pith. machine review for the scientific record. sign in

arxiv: 2604.02740 · v1 · submitted 2026-04-03 · 💻 cs.SI · cs.AI

Cross Event Detection and Topic Evolution Mining in cross events for Man Made Disasters in Social Media Streams

Pramod Bide , Sudhir Dhage , Mohammed Afaan Ansari , Rudresh Veerkhare This is my paper

Pith reviewed 2026-05-13 18:45 UTC · model grok-4.3

classification 💻 cs.SI cs.AI
keywords cross event detectiontopic evolution miningsocial media streamsTwitterman-made disastersevent clusteringWikipedia segmentationdisaster response
0
0 comments X

The pith

The CEED framework detects cross events overlapping in time and context within Twitter streams for man-made disasters through Wikipedia-based segmentation and similarity clustering.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes the CEED framework to identify cross events, which are similar incidents linked to a main man-made disaster event and occurring in the same timeframe. It processes tweets by segmenting them against Wikipedia titles to extract relevant parts and clusters these segments using a similarity measure to find overlapping events in both time and context. Additionally, it applies a topic evolution algorithm to observe how discussion focuses change as the events unfold. A reader might care because this helps track how information about related disasters spreads and evolves on social media, offering insights into public engagement with such events. Tests on actual Twitter data indicate the framework performs with good effectiveness and precision for these tasks.

Core claim

The paper establishes that cross events can be detected by first segmenting tweets using the Wikipedia title database and then clustering the segments based on similarity measures, revealing events that overlap in time and context with the main event; separately, the topic evolution algorithm tracks changes in the focused topics throughout the event's duration, and experiments on real Twitter datasets confirm the framework's effectiveness and precision in handling both aspects during the evolution of cross events for man-made disasters.

What carries the argument

Tweet segmentation using the Wikipedia title database combined with similarity-based clustering to identify temporally and contextually overlapping cross events, along with a topic evolution algorithm.

If this is right

  • Cross events overlapping in time and context can be identified from tweet clusters.
  • Topic shifts during an event's lifetime become visible through the evolution algorithm.
  • The approach proves effective for man-made disaster related discussions on Twitter.
  • Information dissemination reveals similarities and differences between linked events.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This could support development of monitoring tools for crisis response teams.
  • Applying the method to other platforms might yield similar patterns in information spread.
  • It opens questions about how cross events influence public opinion over longer periods.
  • Testing on datasets from natural disasters could validate broader applicability.

Load-bearing premise

Tweet segmentation with Wikipedia titles accurately captures event-related content and similarity clustering reliably groups cross events that overlap in time and context.

What would settle it

A dataset where human experts label cross events in Twitter streams, and the framework's output clusters show low agreement with those labels, or where topic changes do not align with manual review of discussion shifts.

Figures

Figures reproduced from arXiv: 2604.02740 by Mohammed Afaan Ansari, Pramod Bide, Rudresh Veerkhare, Sudhir Dhage.

