Mining The Social Web Data Mining Facebook Twit...
User-generated content from social media or other mediums may present a suitable method to gauge sentiment. However, the same study determined that inaccuracies arose from using sentiment from an entire social media population; therefore, experts within the masses should be identified. The aggregation of data from Facebook utilized in conjunction with more traditional resources could produce more accurate market predictions, otherwise known as crowd sourcing.
Mining the Social Web Data Mining Facebook Twit...
Cambridge Analytica reportedly acquired the data in a way that violated the social network's policies. It then reportedly tapped the information to build psychographic profiles of users and their friends, which were used for targeted political ads in the UK's Brexit referendum campaign, as well as by Trump's team during the 2016 US election.
Facebook says it told Cambridge Analytica to delete the data, but reports suggest the info wasn't destroyed. Cambridge Analytica says it complies with the social network's rules, only receives data "obtained legally and fairly," and did wipe out the data Facebook is worried about.
The New York Times characterized the original problem as a data "breach" and said it's "one of the largest data leaks in the social network's history." That's in part because the roughly 270,000 users who gave Kogan access to their information allowed him to collect data on their friends as well. In total, more than 87 million Facebook users are said to have been affected.
When you log in to an app using your Facebook account, the developer typically asks for access to information the social network has. Sometimes it's just your name and email address. Other times, it's your location and your friends' data too.
The consent decree required that Facebook must get users to agree to and must notify them about the social network sharing their data. Facebook earlier told The Washington Post it rejects "any suggestion of violation of the consent decree."
Some, like New Jersey Rep. Frank Pallone, hammered Zuckerberg on default privacy settings. California Rep. Anna Eshoo asked Zuckerberg if his own data was swept up in the Cambridge Analytica scandal. (He said that it was.) And Florida Rep. Kathy Castor and New Mexico Rep. Ben Lujan raised concerns about how much Facebook follows people as they browse the web -- and whether people without accounts on the social media network still get tracked via "shadow profiles." Zuckerberg said that he wasn't familiar with that term and that Facebook collects data on nonusers for security purposes.
We're also starting to see some action that could hit Facebook in the wallet. Within days of the scandal erupting, Firefox maker Mozilla said it would no longer advertise on Facebook because of data privacy concerns, and it launched a petition to ask the social network to improve its privacy settings. Meanwhile, Tesla and SpaceX CEO Elon Musk has taken a different kind of stand. Prompted by an inquiry from a Twitter user, he quickly deleted both companies' Facebook pages. So did Playboy, for what it's worth.
But from swipes to clicks to status updates, our online lives are being captured by social media companies and used to fill some of the largest data servers in the world. We are producing more data than ever before. By looking at these data points as a whole, we can gain tremendous insight into human behavior. We can also investigate the harm done by these systems, from detecting false online actors (for example, automated bot accounts or fake profiles that seed misinformation) to understanding how algorithms surface questionable content to viewers over time.
If we look at these data points collectively, we can find patterns, trends, or anomalies and, hopefully, better understand the ways in which we consume and shape the human experience online. This book aims to help those who want to go from simply observing the social web one post or tweet at a time to understanding it on a larger, more meaningful scale.
Defines scraping and describes how to inspect HTML to structure content from web pages into data. It also covers data archives that social media companies provide to users of their own data and shows you how to extract data into .csv files.
So many of our interactions and our behavior are now captured on social media platforms. While companies like Facebook or Twitter have certainly found ways to leverage this data in aggregate, I firmly believe that researchers and users themselves should be enabled and empowered to glean their own insights from some of these vast data sets. This book offers a beginner-friendly introduction to this kind of data analysis.
Criminals are adept at tricking social media users into handing over sensitive information, stealing personal data, and gaining access to accounts users consider private. Following are typical social media threats.
Phishing AttemptsPhishing is one of the most common ways criminals attempt to gain access to sensitive personal information. Often in the form of an email, a text message, or a phone call, a phishing attack presents itself as a message from a legitimate organization. These messages trick people into sharing sensitive data, including passwords, banking information, or credit card details. Phishing attacks often pose as social media platforms. In August 2019, a massive phishing campaign targeted Instagram users by posing as a two-factor authentication system, prompting users to log in to a false Instagram page.
Aaron Williams: Social media has developed into a tool that is used far beyond what it was originally intended for, whether that was keeping in touch with friends and family, staying in touch with business contacts, or simply sharing photos. Conversations can now be monitored as they happen in real time thanks to the instant nature of social media. These conversations often originate from mobile devices, which means their data has a spatio-temporal component to it, and by merging that data with pop culture topics, political events, or even natural disaster headlines, new insights can be gleaned.
Using location intelligence, these social media topics can be tracked as they spread from neighborhood to neighborhood and around the world. More companies are looking to tap into the power of this data. Brands are now able to see what other brands their loyal customers are using to explore co-marketing opportunities. Sentiment analysis of customer posts is being used to identify products that are selling below where they should be based on customer appreciation, helping companies identify marketing or sales gaps.
Let's also not forget that data is being collected by the social networks themselves on our activity, including likes, posts, and images. Social media companies make billions by being able to target us with the right ad for the right product at the right time, and that's only possible because our social media data is such a rich representation of us.
Data from any of the social media sites can provide information that can inform strategic business decisions. The "best" information is often the information driving an organization forward. For instance, Twitter data is almost peerless among social media data in its ability to provide a glimpse into the human experience -- revealing what people are saying when and where. The ability to monitor hashtags on Twitter is an important part of the power of that platform and a fantastic tool for brands looking to find and engage their customers in real time.
Given all the controversy about social media sites sharing data, how is social media data obtained (for example, are you screen scraping or getting feeds from the social media companies themselves) and are there privacy concerns about accessing it?
Because our platform can handle all kinds of structured data, we remain agnostic to how our users get the data they load into OmniSci. We get the data for our TweetMap demo directly from the Twitter API, and we have a license from Twitter to use that data in our demo. All of the social media APIs are getting more restrictive though, which is a shame, because it does encourage the kind of data grab you're talking about.
The era of big data has, among others, three characteristics: the huge amounts of data created every day and in every form by everyday people, artificial intelligence tools to mine information from those data and effective algorithms that allow this data mining in real or close to real time. On the other hand, opinion mining in social media is nowadays an important parameter of social media marketing. Digital media giants such as Google and Facebook developed and employed their own tools for that purpose. These tools are based on publicly available software libraries and tools such as Word2Vec (or Doc2Vec) and fasttext, which emphasize topic modeling and extract low-level features using deep learning approaches. So far, researchers have focused their efforts on opinion mining and especially on sentiment analysis of tweets. This trend reflects the availability of the Twitter API that simplifies automatic data (tweet) collection and testing of the proposed algorithms in real situations. However, if we are really interested in realistic opinion mining we should consider mining opinions from social media platforms such as Facebook and Instagram, which are far more popular among everyday people. The basic purpose of this paper is to compare various kinds of low-level features, including those extracted through deep learning, as in fasttext and Doc2Vec, and keywords suggested by the crowd, called crowd lexicon herein, through a crowdsourcing platform. The application target is sentiment analysis of tweets and Facebook comments on commercial products. We also compare several machine learning methods for the creation of sentiment analysis models and conclude that, even in the era of big data, allowing people to annotate (a small portion of) data would allow effective artificial intelligence tools to be developed using the learning by example paradigm. 041b061a72