Event Mining from the Twitter Data for Recommendation System
The goal of proposed project is to use location based tweets to identify local events. Users can be notified of local events before news outlets can report them. For example, if a traffic delay is detected, a user could avoid the delay by taking an alternate route. Proposed system would also be able
2025-06-28 16:27:08 - Adil Khan
Event Mining from the Twitter Data for Recommendation System
Project Area of Specialization Artificial IntelligenceProject SummaryThe goal of proposed project is to use location based tweets to identify local events. Users can be notified of local events before news outlets can report them. For example, if a traffic delay is detected, a user could avoid the delay by taking an alternate route. Proposed system would also be able to provide information to news stations so they can investigate further.Twitter is a web-based media stage that huge number of clients use to share refreshes about their lives. Regularly, these tweets are about local events happening around the user. However news organizations report on nearby events, the time it takes an office to find out about, explore, and report on the events can be significant, particularly when contrasted with the length of the events. Conversely, client’s translations of a potential events are transferred onto Twitter continuously, and a supporter of their Twitter channel can find out with regards to an events before a media source could communicate the data.
In this project, we will analyze tweets from a given geographical region to determine if an event has occurred. We then report the most descriptive tweet associated with the event. Solving this problem would be a quick way to alert a user about an incident that may be occurring in their area. Our approach splits data into location buckets, identifies spikes in tweet activity, and then clusters tweets based on similarity.
Objectives of our project is to determine which tweet provides concise and useful information about an events, given a set of tweets related to the event. Deciding how to assess exactness was intense as the dataset was not marked and events are emotionally characterized. To see how well our project performed, we went through the data and found all events that occurred in our dataset.Twitter is a social media platform that millions of users use to share updates about their lives. Often, these tweets are about local events happening around the user. Though news agencies report on local events, the time it takes an agency to learn about, investigate, and report on the event can be substantial, especially when compared to the length of the event. In contrast, users’ interpretations of a possible event are uploaded onto Twitter in real-time, and a follower of their Twitter feed can learn about an event before a news outlet could broadcast the information.
Project Implementation MethodWe would focus on more accurate temporal and spatial model for time and location estimation if we have more data with location information. For temporal model, we should consider about the interval time between the occurrence time and the post time. So we should not use post time as occurrence time. For spatial model, we’d like to apply Karman filtering or particle filtering into estimating the locations of events. We plan to detect real-time events of various kinds using Twitter, such as traffic jam,accidents and typhoon. Our model includes the assumption that a single instance of the target event exists at a certain time. For example, we assume that we do not have two or more earthquakes or typhoons simultaneously. Although the assumption is
reasonable for these cases, it might not hold for other events such as traffic jams, accidents, and rainbows. To realize multiple event detection, we must produce
advanced probabilistic models that allow hypotheses of multiple event occurrences.
A search query is important to search for possibly relevant tweets. For example, we set a query term as earthquake because most tweets mentioning an earthquake occurrence use this word. However, to improve the recall, it is necessary to obtain a good set of queries. We can use advanced algorithms for query expansion, which is a subject of our future work.
Open platform: Twitter is an open social information network, that means that it allows users to freely access other platforms, apps or tools from third parties, and so it allows information to be gathered and monitored. This is very useful to follow events or trending topics, to share or programme content and obtain reports from online tools. Instagram is also open, but Facebook, Snapchat and LinkedIn, for example, are not.
The power of RT: RTs manage to turn something viral much more than the Likes of other media. Although on Facebook, Instagram or LinkedIn the Likes help a publication to gain greater visibility, these social networks have still not achieved the immediacy of Twitter.The RT would be the equivalent to the “shares” on Facebook or “Repost” on Instagram, but it’s harder to get a share/repost than a Like, unlike Twitter, where the most common actions are the RTs.The algorithms of Facebook, Instagram and other social media select and limit the content shown to users. This makes Twitter the best option to follow events, as its algorithm does not yet filter as much as the other
Twitter Moments: A few months ago, Twitter launched a new function to allow brands to create a timeline with publications about their events. The advantage being that it allows them to give greater visibility to conversations about an event, although the “but” is that the Tweets have to be selected manually; but the upside is that this allows a filter and to be able to select the most relevant content, leaving the less useful or less valuable tweets to one
Public affinity: Depending on the public you are aiming the event at and the sector that the organizing brand belongs to, Twitter could be much more relative than Facebook, Instagram or Snapchat. Twitter is considered to be more professional and the average age of users is at some point between that of Facebook and Tumblr,
Technical Details of Final DeliverableTechnical Details Python would use the system provided certificate database on all platforms. Failure to locate such a database would be an error, and users would need to explicitly specify a location to fix it.
Twitter is what’s happening in the world and what people are talking about right now. You can access Twitter via the web or your mobile device. To share information on Twitter as widely as possible, we also provide companies, developers, and users with programmatic access to Twitter data through our APIs (application programming interfaces). This article explains what Twitter’s APIs are, what information is made available through them, and some of the protections Twitter has in place for their use. At a high level, APIs are the way computer programs “talk” to each other so that they can request and deliver information. This is done by allowing a software application to call what's known as an endpoint: an address that corresponds with a specific type of information we provide (endpoints are generally unique like phone numbers). Twitter allows access to parts of our service via APIs to allow people to build software that integrates with Twitter, like a solution that helps a company respond to customer feedback on Twitter.
Twitter data is unique from data shared by most other social platforms because it reflects information that users choose to share publicly. Our API platform provides broad access to public Twitter data that users have chosen to share with the world. We also support APIs that allow users to manage their own non-public Twitter information (e.g., Direct Messages) and provide this information to developers whom they have authorized to do Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 70000 | |||
| Laptop | Equipment | 1 | 70000 | 70000 |