Figure 1
Figure 1. Figure 1: CEED Model system flow the dynamic event databases during updates performed as the event evolves. But this approach incorporates temporal decay which reduces the accuracy and scalability of event detection. VSM (Vector Space models) [25] [26] are also used in many existing research to represent different social events in social media. But, VSM models do not consider other information related to events like… view at source ↗
Figure 2
Figure 2. Figure 2: EDA Model To achieve our goal of identifying the cross events we need to first structurize the tweets into clusters of events. For this purpose we use the Event Detection Algorithm (EDA model), [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Cross Event Detection Algorithm clustered Segments and Tweets in Events. Time similarity is calculated based on the overlapping nature of Events in terms of time. In CEDA(Algorithm 4) Representative Segments S(e) are extracted from Event e by sorting all segments present in Event Cluster e in descending order of there probability of occurrence and selecting √ N top segments where N is the total count of se… view at source ↗
Figure 5
Figure 5. Figure 5: Cross Event Heat Map 3) Topic Evolution Algorithm: The topic evolution algo￾rithm is concerned with the pattern of evolution of topics within clustered events. With respect to algorithm 5, topics are identified over the life span of the event and then the weightage of each topic is calculated in every subwindow to get the evolution timeline of the event. Following figures show the topic evolution timeline … view at source ↗
Figure 4
Figure 4. Figure 4: Event wise Tweet count 2) Cross Event Detection Algorithm: a) Cross Event Detection Matrix: It defines the degree to which all events are cross in nature. Using the CEDA algorithm the factor of cross incidents is calculated. Table IV demonstrates us how events are cross in nature. Table demonstrates that E1 and E3 are cross in nature as events say no to war and bring back abhinandan was going on in the sam… view at source ↗
Figure 6
Figure 6. Figure 6: Topic Evolution Timeline in E1 Table V SEGMENTS DESCRIBING TOPICS IN E1 Topic No. Segments 0 ’peace peace’, ’war peace’, ’peace war’ 1 ’bring back abhinandan’, ’say noto war’, ’saynotowar’ 2 ’wing commander’, ’jai hind’, ’abhinandan varthaman’ 3 ’go back modi’, ’nobel peace prize for imran khan’, ’pakistan leads with peace’ b) E2(blood donation by dss) [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 9
Figure 9. Figure 9: Topic Evolution Timeline in E4 Table VIII SEGMENTS DESCRIBING TOPICS IN E4 Topic No. Segments 0 ’crpf kashmir attack’, ’attack convoy’, ’crpf’, ’kashmir terror attack’, ’pulwana attack’, ’crpf jawans’, ’rip brave hearts’, ’terrorist attack’ 1 ’india’, ’kashmir’, ’surgical strike’, ’imran khan’, ’narendra modi’, ’pakistan’ [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Topic Evolution Timeline in E5 Table IX SEGMENTS DESCRIBING TOPICS IN E5 Topic No. Segments 0 ’golden globes’, ’bryan singer’, ’black girls matter’, ’goldenglobes’, ’surviving kelly’, ’red carpet’, ’surviv￾ingrkelly’, ’lifetime’, ’surviving r kellly’, ’suriving r kelly’, ’black women’, ’r kelly’, ’timesup’, ’tarana’, ’last year’, ’black men’, ’ryan seacrest’, ’golden globe awards’, ’times up’, ’muterkelly… view at source ↗
read the original abstract

Social media is widely used to share information globally and it also aids to gain attention from the world. When socially sensitive incidents like rape, human rights march, corruption, political controversy, chemical attacks occur, they gain immense attention from people all over the world, causing microblogging platforms like Twitter to get flooded with tweets related to such events. When an event evolves, many other events of a similar nature have happened in and around the same time frame. These are cross events because they are linked to the nature of the main event. Dissemination of information relating to such cross events helps in engaging the masses to share the varied views that emerge out of the similarities and differences between the events. Cross event detection is critical in determining the nature of events. Cross events have fulcrums points, i.e., topics around which the discussion is focused, as the event evolves which must be considered in topic evolution. We have proposed Cross Event Evolution Detection CEED framework which detects cross events that are similar with regards to their temporal nature resulting from main events. Event detection is based on the tweet segmentation using the Wikipedia title database and clustering segments based on a similarity measure. The cross event detection algorithm reveals events that overlap in both time and context to evaluate the effects of these cross events on deliberate negligent human actions. The topic evolution algorithm puts into perspective the change in topics for an events lifetime. The experimental results on a real Twitter data set demonstrate the effectiveness and precision of our proposed framework for both cross event detection and topic evolution algorithm during the evolution of cross events.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The manuscript proposes the CEED (Cross Event Evolution Detection) framework for identifying cross events in Twitter streams related to man-made disasters. Tweets are segmented against the Wikipedia title database and clustered by similarity to detect events that overlap in both time and context; a separate topic evolution algorithm tracks changes in discussion foci over an event lifetime. The central claim is that experiments on real Twitter data demonstrate the effectiveness and precision of both the cross-event detection and topic-evolution components.

