Applications...gerogepatton
 
A brief look at visionOS - How to develop app on Apple's Vision Pro
A brief look at visionOS - How to develop app on Apple's Vision ProA brief look at visionOS - How to develop app on Apple's Vision Pro
A brief look at visionOS - How to develop app on Apple's Vision ProRay Yuan Liu
 
Secure Key Crypto - Tech Paper JET Tech Labs
Secure Key Crypto - Tech Paper JET Tech LabsSecure Key Crypto - Tech Paper JET Tech Labs
Secure Key Crypto - Tech Paper JET Tech Labsamber724300
 
priority interrupt computer organization
priority interrupt computer organizationpriority interrupt computer organization
priority interrupt computer organizationchnrketan
 
1- Practice occupational health and safety procedures.pptx
1- Practice occupational health and safety procedures.pptx1- Practice occupational health and safety procedures.pptx
1- Practice occupational health and safety procedures.pptxMel Paras
 
Prach: A Feature-Rich Platform Empowering the Autism Community
Prach: A Feature-Rich Platform Empowering the Autism CommunityPrach: A Feature-Rich Platform Empowering the Autism Community
Prach: A Feature-Rich Platform Empowering the Autism Communityprachaibot
 
Theory of Machine Notes / Lecture Material .pdf
Theory of Machine Notes / Lecture Material .pdfTheory of Machine Notes / Lecture Material .pdf
Theory of Machine Notes / Lecture Material .pdfShreyas Pandit
 
Gravity concentration_MI20612MI_________
Gravity concentration_MI20612MI_________Gravity concentration_MI20612MI_________
Gravity concentration_MI20612MI_________Romil Mishra
 
TEST CASE GENERATION GENERATION BLOCK BOX APPROACH
TEST CASE GENERATION GENERATION BLOCK BOX APPROACHTEST CASE GENERATION GENERATION BLOCK BOX APPROACH
TEST CASE GENERATION GENERATION BLOCK BOX APPROACHSneha Padhiar
 
Robotics-Asimov's Laws, Mechanical Subsystems, Robot Kinematics, Robot Dynami...
Robotics-Asimov's Laws, Mechanical Subsystems, Robot Kinematics, Robot Dynami...Robotics-Asimov's Laws, Mechanical Subsystems, Robot Kinematics, Robot Dynami...
Robotics-Asimov's Laws, Mechanical Subsystems, Robot Kinematics, Robot Dynami...Sumanth A
 
Submerged Combustion, Explosion Flame Combustion, Pulsating Combustion, and E...
Submerged Combustion, Explosion Flame Combustion, Pulsating Combustion, and E...Submerged Combustion, Explosion Flame Combustion, Pulsating Combustion, and E...
Submerged Combustion, Explosion Flame Combustion, Pulsating Combustion, and E...Ayisha586983
 
Machine Learning 5G Federated Learning.pdf
Machine Learning 5G Federated Learning.pdfMachine Learning 5G Federated Learning.pdf
Machine Learning 5G Federated Learning.pdfadeyimikaipaye
 
Javier_Fernandez_CARS_workshop_presentation.pptx
Javier_Fernandez_CARS_workshop_presentation.pptxJavier_Fernandez_CARS_workshop_presentation.pptx
Javier_Fernandez_CARS_workshop_presentation.pptxJavier Fernández Muñoz
 
High Voltage Engineering- OVER VOLTAGES IN ELECTRICAL POWER SYSTEMS
High Voltage Engineering- OVER VOLTAGES IN ELECTRICAL POWER SYSTEMSHigh Voltage Engineering- OVER VOLTAGES IN ELECTRICAL POWER SYSTEMS
High Voltage Engineering- OVER VOLTAGES IN ELECTRICAL POWER SYSTEMSsandhya757531
 

Recently uploaded (20)

Novel 3D-Printed Soft Linear and Bending Actuators
Novel 3D-Printed Soft Linear and Bending ActuatorsNovel 3D-Printed Soft Linear and Bending Actuators
Novel 3D-Printed Soft Linear and Bending Actuators
 
AntColonyOptimizationManetNetworkAODV.pptx
AntColonyOptimizationManetNetworkAODV.pptxAntColonyOptimizationManetNetworkAODV.pptx
AntColonyOptimizationManetNetworkAODV.pptx
 
2022 AWS DNA Hackathon 장애 대응 솔루션 jarvis.
2022 AWS DNA Hackathon 장애 대응 솔루션 jarvis.2022 AWS DNA Hackathon 장애 대응 솔루션 jarvis.
2022 AWS DNA Hackathon 장애 대응 솔루션 jarvis.
 