Significance. If the empirical claims were supported by quantitative validation, the work would offer a concrete pipeline for linking temporally co-occurring disaster-related discussions in social media, with potential utility for situational awareness and public-opinion tracking. The design choice to anchor segmentation in an external knowledge base rather than purely data-driven fitting is a positive architectural feature that could reduce certain forms of overfitting.

major comments (3)
  1. [Experimental evaluation] Experimental evaluation section: the abstract states that experiments on real Twitter data demonstrate effectiveness and precision, yet no quantitative metrics (precision, recall, F1, or cluster purity), baseline comparisons, error analysis, or dataset statistics are supplied. This absence leaves the headline claim without measurable support.
  2. [Cross Event Detection Algorithm] Cross-event detection algorithm: tweet segmentation is performed exclusively against the Wikipedia title database, but no validation, coverage statistics, or error analysis is reported for the informal language typical of disaster tweets (hashtags, abbreviations, transliterations, neologisms). Because downstream similarity clustering depends directly on the quality of these segments, the lack of assessment is load-bearing for the detection claim.
  3. [Cross Event Detection Algorithm] Clustering step: the similarity threshold used for grouping segments is treated as a free parameter with no sensitivity analysis, selection procedure, or robustness checks reported. This directly affects which events are declared as cross events and therefore requires explicit justification.
minor comments (1)
  1. [Abstract] The abstract repeats the phrase 'cross events' multiple times; a single concise definition early in the paragraph would improve readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We agree that the current version of the manuscript would benefit from additional quantitative support and analyses to substantiate the claims. We address each major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: Experimental evaluation section: the abstract states that experiments on real Twitter data demonstrate effectiveness and precision, yet no quantitative metrics (precision, recall, F1, or cluster purity), baseline comparisons, error analysis, or dataset statistics are supplied. This absence leaves the headline claim without measurable support.

    Authors: We acknowledge the absence of explicit quantitative metrics, baselines, error analysis, and dataset statistics in the current manuscript. In the revised version we will expand the experimental evaluation section to include precision, recall, F1, and cluster purity scores for both the cross-event detection and topic-evolution components, together with comparisons against standard event-detection baselines, a description of the Twitter dataset (size, time span, collection method), and a qualitative error analysis of mis-detected or missed cross events. revision: yes

  2. Referee: Cross-event detection algorithm: tweet segmentation is performed exclusively against the Wikipedia title database, but no validation, coverage statistics, or error analysis is reported for the informal language typical of disaster tweets (hashtags, abbreviations, transliterations, neologisms). Because downstream similarity clustering depends directly on the quality of these segments, the lack of assessment is load-bearing for the detection claim.

    Authors: We agree that the segmentation step requires explicit validation for the noisy language found in disaster tweets. In the revision we will add coverage statistics (percentage of tweets that yield at least one Wikipedia title match) and an error analysis performed on a manually annotated sample of tweets from the man-made disaster events. The analysis will quantify how the method handles hashtags, abbreviations, transliterations, and neologisms, and will discuss any residual segmentation failures that propagate to the clustering stage. revision: yes

  3. Referee: Clustering step: the similarity threshold used for grouping segments is treated as a free parameter with no sensitivity analysis, selection procedure, or robustness checks reported. This directly affects which events are declared as cross events and therefore requires explicit justification.

    Authors: We recognize that the similarity threshold is a critical hyper-parameter whose choice directly influences the detected cross events. In the revised manuscript we will include a sensitivity analysis that varies the threshold over a reasonable range, reports the resulting number and quality of cross events, and provides the selection procedure (e.g., value that maximized a preliminary F1 on a small held-out set). Robustness checks across different disaster events will also be reported. revision: yes

Circularity Check

0 steps flagged

No circularity in CEED framework derivation

full rationale

The paper describes a new CEED framework that segments tweets against an external Wikipedia title database and applies similarity-based clustering to detect temporally overlapping cross events, followed by a separate topic evolution step. No equations, fitted parameters, or self-citations are invoked as load-bearing premises; the central claims rest on experimental results from real Twitter data rather than any reduction of outputs to inputs by construction. The methodology is presented as a self-contained construction relying on standard external resources and clustering techniques.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the utility of Wikipedia titles for accurate tweet segmentation and the validity of similarity clustering for identifying temporally overlapping events; one free parameter is implied in the similarity measure used for clustering.

free parameters (1)
  • similarity threshold for clustering
    Critical value in the clustering step that determines which segments form events; exact value and selection method not specified in the abstract.
axioms (1)
  • domain assumption Wikipedia title database provides reliable segmentation boundaries for event-related tweet content
    Directly invoked in the event detection step described in the abstract.

pith-pipeline@v0.9.0 · 5598 in / 1259 out tokens · 27361 ms · 2026-05-13T18:45:36.875436+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

27 extracted references · 27 canonical work pages

  1. [1]

    Online social network infor- mation forensics,

    X. H. Amber Umair, Priyadarsi Nanda, “Online social network infor- mation forensics,”IEEE Trustcom/BigDataSE/ICESS, 2017

    work page 2017

  2. [2]

    Learning from cross-domain media streams for event-of-interest discovery,

    S. S. Wen-Y u Lee, Winston W. Hsu, “Learning from cross-domain media streams for event-of-interest discovery,”IEEE Transactions on Multimedia, VOL. 20, January, 2018

    work page 2018

  3. [3]

    Indexing evolving events from tweet streams,

    D. S. Hongyun Cai, Zi Huang and Q. Zhang, “Indexing evolving events from tweet streams,”IEEE Transactions on Knowledge and Data engineering, vol. 27, no. 11, November, 2015

    work page 2015

  4. [4]

    Exploring cross-event relations on twitter datasets via topic recommendation and word em- bedding,

    B.-C. X. Chung-Hong Lee, Hsin-Chang Y ang, “Exploring cross-event relations on twitter datasets via topic recommendation and word em- bedding,”IEEE 8th International Conference on Awareness Science and Technology, 2017

    work page 2017

  5. [5]

    Event detection on large social media using temporal analysis,

    J. L. Abdulrahman Aldhaheri, “Event detection on large social media using temporal analysis,”IEEE 7th Annual Computing and Communi- cation Workshop and Conference (CCWC), 2017

    work page 2017

  6. [6]

    Real-time multimedia social event detection in microblog,

    G. D. T.-S. C. Sicheng Zhao, Y ue Gao, “Real-time multimedia social event detection in microblog,”Published in: IEEE Transactions on Cybernetics ( V olume: 48 , Issue: 11), Nov. 2018

    work page 2018

  7. [7]

    Social event detection on twitter,

    I. C. F. A. G.-J. H. Elena Ilina, Claudia Hauff, “Social event detection on twitter,”ICWE International Conference on Web Engineering, 2012

    work page 2012

  8. [8]

    Twevent: Segment-based event detection from tweets,

    C. Li, A. Sun, and A. Datta, “Twevent: Segment-based event detection from tweets,”ACM, 2012

    work page 2012

  9. [9]

    Event detection in twitter microblogging,

    P . K. Nikolaos D. Doulamis, Anastasios D. Doulamis and E. M. V ar- varigos, “Event detection in twitter microblogging,”IEEE Transactions on Cybernetics, vol. 46, December, 2016

    work page 2016

  10. [10]

    Identifying on-site users for social events: Mobility, content, and social relationship,

    Q. L. B. G. Zhiwen Y u, Fei Yi, “Identifying on-site users for social events: Mobility, content, and social relationship,”IEEE Transactions on Mobile Computing, 2018

    work page 2018

  11. [11]

    Online bursty event detection from microblog,

    R. Z. W. Y . L. L. Jianxin Li, Zhenying Tai, “Online bursty event detection from microblog,”IEEE/ACM 7th International Conference on Utility and Cloud Computing, 2014

    work page 2014

  12. [12]

    Twitter,

    “Twitter,” http://www.twitter.com

    work page

  13. [13]