Guardians of E-Commerce: Harnessing NLP and Machine Learning Approaches for A...
Guardians of E-Commerce: Harnessing NLP and Machine Learning Approaches for A...Guardians of E-Commerce: Harnessing NLP and Machine Learning Approaches for A...
Guardians of E-Commerce: Harnessing NLP and Machine Learning Approaches for A...
 
SOFTWARE ESTIMATION COCOMO AND FP CALCULATION
SOFTWARE ESTIMATION COCOMO AND FP CALCULATIONSOFTWARE ESTIMATION COCOMO AND FP CALCULATION
SOFTWARE ESTIMATION COCOMO AND FP CALCULATION
 
March 2024 - Top 10 Read Articles in Artificial Intelligence and Applications...
March 2024 - Top 10 Read Articles in Artificial Intelligence and Applications...March 2024 - Top 10 Read Articles in Artificial Intelligence and Applications...
March 2024 - Top 10 Read Articles in Artificial Intelligence and Applications...
 
A brief look at visionOS - How to develop app on Apple's Vision Pro
A brief look at visionOS - How to develop app on Apple's Vision ProA brief look at visionOS - How to develop app on Apple's Vision Pro
A brief look at visionOS - How to develop app on Apple's Vision Pro
 
Secure Key Crypto - Tech Paper JET Tech Labs
Secure Key Crypto - Tech Paper JET Tech LabsSecure Key Crypto - Tech Paper JET Tech Labs
Secure Key Crypto - Tech Paper JET Tech Labs
 
priority interrupt computer organization
priority interrupt computer organizationpriority interrupt computer organization
priority interrupt computer organization
 
1- Practice occupational health and safety procedures.pptx
1- Practice occupational health and safety procedures.pptx1- Practice occupational health and safety procedures.pptx
1- Practice occupational health and safety procedures.pptx
 
Prach: A Feature-Rich Platform Empowering the Autism Community
Prach: A Feature-Rich Platform Empowering the Autism CommunityPrach: A Feature-Rich Platform Empowering the Autism Community
Prach: A Feature-Rich Platform Empowering the Autism Community
 
Theory of Machine Notes / Lecture Material .pdf
Theory of Machine Notes / Lecture Material .pdfTheory of Machine Notes / Lecture Material .pdf
Theory of Machine Notes / Lecture Material .pdf
 
Gravity concentration_MI20612MI_________
Gravity concentration_MI20612MI_________Gravity concentration_MI20612MI_________
Gravity concentration_MI20612MI_________
 
TEST CASE GENERATION GENERATION BLOCK BOX APPROACH
TEST CASE GENERATION GENERATION BLOCK BOX APPROACHTEST CASE GENERATION GENERATION BLOCK BOX APPROACH
TEST CASE GENERATION GENERATION BLOCK BOX APPROACH
 
Versatile Engineering Construction Firms
Versatile Engineering Construction FirmsVersatile Engineering Construction Firms
Versatile Engineering Construction Firms
 
Robotics-Asimov's Laws, Mechanical Subsystems, Robot Kinematics, Robot Dynami...
Robotics-Asimov's Laws, Mechanical Subsystems, Robot Kinematics, Robot Dynami...Robotics-Asimov's Laws, Mechanical Subsystems, Robot Kinematics, Robot Dynami...
Robotics-Asimov's Laws, Mechanical Subsystems, Robot Kinematics, Robot Dynami...
 
Submerged Combustion, Explosion Flame Combustion, Pulsating Combustion, and E...
Submerged Combustion, Explosion Flame Combustion, Pulsating Combustion, and E...Submerged Combustion, Explosion Flame Combustion, Pulsating Combustion, and E...
Submerged Combustion, Explosion Flame Combustion, Pulsating Combustion, and E...
 
Machine Learning 5G Federated Learning.pdf
Machine Learning 5G Federated Learning.pdfMachine Learning 5G Federated Learning.pdf
Machine Learning 5G Federated Learning.pdf
 
Javier_Fernandez_CARS_workshop_presentation.pptx
Javier_Fernandez_CARS_workshop_presentation.pptxJavier_Fernandez_CARS_workshop_presentation.pptx
Javier_Fernandez_CARS_workshop_presentation.pptx
 
High Voltage Engineering- OVER VOLTAGES IN ELECTRICAL POWER SYSTEMS
High Voltage Engineering- OVER VOLTAGES IN ELECTRICAL POWER SYSTEMSHigh Voltage Engineering- OVER VOLTAGES IN ELECTRICAL POWER SYSTEMS
High Voltage Engineering- OVER VOLTAGES IN ELECTRICAL POWER SYSTEMS
 

Techniques for Automating Quality Assessment of Context-specific Content on Social Media Services