    Damage assessment from social media imagery data during disasters,

    M. I. P . M. Dat T. Nguyen, Ferda Ofli, “Damage assessment from social media imagery data during disasters,”IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2017

    work page 2017

  14. [14]

    Social media in emergency management advances, challenges and future directions,

    S. Luna and M. Pennock, “Social media in emergency management advances, challenges and future directions,” Annual IEEE Systems Conference (SysCon) Proceedings, 2015

    work page 2015

  15. [15]

    On-demand real-time information dissemination: A general approach with fairness, productivity and urgency,

    C.-L. Hu, “On-demand real-time information dissemination: A general approach with fairness, productivity and urgency,”International Confer- ence on Advanced Information Networking and Applications, 2007

    work page 2007

  16. [16]

    Event detection and user interest discovering in social media data streams,

    Y . W. L. J. J. H. Lei-Lei Shi, Lu Liu, “Event detection and user interest discovering in social media data streams,”IEEE Access, vol. 5, 2017

    work page 2017

  17. [17]

    Identifying influential spreaders in social networks via normalized local structure attributes,

    S. X. XIAOHUI ZHAO, FANG’AI LIU and Q. W ANG, “Identifying influential spreaders in social networks via normalized local structure attributes,”IEEE Access, 2018

    work page 2018

  18. [18]

    Managing social media uncertainty to support the decision making process during emergencies,

    S. W. . S. O. Silvia Planella Conrado, Karen Neville, “Managing social media uncertainty to support the decision making process during emergencies,”Journal of Decision Systems, 2016

    work page 2016

  19. [19]

    Cross-platform social event detection,

    M. Z. Maia Zaharieva, Manfred Del Fabro, “Cross-platform social event detection,”IEEE Computer Society, 2015

    work page 2015

  20. [20]

    SEDTWik: Segmentation-based event detection from tweets using Wikipedia,

    K. Morabia, N. L. Bhanu Murthy, A. Malapati, and S. Samant, “SEDTWik: Segmentation-based event detection from tweets using Wikipedia,” pp. 77–85, Jun. 2019

    work page 2019

  21. [21]

    Probabilistic latent semantic indexing,

    T. Hofmann, “Probabilistic latent semantic indexing,”Proc. 22nd Annual Int. ACM SIGIR Conf. Res. Develop. Inf. Retr ., vol. 8., pp. 50-57, Aug. 1999

    work page 1999

  22. [22]

    Latent dirichlet allocation,

    D. M. Blei, A. Y . Ng, and M. I. Jordan, “Latent dirichlet allocation,” J. Mach. Learn. Res., vol. 3, pp. 993–1022, Mar. 2003

    work page 2003

  23. [23]

    Improving lda topic models for microblogs via tweet pooling and automatic labeling,

    R. Mehrotra, S. Sanner, W. Buntine, and L. Xie, “Improving lda topic models for microblogs via tweet pooling and automatic labeling,”Pro- ceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, 2013

    work page 2013

  24. [24]

    Topics in tweets: A user study of topic coherence metrics for twitter data,

    I. O. P . H. Anjie Fang, Craig Macdonald, “Topics in tweets: A user study of topic coherence metrics for twitter data,”Advances in Information Retrieval. ECIR 2016. Lecture Notes in Computer Science, vol 9626. Springer , Cham, 2016

    work page 2016

  25. [25]

    Entity matching in online social networks,

    L. R. Y . E. Olga Peled, Michael Fire, “Entity matching in online social networks,”International Conference on Social Computing, 2013

    work page 2013

  26. [26]

    Enhanced vector space models for content-based recom- mender systems,

    C. Musto, “Enhanced vector space models for content-based recom- mender systems,”Proceedings of the F ourth ACM Conference on Recommender Systems, 2010

    work page 2010

  27. [27]

    Clustering using a similarity measure based on shared near neighbors,

    R. A. Jarvis and E. A. Patrick, “Clustering using a similarity measure based on shared near neighbors,”IEEE Transactions on Computers, vol. C-22, no. 11, pp. 1025–1034, 1973

    work page 1973